# data[].attributes.body
건강하시고 행복하게 지내세요 선생님!!!!

# data[].attributes.body
alliswellsy@naver.com

로 조장이 PPT보내기

제목: 2-3 조 이름

# data[].attributes.attachment_link.title
영어권문화_수행평가안내

# data[].attributes.attachment_link.title
Blue White Cute Illustrative Blank Background Poster

# data[].attributes.attachment_link.title
Blue and Orange Modern Simple Minimalist Marketing Annual Report

# data[].attributes.attachment_link.title
Summarize important details for your team

# data[].attributes.attachment_link.title
I didn't do two things during my Jeju Island trip, but I had fun. 추가

# data[].attributes.attachment_link.title
파란색 하얀색 사진 테두리 배경 여행 가이드 브이로그 유튜브 썸네일

# data[].attributes.attachment_link.title
Face synthesis (with portrait rights granted)

# data[].attributes.body
합성 데이터를 사용하여 전립선암 진단의 편향을 완화하고 AI 기반 Gleason 등급을 개선합니다.

<기사 링크>

https://www.nature.com/articles/s41698-025-00934-5

<모르는 단어>

EfficientNet

뜻: 효율적인 신경망 (Efficient + Network)

예문 : EfficientNet achieved state-of-the-art performance on ImageNet with significantly fewer parameters.

-> EfficientNet은 훨씬 적은 파라미터로도 ImageNet에서 최첨단 성능을 달성했다.

(SHRQA) Spatial Heterogeneous Recurrence Quantification Analysis

뜻 : 공간적으로 이질적인 반복 정량 분석

예문 : SHRQA was used to identify heterogeneous recurrence structures in tumor tissue samples.
-> SHRQA는 종양 조직 샘플에서 이질적인 반복 구조를 식별하는 데 사용되었다.

histopathological images

뜻 : 조직 병리화 이미지

예문 : Histopathological images provide critical insights for cancer diagnosis.
-> 조직 병리학 이미지는 암 진단을 위한 중요한 정보를 제공한다.

generalizability

뜻 : 일반화 가능성

예문 : Improving generalizability is crucial for deploying models in real-world clinical settings.
-> 실제 임상 환경에서 모델을 적용하기 위해서는 일반화 가능성 향상이 필수적이다.

expert pathologist

뜻 : 전문 병리하자

예문 : The model's performance was comparable to that of expert pathologists.
-> 모델의 성능은 전문 병리학자 수준에 근접했다.

<기사 내용>

전립선암은 남성 암 관련 사망의 주요 원이이며, 그에 따른 글리슨 등급를 보고 예후 또는 치료를 결정하는게 중요합니다.

머시 러닝 모델로 등급 분류가 가능하지만 , 데이터셋 편향 및 부족으로 인해 일반화가 어렵습니다.

본 연구에서는 생성적 적대 신경망을 사용하여 이러한 문제점을 해결하기 위해 조직병리학 합성 이미지를 생성하였으며, 전문가의 검토와 공간적 이질성 정령화 분석 기법을 사용하여 개발 및 검증을 수행하였습니다. 합성곱 신경망(EfficientNet)을 원본 이미지와 합성 이미지를 함께 사용해 학습시킨 결과, TCGA, PANDA Challenge, MAST 임상시험 데이터셋에서 검증한 결과 80%의 진단 품질 승인을 받았습니다. 특히 합성 이미지를 포함시킨 경우, Gleason 등급 3, 4, 5의 분류 정확도가 각각 26%, 15%, 32% 향상되었고,민감도와 특이도는 각각 81%와 92%에 달했습니다.

이 연구는 합성 데이터가 머신러닝 기반 전립선암 등급 분류 성능과 재현성을 크게 높여 정밀 의료에 적용 가능한 AI 솔루션임을 보여줍니다.

<추가 단어>

글리슨 등급 : 암의 공격성 평가 점수

예후 : 질병 결과 예측

# data[].attributes.attachment_link.readable_text
Abstract

Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, with Gleason grading critical for prognosis and treatment decisions. Machine learning (ML) models offer potential for automated grading but are limited by dataset biases, staining variability, and data scarcity, reducing their generalizability. This study employs generative adversarial networks (GANs) to generate high-quality synthetic histopathological images to address these challenges. A conditional GAN (dcGAN) was developed and validated using expert pathologist review and Spatial Heterogeneous Recurrence Quantification Analysis (SHRQA), achieving 80% diagnostic quality approval. A convolutional neural network (EfficientNet) was trained on original and synthetic images and validated across TCGA, PANDA Challenge, and MAST trial datasets. Integrating synthetic images improved classification accuracy for Gleason 3 (26%, p = 0.0010), Gleason 4 (15%, p = 0.0274), and Gleason 5 (32%, p  < 0.0001), with sensitivity and specificity reaching 81% and 92%, respectively. This study demonstrates that synthetic data significantly enhances ML-based Gleason grading accuracy and improves reproducibility, providing a scalable AI-driven solution for precision oncology.

Introduction

Prostate cancer (PCa) is the third most commonly diagnosed cancer globally and a leading cause of cancer-related deaths among males. The diagnostic pathway typically starts with measuring prostate-specific antigen (PSA) levels8,9,10,11,12,13,14,15,16,17,18,19. If PSA levels are elevated, clinicians may proceed with additional assays, such as 4Kscore, PCA3, or phi34,35,36,37,38,39, followed by imaging studies like MRI to identify potential PCa lesions40,41,42,43,44,45. In cases where abnormalities are detected, biopsies are often performed for histopathological and genomic evaluation. This diagnostic approach is critical for determining PCa prognosis, especially in men under active surveillance, which has significantly increased in the past decade46,47,48,49. However, overdiagnosis remains a significant concern, contributing to adverse physical and psychological health outcomes50. Therefore, improving diagnostic accuracy is essential to enhance patient outcomes.

Machine learning (ML) has gained traction in recent years for automating various stages of PCa diagnosis and prognosis51. ML algorithms, trained on data from tissue samples, medical images, and clinical information, can detect patterns associated with the disease. Particularly effective in the automated analysis of MRI scans and biopsy slides, ML models are being used to identify suspicious prostate areas with greater precision than traditional manual methods58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78. Technologies such as PathAI, PaigeAI, and Deciplex convert digital histology into data that can differentiate between cancerous and non-cancerous areas79,80,81,82,83.

Despite these advances, current technologies face limitations, primarily due to the reliance on clinical trial datasets for training84,85,86,87. Clinical trial data often represent specific patient groups and disease stages, limiting the generalizability of models to the broader population81,88,89,90,91. Additionally, inconsistencies in staining, slide preparation, and imaging resolution introduce variability that can negatively impact ML model performance92,93. Moreover, ethical concerns arise from the use of sensitive patient data, which must be handled in accordance with strict privacy guidelines.

To address these challenges, synthetic data generation has emerged as a complementary strategy to expand training datasets while preserving patient privacy94. Our previous study have demonstrated that GANs and other generative models can create histopathological images that capture critical morphological features for training AI models94. While prior research has successfully used synthetic data augmentation in medical imaging and general histopathology, its application in Gleason grading for prostate cancer remains underexplored95. Furthermore, the integration of synthetic data into AI-driven pathology models has largely focused on image augmentation rather than standalone training and validation strategies95,96, highlighting the need for further investigation.

To address these challenges, we propose reducing reliance on clinical trial data by generating synthetic data. This study presents a novel approach using customized generative adversarial networks (GANs) to create synthetic histopathological images from a small set of original radical prostatectomy and needle biopsy images. The synthetic images were analyzed using spatial heterogeneous recurrence quantification analysis (SHRQA) modules to assess granularity compared to real images. We then trained a convolutional neural network (CNN, EfficientNet) on these synthetic image patches and evaluated its grading performance against a CNN trained on real image patches. For validation, we utilized histology images from various sources, including the TCGA, PANDA Challenge83 and MAST trial.

The CNN model trained on synthetic data significantly improved grading accuracy compared to models trained solely on original data. These findings demonstrate the potential of this method to overcome data scarcity in AI model training, offering a promising solution for PCa diagnostics in clinical settings.

Results

Network architecture and model selection

We evaluated four CNN models using 20 randomly selected histology images from the TCGA Prostate Adenocarcinoma dataset. AlexNet was the first CNN to be defined, and it consists of a series of convolutional pooling layers and an interconnected output layer97, achieved 55% accuracy, matching pathologists' assessments and TCGA scoring. ResNet, which incorporates residual networks based on skip connections within the network, significantly improving AlexNet98, also achieved 55% accuracy on the same images. Xception model, which is based on the Inception model, improves traditional network architectures such as AlexNet and separates space and depth into different dimensions in its convolutional layers99, improved accuracy to 60%. EfficientNet, which utilizes a novel scaling method that employs a compound coefficient to scale up a CNN model in a more structured manner100,101, outperformed all models with 65% accuracy. Based on these results, EfficientNet was selected for this study (Supplementary Fig. 1, Supplementary Table 1).

Image preprocessing

To address variability in staining protocols, tissue quality, and section thickness102, pre-processing normalization was applied to the datasets. For the TCGA dataset (500 images), 21 outliers with mean RGB intensity beyond two standard deviations were excluded. Similarly, one outlier was removed from 32 radical prostatectomy section images from the University of Miami. For 3949 needle biopsy slides from the PANDA challenge83, area evaluation and color normalization excluded 257 images. These steps minimized variability and ensured a more consistent dataset for AI model predictions (Table 1).

Table 1 Similarity index values returned from the 10 fold cross validation run for RP images (left) and needle biopsy images (right)

Quality-controlled annotation and patch generation

RP sections from TCGA and UM were divided into pseudo-cohorts based on Gleason patterns and analyzed with HistoQC103 to exclude low-quality samples. Of 143 randomly reviewed sections, only 33 with agreement between two pathologists and TCGA scoring were selected for gold standard training data. Using PyHIST, 96 × 96 and 256 × 256 pixel patches were generated, each containing at least 75% tissue and no more than 25% whitespace. The minimum patch size was set at 96 pixels, matching the smallest overlapping annotation. From 33 images, 219 patches were created and augmented to 2082 patches (Fig. 1A).

GAN model selection

GANs are a class of deep learning models used to generate synthetic images by learning patterns from real data through an adversarial training process. A GAN consists of two neural networks: a generator, which produces synthetic images, and a discriminator, which differentiates between real and generated images. The networks are trained iteratively, with the generator improving its ability to create realistic images while the discriminator refines its capacity to distinguish them. Various GAN architectures have been developed to optimize image synthesis for different applications. In this study, three GAN variants-conditional GAN (cGAN), StyleGAN, and deep convolutional GAN (dcGAN)104,105,106 were evaluated. cGAN incorporates labeled input data to guide the generation process, allowing for controlled image synthesis based on predefined categories. StyleGAN introduces a style-based generator that enhances image quality and provides greater control over fine-grained image features. dcGAN, a simpler and computationally efficient architecture, utilizes convolutional layers to improve image synthesis while maintaining relatively low training complexity.

To select the optimal GAN architecture, 2082 RP image patches were divided by Gleason patterns (GS3, GS4, GS5) and tested using each GAN model. Each GAN generated 1,000 synthetic images, which were classified using a CNN. StyleGAN and dcGAN showed similar accuracies (0.65 and 0.64, respectively), but dcGAN was significantly faster (901 min vs. 2372 min for 1000 images). Based on these results, we selected dcGAN to generate synthetic images for training. The Adam optimization algorithm determined the best iteration value at 14,000 iterations. We generated synthetic images in 128 × 128 and 256 × 256 pixel sizes and performed a manual quality control (QC) assessment on a randomized set of images by board-certified pathologists, who approved 80% of the images.

For needle biopsy analysis, 300 annotated biopsies produced 1712 cancer-specific (752 for GS3, 592 for GS4, and 368 for GS5, respectively) and 539 benign tissue patches (256 × 256 pixels). Various patch sizes (from 512 × 512 to 32 × 32) were tested, with CNN accuracy dropping significantly below 64 × 64 pixels. We selected 64 × 64 patches, upscaled to 256 × 256, for optimal performance. GAN training on a single NVIDIA T1000 GPU generated 1000 synthetic images in 2.5 h, with loss functions balancing at approximately 50,000 epochs (Supplementary Fig. 2). A repository of 2000 patches was created for tumor and normal samples (Fig. 2). Pathologists manually assessed these images, with an 80% approval rate for adequacy in patch size.

Benchmarking

To determine the optimal number of synthetic images for EfficientNet training, we fine-tuned the model with synthetic data generated by dcGAN and assessed overfitting using the similarity index and Frechet Inception Distance (FID)107. We generated synthetic images based on Gleason patterns in batches of 10 K, 50 K, and 100 K. FID scores for RP images were 25.1 (10 K), 18.8 (50 K), and 36.2 (100 K). The 50 K batch produced the best results, with accuracy increasing from 33% to 70% for GS3, 35% to 65% for GS4, and 41% to 71% for GS5, stabilizing beyond 50 K images. Thus, we integrated a maximum of 50 K synthetic images into the model.

To further avoid overfitting, we applied 10-fold cross-validation, randomly organizing all real and synthetic image tiles. Accuracy remained consistent, ranging from 0.68 to 0.71 for GS3, 0.63 to 0.70 for GS4, and 0.66 to 0.73 for GS5, with similarity index values between 0.8 to 1.0 (Fig. 2) (Table 1). This process confirmed the quality and variability of synthetic images.

For needle biopsies, FID scores were 21.2 (10 K), 20.2 (50 K), and 33.7 (100 K). Cross-validation of 1000 samples yielded stable accuracy across Gleason scores (GS3: 0.67-0.71, GS4: 0.63-0.70, GS5: 0.65-0.73) with similarity index values ranging from 0.92 to 1.0 (Table 1), confirming the quality of synthetic needle biopsy images (Supplementary Fig. 2).

Quality evaluation of synthetic images

Next, we sought to address the extent of technical variations that exist between the synthetically generated images and the original images. This examination is crucial because each Gleason pattern possesses specific morphological characteristics108,109 that synthetically generated images should ideally replicate. For this, we utilized SHRQA, a robust technique capable of measuring complex microstructures based on spatial patterns110,111,112. The SHRQA process, as shown and detailed in Supplementary Fig. 3, involves six key steps111,112,113,114,115,116,117,118. It begins with the application of the 2D-Discrete Wavelet Transform (2D-DWT) using the Haar wavelet to reveal patterns that are not apparent in the original image111,112,113,114,115,116. Subsequently, each image is converted into an attribute vector using the Space-Filling Curve (SFC), which plays a crucial role in maintaining the spatial proximity between pixels within the vector. This step is essential for representing the image's geometric recurrence in vector form. The attribute vector is then projected into state space, forming a trajectory that emphasizes the geometric structure of the image. To further analyze spatial transition patterns, Quadtree segmentation is employed to divide the state space into distinct subregions117,118. Following this, an Iterated Function System projection is applied, transforming each attribute vector into a fractal plot that captures recurrence within the fractal topology. Finally, the fractal structures are quantified to provide insights into the geometric properties of the image. In one of our earlier studies, we used the SHRQA method to quantify synthetically generated image patches from eight different genitourinary organs, including the testis, kidney, prostate, bladder, vagina, cervix, ovary, and uterus94. Following a similar approach, the SHRQA method was applied to examine the spatial recurrence properties of real and synthetic image patches across four Gleason patterns (GS0/3/4/5) in the RP section and needle biopsies.

For RP, our sample set included an equal number of patches from real and synthetic sources, with a balanced representation of each Gleason pattern. We analyzed 4000 image patches, each 256 × 256 pixels, evenly split between real and synthetic. A one-layer 2D-DWT with Haar wavelet decomposed each image into four sub-images, revealing fine details. SHRQA quantitatively outlined each patch's microstructures. From an initial extraction of 1997 spatial recurrence features per patch, LASSO119 selected 1819 features as significant to the Gleason pattern. Hotelling's T-squared test, compared the spatial recurrence attributes. The resulting p-values of 0.8991 signified no significant differences in spatial recurrence properties between real and synthetic patches, as confirmed by the T-squared tests' p-values for each Gleason pattern (refer to Table 2, left). We also employed PCA on the spatial recurrence properties, visualized using radar charts, revealing that the six principal components capture 82% of the variability. This allowed us to map the distributions of spatial properties for real and synthetic images across Gleason patterns, as depicted in Fig. 3. Notably, while distributions for the same Gleason pattern aligned closely between real and synthetic images, significant differences were evident across different patterns.

Table 2 Hotelling's T-squared Two-sample Test: comparisons of spatial recurrence properties between real and synthetic images under different Gleason patterns for radical prostatectomy (RP: left) and needle biopsies (NB: right)

A similar analysis was conducted for the needle Biopsies section (Fig. 4), examining 1600 patches with an equal split between real and synthetic images distributed evenly across GS0, GS3, 4, and 5. SHRQA extracted 2585 initial features, with LASSO identifying 1578 as significant. Hotelling's T-squared tests in the NB section corroborated the RP section's results (refer to Table 2, right), demonstrating that synthetic images reliably replicate the geometric nuances of real images for each Gleason pattern. These findings across both RP and NB sections validate the model's efficiency in capturing the geometric intricacies consistent with real images.

Furthermore, we subjected a randomized number of synthetic image patches allocated into Gleason 3, 4, and 5 via SHRQA quantification models to cross-validation for characterization by pathologists. Specific features associated with each phenotype, such as uniform glandular arrangement, moderate differentiation, fibrous stroma, mild nuclear atypia, clear lamina, and minimal desmoplasia associated with Gleason 3; Moderate to severe nuclear atypia, poor differentiation, irregular and cribriform patterns, inflammatory infiltrate associated with Gleason 4; and Sheet of cells, scant stromal tissue, undifferentiated tumor cells, sever nuclear atypia, complete loss of glandular structure associated with Gleason 5 were accurately identified by the SHRQA models. These findings across both RP and NB sections validate the model's efficiency in capturing the geometric intricacies consistent with real images (Fig. 5A-C)

Shows the distributions of granular features associated with ( A) Gleason pattern 3, ( B) Gleason pattern 4, and ( C) Gleason pattern 5 as identified by the SHRQA quantification and verified by the pathologists.

Validation of CNN performance post-training with enhanced synthetic data

Next, to determine whether synthetic images could substitute original images for training a CNN model, we trained the CNN model with two sources of image patches. The first source was the patches derived from original RP sections from TCGA and inhouse images, which were classified according to the Gleason pattern (normal ( n = 175), GS3 ( n = 726), GS4 ( n = 1029), and GS5 ( n = 152)). The second source of patches was derived from a combination of original and synthetic images, with 5000 image patches used for each Gleason pattern. The grading capabilities of this CNN model were then compared by allowing it to assign Gleason scoring to the RP images in the TCGA ( n = 475). The results demonstrated that the CNN's accuracy significantly improved in GS3 from 0.53 to 0.67 ( p = 0.0010), in GS4 from 0.55 to 0.63 ( p = 0.0274), and in GS5 from 0.57 to 0.75 ( p < 0.0001) when trained with the combination of original and synthetic image patches compared to just being trained with original image patches. Moreover, the comparative analysis revealed a notable enhancement in accuracy and the receiver operating characteristic curve (ROC) for the combined (original and synthetic) dataset relative to the original ( p = 0.0381). Additionally, the in-house RP images ( n  = 24) were subjected to grading using CNN models described above. Results demonstrated a significant improvement of the CNN model to accurately assign the grade.

Furthermore, we extended the validation of the CNN model using needle biopsies. Similar to the RP sections, we trained the CNN model with two sources of image patches. The first source was the patches derived from original needle biopsy sections, classified according to the Gleason pattern (normal ( n = 539), GS3 ( n = 610), GS4 ( n = 890), and GS5 ( n = 212)). The second source was derived from a combination of original and synthetic images, classified according to the Gleason pattern, with a total of 2000 image patches used for each Gleason pattern. The grading capabilities of this CNN model were then compared by allowing it to assign Gleason grading to the needle biopsy images ( n = 3649). The comparative analysis revealed a significant enhancement in accuracy and the ROC for the amalgamated dataset relative to the original (Fig. 6). Specifically, the enhancement was consistent across both benign and malignant samples, with an overall accuracy increase from 91% when using the original training images to an accuracy of 95% when using original and synthetic images combined ( p  = 0.0402). The original and synthetic combined training database yielded a sensitivity of 0.81, and a specificity of 0.92.

Showing cumulative improvement in accuracy through ROC curves between synthetic+original against the original dataset of RP (left) and needle biopsies (right). p  < 0.05 in both cases.

Moreover, to confirm the efficiency of the validated AI model, we subjected it to evaluate the images derived from MAST trial samples. While the MAST trial analysis focused on baseline data, it provides a unique real-world validation setting for AI applications in active surveillance workflows. The outcome of the AI model evaluation was compared to the pathologist grading. Results showed an overall accuracy of 87% for Gleason grading, with sensitivity and specificity of 81% and 92%, respectively. Together, cross-validation of the final model yielded consistent performance, with mean accuracy across folds varying by less than 1.5 percentage points (95% CI, ±1.5%). The improved accuracy across datasets highlights the potential of synthetic data for enhancing model robustness and reproducibility (Fig. 4).

Discussion

The integration of AI-based tools into cancer diagnostics, particularly for prostate cancer (PCa), holds significant promise. Over the past decade, numerous image analysis algorithms have been developed to assist with tumor grading, classification, and metastasis detection127,128,129,130,131,132,133,134,135,136,137. Despite their potential, many AI tools face substantial barriers to widespread clinical adoption138. These challenges stem primarily from biases in training data, the inability to generalize across diverse populations, and technological constraints such as model drift139,140. This study sought to address these limitations by employing GANs to generate synthetic histopathological data, supplementing real-world datasets, and enhancing the accuracy and robustness of convolutional neural network (CNN) models for PCa Gleason grading81,141,142.

AI models trained on datasets with narrow demographic representation often fail to generalize effectively to diverse clinical environments, risking misdiagnosis and suboptimal performance when applied to unseen populations. This study addressed this limitation by integrating data from three diverse datasets: TCGA, PANDA Challenge, and the MAST trial. While these datasets provided robust foundations for model training and validation, they are not fully representative of global patient populations. Future research should prioritize external validation across broader ethnic, geographic, and demographic cohorts. Doing so would ensure that AI models trained for Gleason grading are equitable and clinically reliable in a wide range of settings.

GANs were leveraged to generate synthetic histopathological images, providing a scalable solution to address data scarcity and augment training datasets. Unlike prior approaches where synthetic data was used primarily for general augmentation, this study validated synthetic images against pathologist-annotated real-world images using SHRQA, FID, and Hotelling's T-squared test. These quantitative validation methods confirmed that GAN-generated images preserved key histological structures, including cribriform patterns and glandular arrangements, with no significant differences in spatial recurrence properties. However, while synthetic data significantly improved model performance, real-world validation remains critical, as synthetic images may not fully capture the biological variability inherent in tissue samples. Future studies should explore the optimal balance between synthetic and real-world data to maximize both accuracy and generalizability in AI-driven diagnostic models.

Given the growing prominence of diffusion models for synthetic data generation, their potential role in histopathology warrants discussion. While diffusion models offer superior image fidelity143,144,145, they require significantly greater computational resources and iterative refinement steps, making them less practical for large-scale AI training146,147. In contrast, dcGAN was chosen for its computational efficiency, rapid convergence, and ability to generate clinically relevant images validated through SHRQA and FID metrics. While diffusion models remain a promising alternative, their added complexity did not provide a clear advantage for this study's objectives. However, future work should investigate hybrid models that integrate the strengths of both GANs and diffusion-based approaches for optimizing synthetic histopathological image generation.

A crucial consideration in this study was dataset heterogeneity, particularly in combining RP and needle biopsy datasets. While merging these datasets into a single training framework was considered, it posed technical and biological challenges. Technically, needle biopsy images were processed at 64 × 64 and upscaled to 256 × 256, whereas RP sections were directly processed at 256 × 256. Combining these datasets without accounting for resolution differences could have led to inconsistencies in feature extraction and CNN performance. Biologically, RP specimens provide larger, whole-mount tissue architecture, while needle biopsies capture smaller, fragmented tumor regions, leading to distinct histological patterns relevant for Gleason grading148,149. Training separate CNN models for RP and needle biopsies ensured that tissue-specific morphological patterns were preserved, improving the robustness and clinical applicability of the AI-based grading system.

The impact of patch size and resolution variability on CNN performance was also systematically evaluated as it influences feature extraction, model convergence, and classification accuracy150,151,152. Ablation studies demonstrated that patches smaller than 64 × 64 resulted in decreased accuracy, likely due to the loss of critical histological details, while patches larger than 512 × 512 increased computational cost without proportional accuracy gains. These findings align with prior studies in digital pathology (Litjens et al. 2017; Campanella et al. 2019) and emphasize the importance of optimizing input resolution to balance accuracy and efficiency. Additionally, image standardization techniques, including color normalization and histogram-based intensity adjustments, were employed to minimize artifacts and domain shift across datasets. A key challenge in deep learning models for histopathology is interpretability, which is essential for clinical adoption. While this study prioritized quantitative validation techniques (SHRQA, FID, Hotelling's T-squared test, PCA), which provide structured insights into synthetic data fidelity, we acknowledge the importance of explicit interpretability techniques such as Grad-CAM or SHAP. Future work will explore attention-based visualization methods to further enhance clinician trust in AI-driven Gleason grading.

Performance comparison with existing AI-based Gleason grading models highlights the effectiveness of incorporating synthetic histopathological images into CNN training pipelines. The proposed model achieved 95% accuracy when trained on a combined dataset of real and synthetic images, surpassing the 91% accuracy of models trained on real data alone ( p = 0.0402). Independent validation on the MAST trial dataset demonstrated 87% accuracy, reinforcing the robustness and generalizability of the approach in real-world clinical settings. These findings align with existing AI-based models such as PathAI and PaigeAI, which, despite high diagnostic accuracy, face dataset bias and generalizability issues153. Notably, the inclusion of GAN-generated synthetic data improved classification performance across all Gleason patterns. Unlike prior AI approaches, which rely solely on real-world datasets, this study demonstrates that synthetic data can be strategically integrated to enhance AI-based pathology models.

While this study demonstrated significant technical advancements, transitioning from research to real-world clinical implementation requires addressing practical challenges. Integration into clinical workflows demands robust digital infrastructure, clinician training, and seamless interoperability with existing pathology systems. To facilitate adoption, future efforts should focus on developing intuitive user interfaces and decision-support systems that enhance, rather than disrupt, clinician workflows. Moreover, regulatory approvals and adherence to ethical guidelines will be pivotal in fostering trust and acceptance among healthcare providers and patients.

Although this study focused on PCa diagnosis, the methodologies demonstrated here-particularly the use of GAN-generated synthetic data-can be extended to other cancer types. The ability to generate high-fidelity synthetic data holds immense potential for augmenting AI models across a spectrum of oncological applications. Future research should refine these models for PCa-specific contexts, such as active surveillance and advanced disease stages, while also exploring their adaptability to other malignancies.

Together this study addresses key limitations in AI-driven cancer diagnostics by introducing synthetic data to overcome dataset scarcity, integrating diverse datasets to improve generalizability, and demonstrating the feasibility of using CNN models for Gleason grading. While challenges such as model drift, workflow integration, and demographic diversity remain, these findings lay a strong foundation for future longitudinal studies and clinical applications. As AI continues to evolve, its potential to transform cancer diagnostics and personalized medicine becomes increasingly evident, particularly when developed in collaboration with clinicians and validated in real-world settings.

Methods

Scanning and image annotation

Images are deconstructed by first taking individual areas in patchwise fashion using the software package PYHist. Briefly, the image is taken from raw.svs format and scanned for areas that are defined as tissue regions. This is done by color definition compared to whitespace background. Each predefined block is taken at a 512 × 512 pixel area which has been identified as the lowest amount of space that our pathologist graded any given image. These patches were then used as the training database from which other images are annotated. The image annotation for a given test image then is a whole image from which a sliding window approach is taken that moves through each of the given pixel windows and scored against the training database.

Cohorts used

Samples were taken from TCGA. Histology images from 500 individuals were considered. 32 local samples were taken from the University of Miami pathology core. The Institutional Review Board (IRB) protocol was approved by the University of Miami Miller School of Medicine, Miami, FL, to ensure that the research adhered to ethical guidelines and principles. Furthermore, the study included 3949 needle biopsies sourced from Radboud University Medical Center and Karolinska Institute (the PANDA challenge)83,154. The MAST Trial dataset comprised 141 patients stratified into NCCN risk groups: Very Low (44.2%), Low (38.1%), and Intermediate (17.3%). Mean patient age in the MAST dataset was 62.8 years (SD, 8.5 years; range, 43-85 years) (Table 3). Across datasets, pathologists annotated Gleason patterns, ensuring robust and consistent training data (Fig. 1).

Table 3 Summary of Key Variables in MAST Trial Data

Algorithm design training and testing procedure

We selected 3 initial algorithms from which to test out the convolutional network. These were defined as the major stepwise breakthroughs in AI with the AlexNET, ResNet, and Xception models. Initially, training images were taken from a single pathologist defined areas and 10 random images were used as test images. We used granular level annotation (detail in result section) to select the model with the highest accuracy. Post model selection, the hyperparameters were turned. Here a Tree-structured Parzen Estimator was implemented to complete a sequential model optimization. In addition, tree weights were investigated by a population based training approach. From this, the highest performing hyperparameters were used along with the appropriate tree weights that were used to define the network.

Calculation of performance

We defined accuracy in 2 different ways. First, in granular accuracy we started with the areas that were selected and annotated with the exact same Gleason pattern by all the 3 pathologists. Gold standard granular accuracy is when the convolutional network overlaps with the agreement between all pathologists. This is defined by the number of overlapping pixels in the defined area compared with the definition between the annotated areas of the pathologist pattern. Second, patient level accuracy is defined when the artificial intelligence pipeline has correctly identified a patient's Gleason Score as defined by the TCGA pathology confirmed score.

Development and evaluation of a conditional Generative Adversarial Network

A preliminary conditional Generative Adversarial Network (cGAN) was designed and implemented to assess the performance accuracy of various GAN architectures. The cGAN was developed utilizing Python 3.7.3 and the Tensorflow Keras 2.7.0 package. The generator component of the cGAN comprises three input layers and a single output layer. In parallel, the discriminator component is configured with analogous input, hidden, and output layers. The cGAN's total parameter count was 19.2 million for each of the evaluated Gleason patterns (Supplementary Fig. 1).

Implementation and adaptation of StyleGAN for tissue image analysis

StyleGAN, a progressive generative adversarial network architecture, serves as a baseline for comparison to the cGAN, featuring a distinct generator configuration. The architecture was adopted from the original StyleGAN publication with minimal alterations to the generator and discriminator networks. A notable modification involved substituting human face images in the StyleGAN with tissue images to create a tissue image GAN. The generator's total parameter count amounted to 28.5 million, in contrast to 26.2 million in the original StyleGAN publication and 23.1 million in a conventional generator. Particular emphasis was placed on refining the GS4 and GS5 images to ensure adequate representation of tumor heterogeneity.

dcGAN

The dcGAN weights were initialized randomly from a normal distribution with mean = 0 and a standard deviation of 0.02. The generator neural network was constructed using 13 layers consisting of transpose function with batch normalization and ReLU functions. The walkthrough of the generator layers is as follows :

ConvTranspose2d -> BatchNorm2d -> ReLU -> ConvTranspose2d -> BatchNorm2d -> ReLU -> ConvTranspose2d -> BatchNorm2d -> ReLU -> ConvTranspose2d -> BatchNorm2d -> ReLU -> ConvTrnaspose2d -> Tanh.

The discriminator neural network was constructed using 12 layers of Conv2d, LeakyReLU functions with batch normalization. The walkthrough of the layers is as follows :

Conv2d -> LeakyReLU -> Conv2d -> BatchNorm2d -> LeakyReLU -> Con v2d -> BatchNorm2d -> LeakyReLU -> Conv2d -> BatchNorm2d -> LeakyReLU -> Conv2d -> Sigmoid.

Estimation of the number of parameters used for a single run ranged from 1.3 T to 1.4 T calculations per run to generate 1,000 synthetic images that were run on standard GPU chips (GET INFO). Time taken for a single run was dependent on the number of GPU processors available. For multi-threaded GPU the time taken ranged from 1.5 h to 5 h for a run while for a single GPU it ranged from 3 h to 12 h. Time was dependent on the level of resolution desired.

Finally, the BCE loss function was used and the Adam optimizer was implemented for both the generator and discriminator. Iteration level statistics were generated at the end of each run and saved for further analysis in matplot.

EfficientNET

The EfficientNET baseline model was setup with the B3 function within the TensorFlow python backend. Initial testing for the B6 function was found to be too restrictive and B1 was too simple to form complex patterns so B3 was selected as our model function. To form the model based on our images, a GlobalMaxPooling2D layer was added after the initial base as well as a Dropout layer which will help avoid overfitting. The dropout rate was set to 0.2 after initial estimations were too high. The number of classes for prediction layer was set to 4 which represented the normal tissue group as compared to the primary Gleason patterns GS3, GS4, and GS5. The EfficientNET model was setup using pre-trained weights from the "imageNet" to take advantage of transfer learning to reduce analysis time.

Image augmentation was completed using Keras ImageDataGenerator function. Image rotation range was set to 45, width_shift_range and height_shift_range was set to 0.2, and the horizontal flip was set to true for flipping the image. Fill mode was defaulting to "nearest". Validation data was not augmented, but the images were put through a rescale function to ensure that every test image was uniform before annotation.

Statistical calculations

FID was implemented in custom scripts developed in house. The FID model was pre-trained using Inception V3 weights for transfer learning. In house code was centered around the FID model and inserted into the dcGAN to be run during each iteration. Stats were reported at intervals of 1000 and graphed with in house python scripts.

PCA analysis was performed by first transforming the images into numerical arrays. Images were separated into normal and synthetic batches and then distributed by primary Gleason score. Intensity was calculated (using the R package imgpalr and magick) as the average of the color of the entire image while keeping the matrix framework (i.e., positional arguments were retained). PCA was conducted using the general prcomp function in R and plotted results were displayed in ggplot2.

Acknowledgements

This research was funded by the Scott R. MacKenzie Foundation (Grant No. AWD-009374). Additional financial support was provided by the University of Miami U-LINK (Grant No. PG013269) and the Provost Research Award (Grant Nos. PG012860 and PG015498). S.P. received additional funding from the National Institutes of Health/National Cancer Institute (NIH/NCI) (Grant Nos. U01CA239141, 1R01CA272766) and the Paps Corps Champions for Cancer Research. Primary financial support was provided by the Sylvester Cancer Center, the University of Miami, and the Desai and Sethi Institute of Urology. We extend our deepest gratitude to all patients whose willingness to participate made this study possible. We are also grateful to all principal investigators, sub-investigators, and local center staff for their dedication and commitment to patient recruitment. We acknowledge the members of the steering committee and the data monitoring and ethics committee for their valuable contributions. Special thanks to Dr. Dipen Parekh for providing critical insights from a patient perspective. The support of The Cancer Genome Atlas (TCGA) was instrumental to the successful execution of this study, and we appreciate the contributions of all its past and present staff. Finally, we extend our sincere appreciation to the laboratory team for their significant contributions to this research.

Author information

Authors and Affiliations

1. John P Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA

Derek J. Van Booven & Himanshu Arora

2. Department of Industrial and Systems Engineering, University of Miami, Miami, FL, USA

Cheng-Bang Chen & Yujie Wang

3. Department of Pathology, Miller School of Medicine, University of Miami, Miami, FL, USA

Oleksandr N. Kryvenko, Sheetal Malpani & Rehana Qureshi

4. Desai & Sethi Institute of Urology, Miller School of Medicine, University of Miami, Miami, FL, USA

Oleksandr N. Kryvenko, Sanoj Punnen & Himanshu Arora

5. Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA

Sanoj Punnen & Himanshu Arora

6. Hospital Valentin Gomez Farias, Universidad de Guadalajara, Guadalajara, Mexico

Victor Sandoval

7. Dow University of Health Sciences, Karachi, Sindh, Pakistan

Ahmed Noman

8. Department of Pathology and Laboratory Medicine, The University of Kansas Medical Center, Kansas city, KS, USA

Farhan Ismael

9. Department of Medicine, Miller School of Medicine, University of Miami, Miami, FL, USA

Joshua M. Hare

10. Department of Medicine, Cardiology Division, Miller School of Medicine, University of Miami, Miami, FL, USA

Joshua M. Hare

11. The Interdisciplinary Stem Cell Institute, Miller School of Medicine, University of Miami, Miami, FL, USA

Joshua M. Hare & Himanshu Arora

Contributions

H.A. was the chief investigator. D.V.B. and H.A. designed the study and developed the protocol. O.K., F.I., S.M., A.N., V.S., and A.N. carried out the pathological analysis plan. H.A. and D.V.B. coordinated the central modeling investigations. C.B.C. developed Spatial quantification methods. C.B.C. and D.V.B. validated the quantifications. H.A., O.K., and S.P. coordinated the data collection. D.V.B. and H.A. interpreted the data. H.A. and D.V.B. developed the first drafts of the manuscript. C.B.C., O.K., R.Q., S.P., S.M., V.S., A.N., F.I., A.B. and J.M.H. have accessed and verified all the data in the study. All authors had access to all the data reported in the study. All authors contributed to the review and amendments of the manuscript for important intellectual content and approved this final version for submission. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Himanshu Arora.

Ethics declarations

Competing interests

Disclosure of Patent Information: The authors wish to inform that the technology presented in this study is part of a provisional patent application that has been filed with the United States Patent and Trademark Office (USPTO). The application has been assigned Serial No. 63/598,207 and was filed on November 13, 2023. The patent application is currently pending. Some of the authors of this paper are listed as inventors in the patent application. This patent filing may constitute a potential conflict of interest, and this statement serves to disclose this relationship in the interest of full transparency. Joshua M. Hare reports having a patent for cardiac cell-based therapy and holds equity in Vestion Inc., and maintains a professional relationship with Vestion Inc. as a consultant and member of the Board of Directors and Scientific Advisory Board. Vestion Inc. did not play a role in the design, conduct, or funding of the study. Dr. Joshua Hare is the Chief Scientific Officer, a compensated consultant, and a board member for Longeveron Inc. and holds equity in Longeveron. Dr. Hare is also the co-inventor of intellectual property licensed to Longeveron. Longeveron did not play a role in the design, conduct, or funding of the study. The University of Miami is an equity owner in Longeveron Inc., which has licensed intellectual property from the University of Miami.

Ethics statement

This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of the University of Miami Miller School of Medicine, Miami, FL (IRB Protocol Number: 20140372). The MAST Trial was registered on ClinicalTrials.gov (Identifier: NCT02242773). Informed consent was obtained from all participants prior to their inclusion in the study. Additionally, external datasets, including those from The Cancer Genome Atlas (TCGA), Radboud University Medical Center, and Karolinska Institute (PANDA challenge), were used in compliance with their respective data use agreements. All data were anonymized to ensure participant confidentiality and privacy.

Additional information

Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions

About this article

Cite this article

Van Booven, D.J., Chen, CB., Kryvenko, O.N. et al. Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading. npj Precis. Onc. 9, 151 (2025). https://doi.org/10.1038/s41698-025-00934-5
Download citation

# data[].attributes.attachment_link.title
Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading - npj Precision Oncology

# data[].attributes.attachment_link.description
Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, with Gleason grading critical for prognosis and treatment decisions. Machine learning (ML) models offer potential for automated grading but are limited by dataset biases, staining variability, and data scarcity, reducing their generalizability.

# data[].attributes.body
1. Lawsuit (소송)

- The former player filed a lawsuit against the team for negligence.

- The company is facing a lawsuit over alleged patent infringement.

2. Negligence (과실)

- The accident was caused by the driver's negligence.

- The hospital was sued for negligence in patient care.

3. Injury (부상)

- The suffered a serious injury during the match.

- The injury required immediate medical attention.

4. Compensatory (보상적인)

- The court awarded compensatory damages to the plaintiff.

- He received compensatory leave for the overtime worked.

5. Punitive (징벌적인)

- The judge imposed punitive damages to deter future misconduct.

- Punitive measures were taken against the company for environmental violations.

6. Reckless (무모한)

- His reckless driving led to a serious accident.

-The reckless behavior of the employees resulted in a workplace injury.

7. Hazard (위험)

- The construction site posed a hazard to pedestrians.

- The chemicals were stored improperly, creating a hazard.

8. Deformities (기형)

- The accident caused permanent deformities to his leg.

- The child was born with deformities due to a genetic disorder.

9. Recklessly (무모하게)

- He recklessly ignored the safety protocols.

- The company recklessly released the product without proper testing.

10. Precautions (예방 조치)

- The factory implemented strict precautions to prevent accidents.

- Travelers are advised to take necessary precautions during the pandemic.

# data[].attributes.attachment_link.title
Former MLB player suing ex-division rival over career-ending injury: ‘Obvious and avoidable risk’

# data[].attributes.attachment_link.description
Former MLB player Darin Ruf is suing the Cincinnati Reds, claiming he has “permanent and substantial” damage to his knee after an injury at the team’s ballpark.

# data[].attributes.body
1.counterpart

뜻 : 상대, 상대

예 : I personally nominate Korea to be a counterpart.

2.engage

뜻 : 관여하다, 참여하다

예 : Silence, in terms of citizens not engaging, simply not wanting to participate.

3.ceasefire

뜻 : 정전, 전투 중지

예 : The world was never told there was a day of global ceasefire and nonviolence.

4.conflict

뜻 : 갈등, 분쟁

예 : The conflict is still ongoing.

5.invade

뜻 : 침공, 침략하다

예 : After months of tensions and buildup, Russia invaded Ukraine at dawn on Feb. 24.

6. delegation

뜻 : 대표단, 위임

예 : So, let me give you an example of delegation in practice, right.

7.precondition

뜻 : 필수 조건

예 : A senior-level meeting is a precondition for the fundamental resolution of the tragic separation.

8. stall

뜻 : 지연되다

예 : However it has been stalled by tensions between Israel and the Palestinians.

9.financial

뜻 : 금융의, 경제의

예 : The government even offers financial support for airfare and lodging.

10.sector

뜻 : 부문, 분야

예 : Its banking sector was 10 times the GDP of the rest of the economy.

# data[].attributes.attachment_link.title
Starmer: ‘Sooner or later’ Russia must yield to peace

# data[].attributes.attachment_link.description
Leaders from Europe, Australia, New Zealand and Canada met virtually to discuss a proposed ceasefire between Ukraine and Russia

# data[].attributes.body
administration : 행정부

investigation : 조사

discrimination : 차별

allegations : 혐의, 주장

diversity : 다양성

equity : 형평성

inclusion : 포용

funding : 자금 지원

lawsuit : 소송

civil rights : 시민권

# data[].attributes.attachment_link.title
Celebi moves Bombay HC over security nod withdrawal

# data[].attributes.attachment_link.description
Celebi NAS Airport Services India has challenged the revocation of its security clearance in the Bombay High Court, following a similar plea in the Delhi HC. The government, represented by Solicitor General Tushar Mehta, cited national security concerns, arguing the company’s access to sensitive airport and passenger data necessitated the revocation. The Delhi HC will resume the hearing on Friday.

# data[].attributes.body
peril : 위험

Ignoring safety rules puts you in great peril.

Outplacement : 전직 지원

- 퇴직근로자에게 실직 충격 최소화 & 전직을 돕는 제도 or 행위

The company offered outplacement services after layoffs.

demonstrate : 입증하다

She can demonstrate her coding skills easily.

empathy : 공감

Doctors need empathy to comfort patients.

iteration: 반복, 되풀이

The app improved after several iterations.

be prone to ~ : ~하기 쉽다

- 상황이 바뀌기 쉬울 때 씀, tend to랑 뜻 같음

Kids are prone to catch colds in winter.

labor : 노동

Labor laws protect workers' rights.

mammogram : 유방 x선 촬영검사

She scheduled a mammogram for her annual checkup.

radiologist : 방사선학자 ( 영상학과 )

The radiologist analyzed the X-ray images.

ultrasound : 초음파

The doctor used ultrasound to check the baby’s health.

# data[].attributes.attachment_link.title
Opinion: Here are the jobs AI will impact most | CNN

# data[].attributes.attachment_link.description
To get a better sense of the effect AI might have on different industries across the labor market, we reached out to experts in medicine, law, art, retail, film, tech, education and agriculture.

# data[].attributes.body
Summary

The Trump administration revoked Harvard’s right to enroll international students after the university refused to share foreign students’ conduct records. Current students must transfer or lose legal status. The White House accused Harvard of anti-American behavior.

quarter

뜻: 4분의 1

예문: Only a quarter of the students passed the final exam.

enroll

뜻: 입학시키다, 등록하다

예문: She decided to enroll in the university next semester.

once-great

뜻: 한때 위대했던

예문: The once-great empire now lies in ruins.

→ 한때 위대했던 제국은 이제 폐허가 되었다.

demand

뜻: 요구하다, 요청하다

예문: The workers demand better pay and working conditions.

exodus

뜻: 이동, 출국, 이주

예문: There was a mass exodus from the city after the disaster.

threatens

뜻: 위협하다, 협박하다

예문: Climate change threatens the survival of many species.

stifle

뜻: ...의 숨을 막다, 질식시키다, 숨 막히게 하다

예문: The heat was so intense it felt like it would stifle us.

academic

뜻: 대학의, 학원의, 학구적인, 이론적인, 비실용적인, 대학생, 대학교수

예문: He is an academic who teaches philosophy at the university.

prowess

뜻: 용기, 무용, 용맹

예문: The knight was admired for his prowess in battle..

institution

뜻: 학회, 협회

예문: Harvard is a prestigious institution known for academic excellence.

# data[].attributes.attachment_link.title
Trump administration bars Harvard from enrolling international students | CNN

# data[].attributes.attachment_link.description
The Trump administration on Thursday revoked Harvard University’s ability to enroll international students, marking a sharp escalation in the battle over the Ivy League school’s autonomy in the face of the administration’s policy demands.

# data[].attributes.body
영어 단어

material 직물, 천 / (물건의) 재료

-> Wood is a common building material.

imitated 본뜬, 가짜의

-> The child imitated his father’s way of walking.

futuristic 초현대적인 / 미래를 상상하는

-> The building has a futuristic design made of glass and steel.

reflected (거울, 유리 등에) 비추다 / 반사하다

-> The sun reflected off the water.

unveiled (그림·동상 등을 처음으로 공개하기 위해) 덮개를 벗기다

-> The museum unveiled a new painting by Van Gogh.

furniture 가구

-> They bought new furniture for their living room.

backdrop (무대의) 배경

-> The stage had a beautiful sunset backdrop.

sophistication 교양, 세련

-> Her dress showed great sophistication and elegance.

throughout 도처에 / 동안, 내내

-> She was nervous throughout the interview.

ultimate 궁극적인, 최후

-> Our ultimate goal is to achieve world peace.

요약

1960년대 우주 경쟁 시대에 탄생한 우주 시대 디자인은 곡선형 가구, 반사 재질, 유선형 구조 등 미래적이고 실험적인 요소로 가득하다. 최근 이러한 스타일이 다시 인기를 얻고 있으며, 이는 기술 발전과 미래에 대한 낙관주의를 반영한다.

주 시대 디자인의 부활이 단순한 복고 유행을 넘어, 미래에 대한 새로운 상상력과 낙관주의를 반영한다고 설명다. 기술의 발전과 함께, 과거의 미래적 비전을 현대적으로 재해석 하려는 움직임이 디자인 전반에서 나타나고 있다. 이러한 흐름은 단순한 유행을 넘어, 디자인을 통해 미래에 대한 희망과 상상력을 표현하려는 현대인들의 욕구를 반영한다. 우주 시대 디자인은 과거의 미래를 현재에 구현함으로써, 새로운 시대의 미적 감각을 제시하고 있다.

기사 링크

The stunning retro space-age homes that are perfect for today

# data[].attributes.attachment_link.title
The stunning retro space-age homes that are perfect for today

# data[].attributes.attachment_link.description
Space-age style is skyrocketing in pop culture. We look back at the remarkable retro-futuristic origins of 2025’s biggest interiors aesthetic.

# data[].attributes.headline
기사 요약 + 모르는 단어

# data[].attributes.subject
기사 요약 + 모르는 단어

# data[].attributes.body
🔒 What is Vanguard? (Official Riot Games Summary)

Vanguard is Riot Games’ custom-built anti-cheat system for VALORANT. It’s designed to maintain competitive integrity by detecting and preventing cheating at both the user and system levels.

🛠️ Key Components

Client Component: Runs while VALORANT is open to monitor for cheats.

Kernel Driver: Operates at the system level (kernel mode) and launches at computer startup. This allows it to catch cheats that try to operate below the game's surface.

🔐 Security & Privacy

Riot states that Vanguard does not collect personal information and only checks for cheat-related behavior.

It has gone through internal security reviews and external audits by independent cybersecurity firms.

The system complies with global data privacy regulations, including GDPR.

👤 User Control

You can uninstall or disable Vanguard at any time.

If Vanguard is disabled, you won’t be able to play VALORANT until it’s re-enabled.

A System Tray icon shows when Vanguard is running, providing transparency and easy access.

⚠️ Compatibility & Notifications

Vanguard requires Secure Boot to be enabled for better protection.

It uses Windows notifications to alert users if there's an issue or if it blocks software it considers unsafe. 단어 integrity – 진실성, 정직, (시스템의) 무결성

surface – 표면, 외부

behavior – 행동, 작동 방식

independent cybersecurity – 독립적인 사이버 보안 (회사 또는 전문가)

regulations – 규정, 법규

transparency – 투명성, 공개성

notifications – 알림, 통지

automatically – 자동으로

vulnerable – 취약한, 공격받기 쉬운

# data[].attributes.attachment_link.readable_text
Riot Vanguard is Riot Games' custom game security software, designed to uphold the highest levels of competitive integrity for our offerings. Riot Vanguard consists of a client that runs while VALORANT is active, as well as the usage of a kernel mode driver.

Both the client and the driver of Riot Vanguard have been developed in-house, with both game safety and personal computer safety being a priority. We've made this commitment through extensive testing and by reviewing the product both internally and with external security reviews by industry experts.

Our commitment to safety includes our commitment to your privacy. Riot Vanguard was made with Riot Games' dedication to data privacy specifically in mind, and we worked with our legal and compliance teams to ensure it adheres to regional data privacy laws. Specifics on what data we use and collect are available here.

With our latest version, we're making changes to give you more visibility and control of Vanguard, while maintaining all its security features. We've added a visible System Tray icon that lets you know when Riot Vanguard is running. This will come with the functionality of being able to turn Vanguard on and off, without uninstalling it, though it will also come with an uninstaller. For more on how to disable or uninstall, head here.

We're also addressing some of the compatibility issues players may have had with Vanguard initially. We've automatically enabled similar security standards to Windows Secure Boot while Vanguard is on. These standards were previously addressed through not being able to connect to Vanguard. Vanguard will also inform you what vulnerable vectors are being affected through a Windows notification.

To ensure the integrity of players' games, Riot Vanguard must be active during VALORANT gameplay. However, unexpected interactions with either the Vanguard client or the Vanguard driver on your device may impact your ability to play.

Should you wish to uninstall Riot Vanguard, you should consult the Uninstalling or Disabling Riot Vanguard guide. Please keep in mind that uninstalling VALORANT will not uninstall Riot Vanguard.

Riot Vanguard is an on-boot application. That means if you do choose to disable it and later decide you'd like to play VALORANT, you will have to restart your computer.

# data[].attributes.attachment_link.description
Riot Vanguard is Riot Games' custom game security software, designed to uphold the highest levels of competitive integrity for our offerings. Riot Vanguard consists of a client that runs while VALO...

# data[].attributes.body
1. defeated – 패배시켰다, 이겼다

Tottenham defeated Manchester United 1-0.

2. final – 결승전

The Europa League final was held in Bilbao.

3. secured – 확보했다, 획득했다

The victory secured Spurs their first title in 17 years.

4. reputation – 명성, 평판

The team abolished the reputation for not finishing big matches.

5. trophy – 트로피, 우승컵

Spurs win the club’s first trophy since 2008.

6. continental – 유럽 대항전의, 대륙 간의

First continental silverware since the 1983-84 season.

7. sloppy – 느슨한, 엉성한

The first half was sloppy with both teams struggling.

8. midfielder – 미드필더, 중원 선수

Tottenham’s midfielder Pape Sarr whipped in a cross.

9. deflecting – 방향을 바꾸다, 굴절시키다

The ball deflected off Luke Shaw.

10. top scorer – 최다 득점자

Spurs’ top scorer got a slight touch on the ball.

# data[].attributes.attachment_link.title
Tottenham Hotspur wins first trophy in 17 years by beating Manchester United 1-0 in Europa League final | CNN

# data[].attributes.attachment_link.description
Tottenham Hotspur banished the reputation for not getting over the finish line by defeating Manchester United 1-0 to win the Europa League final on Wednesday in Bilbao, Spain.

# data[].attributes.body
https://edition.cnn.com/2025/05/22/europe/russia-buffer-zone-ukraine-border-intl

Participate (참여하다)
Students are encouraged to participate actively in class discussions.

Procrastinate (미루다, 질질 끌다)
If you procrastinate, you might not finish your assignments on time.

Comprehend (이해하다)
It took me a while to comprehend the main idea of the article.

Analyze (분석하다)
We were asked to analyze the poem and explain its meaning.

Efficient (효율적인)
Using a planner can help you become a more efficient student.

Achievement (성취, 업적)
Winning the math competition was a great achievement for our school.

Cooperate (협력하다)
To succeed in group projects, everyone must cooperate.

Deadline (마감 기한)
I’m working hard to meet the deadline for our science report.

Concentration (집중력)
Listening to music helps improve my concentration while studying.

Academic (학업의, 학문적인)
She received an award for her outstanding academic performance.

# data[].attributes.headline
기사 요약 + 모르는 단어

# data[].attributes.subject
기사 요약 + 모르는 단어

# data[].attributes.body
Trump says he has a ‘little problem’ with Tim Cook("트럼프는 팀 쿡(애플 CEO)과 약간의 불만이 있다")

요약) 2025년 5월 15일 CNN 보도에 따르면, 도널드 트럼프 미국 대통령은 애플 CEO 팀 쿡에게 인도에서의 아이폰 생산 확대에 대해 불만을 표명했습니다. 트럼프 대통령은 애플이 미국 내에서 더 많은 생산을 하길 원하며, 인도는 높은 관세를 부과하는 국가 중 하나로, 미국 기업들이 인도에서 생산하는 것에 대해 우려를 나타냈다.

한편, 인도는 미국과의 무역 협상을 통해 관세를 줄이려는 움직임을 보이고 있습니다. 2025년 5월 6일, 인도는 자동차 부품에 대한 "제로 대 제로" 관세 제안을 하며, 미국과의 무역 협상을 재개하려는 의지를 나타냈다.

모르는 단어)

1. Tariff
뜻: 관세. 수입품에 부과되는 세금

2. Manufacture
뜻: 제조하다. 상품을 대량으로 생산하다

3. Reciprocal
뜻: 상호적인. 서로 주고받는

4. Deficit
뜻: 적자. 수입보다 지출이 많은 상태

5. Negotiation
뜻: 협상. 문제를 해결하기 위한 대화

6. Impose
뜻: (세금, 벌 등을) 부과하다

7. Incentive
뜻: 장려책, 유인책

8. Supply chain
뜻: 공급망; 상품이 생산되어 소비자에게 전달되는 전체 과정

9. Dispute
뜻: 분쟁, 논쟁

10. Criticize
뜻: 비판하다

# data[].attributes.attachment_link.title
Trump says he has a ‘little problem’ with Tim Cook | CNN Business

# data[].attributes.attachment_link.description
President Donald Trump isn’t pleased with Apple CEO Tim Cook’s plan to manufacture iPhones bound for the United States at newly built plants in India.

# data[].attributes.body
1. Destroyer - (구축함)

2. Slipway - (진수로)

3. Hull - (선체)

4. Stern - (선미)

5. Bow - (선수)

6. Flatcar - (평판 화차)

7. Empiricism - (경험주의)

8. Phased-array radar - (위상 배열 레이더)

9. Vertical Launch System - (VLS, 수직 발사 시스템)

10. Capsize – (전복되다)

1. Destroyer (구축함)
The navy launched a new destroyer equipped with advanced missile systems.
해군은 첨단 미사일 시스템을 장착한 새로운 구축함을 진수했다.

2. Slipway (진수로)
The ship slid into the water smoothly from the slipway.
배는 진수로에서 부드럽게 물로 미끄러져 들어갔다.

3. Hull (선체)
The damage to the hull caused the ship to take on water.
선체에 생긴 손상으로 배에 물이 들어오기 시작했다.

4. Stern (선미)

The stern of the ship was badly damaged during the collision.

충돌로 인해 배의 선미가 심하게 손상되었다.

5. Bow (선수)
Waves crashed over the bow as the destroyer cut through the storm.
구축함이 폭풍을 가르며 나아가자 파도가 선수 위로 넘실거렸다.

6. Flatcar (평판 화차)
The large equipment was loaded onto a flatcar for transport.
대형 장비는 평판 화차에 실려 운송되었다.

7. Empiricism (경험주의)
Empiricism emphasizes learning through observation and experience.
경험주의는 관찰과 경험을 통한 학습을 중시한다.

8. Phased-array radar (위상 배열 레이더)
The destroyer was equipped with a phased-array radar for early threat detection.
구축함은 조기 위협 탐지를 위한 위상 배열 레이더를 장착하고 있었다.

9. Vertical Launch System (VLS, 수직 발사 시스템)
The warship uses a Vertical Launch System to fire missiles vertically.
전함은 수직 발사 시스템을 이용해 미사일을 수직으로 발사한다.

10. Capsize (전복되다)
The small boat capsized in rough waters, but the passengers were unharmed.

작은 보트가 거친 물살에 전복되었지만, 탑승자들은 다치지 않았다.

# data[].attributes.attachment_link.authors[].name
Brad Lendon, Gawon Bae, Mike Valerio, Yoonjung Seo

# data[].attributes.attachment_link.title
Kim Jong Un’s fury after watching North Korea’s new navy destroyer crippled in botched launch | CNN

# data[].attributes.attachment_link.description
North Korea’s newest warship was severely damaged during a launch ceremony Wednesday, with leader Kim Jong Un, who witnessed the accident, saying it brought shame to the nation’s prestige and vowing to punish those found responsible, state media reported.

# data[].attributes.body
링크 : https://www.aljazeera.com/sports/2025/5/21/tottenham-defeat-manchester-united-to-win-europa-league-final

1. Triumph

뜻: (큰) 승리, 대성공

예문: Tottenham Hotspur triumphed over Manchester United to win their first trophy in 17 years.

2. Drought

뜻: (오랜 기간의) 결핍, 가뭄

예문: The victory ended a painful 17-year trophy drought for Spurs.

3. Scrappy

뜻: (경기 등이) 난잡한, 치열한, 투지가 넘치는

예문: Brennan Johnson's scrappy goal in the first half gave Tottenham the lead.

4. Clinch

뜻: (승리, 계약 등을) 성사시키다, 확정 짓다

예문: Tottenham clinched a spot in next season’s Champions League with their win.

5. Jubilant

뜻: 매우 기뻐하는, 환희에 찬

예문: Jubilant celebrations erupted among Tottenham fans after the final whistle.

6. Acrobatic

뜻: 곡예의, 몸놀림이 날렵한

예문: Micky van de Ven made an acrobatic clearance to deny Manchester United an equalizer.

7. Falter

뜻: (자신감 부족으로) 흔들리다, 주저하다, 실패하다

예문: This win seems to have dispelled the club's long-standing reputation for faltering in crucial matches.

8. Lucrative

뜻: 수익성이 좋은, 이익이 많이 나는

예문: The victory secured a lucrative spot in the upcoming Champions League for Tottenham.

9. Deflect

뜻: (공 등이) 방향을 바꾸다, 빗나가다

예문: The ball deflected off Luke Shaw before crossing the line for the only goal.

10. Resilient

뜻: 회복력 있는, 탄력적인

예문: Tottenham showed a resilient defense in the second half to preserve their lead

# data[].attributes.attachment_link.title
Tottenham defeat Manchester United to win Europa League final | Football News | Al Jazeera

# data[].attributes.attachment_link.description
We use cookies and other tracking technologies to deliver and personalize content and ads, enable features, measure site performance, and enable social media sharing. You can choose to customize your preferences.Learn more about our Cookie Policy.

# data[].attributes.attachment_link.title
제목을-입력해주세요_ (1)

# data[].attributes.headline
영어 발표(환경 표지)

# data[].attributes.subject
영어 발표(환경 표지)

# data[].attributes.attachment_link.title
제목을-입력해주세요_-001

# data[].attributes.attachment_link.title
Black Red Bold Paper Photography Exhibition Event Poster

# data[].attributes.attachment_link.title
제목을 입력해주세요_-001

# data[].attributes.attachment_link.title
제목을-입력해주세요_-001

# data[].attributes.attachment_link.title
Blue & White Modern Winter Sale Poster (Portrait)

# data[].attributes.attachment_link.title
제목을 입력해주세요_-001

# data[].attributes.attachment_link.title
3월 첫주 - winter vacation & 2025 plan 포스터 예시

# data[].attributes.attachment_link.title
black vintage newspaper birthday party poster

# data[].attributes.attachment_link.title
제목을-입력해주세요_-002

# data[].attributes.attachment_link.title
Black And White Simple Personal Business Card