Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial

Jay Jasti, Hua Zhong, Vandana Panwar, Vipul Jarmale, Jeffrey Miyata, Deyssy Carrillo, Alana Christie, Dinesh Rakheja, Zora Modrusan, Edward Ernest Kadel III, Niha Beig, Mahrukh Huseni, James Brugarolas, Payal Kapur, Satwik Rajaram·May 28, 2024

Summary

A deep learning model has been developed to predict the RNA-based Angioscore, a measure of angiogenesis in renal cell carcinoma, from histopathology slides using H&E-stained images. The model, with visual interpretability, shows high correlations (0.77-0.73) with RNA Angioscore across multiple cohorts and reveals associations with tumor grade, stage, and driver mutations. It outperforms CD31 marker in predicting anti-angiogenic therapy response and offers a cost-effective alternative to transcriptomic biomarkers. The study emphasizes the need for predictive biomarkers in ccRCC treatment, particularly for combination therapies, and demonstrates the model's potential to streamline clinical adoption by providing a more accessible and consistent measure of angiogenesis and treatment response. Future work includes refining the model, expanding data, and exploring multi-modal approaches for improved predictive capabilities.

Key findings

5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the lack of predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC), specifically focusing on the response to anti-angiogenic (AA) therapy . This is not a new problem as existing therapies for ccRCC, such as immune checkpoint inhibitors, mTOR inhibitors, and HIF-2 inhibitors, have shown heterogeneous outcomes and do not uniformly benefit all patients . The challenge lies in the absence of evidence-based therapeutic decisions between regimens combining immune checkpoint inhibitors and anti-angiogenic agents, and the uncertainty regarding the synergistic effects of this combination . The paper highlights the critical need for predictive biomarkers of treatment response in ccRCC, emphasizing the limitations of current approaches and the potential of histopathology-based AI models to overcome these challenges .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a deep learning (DL) model can predict the Angioscore solely from histopathology images, specifically H&E images, to predict the response to anti-angiogenic therapy in renal cell carcinoma . The study focuses on overcoming the limitations of transcriptomic assays by using DL models to predict the Angioscore from ubiquitous histopathology slides, aiming to provide a more interpretable and practical approach for predicting treatment response in metastatic clear cell renal cell carcinoma (ccRCC) . The hypothesis is centered around the idea that DL models can accurately predict the Angioscore, which quantifies angiogenesis, from histopathology images, offering a potential solution to the challenges faced by transcriptomic assays in predicting response to anti-angiogenic therapy in ccRCC .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides in order to address the lack of predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC) . This DL model aims to predict the Angioscore, an RNA-based quantification of angiogenesis, from histopathology images, providing a visual representation of vascular features . The model consists of three main arms: the mask prediction arm, the Angio score prediction arm, and the consistency arm .

  • The mask prediction arm utilizes a U-Net architecture with an ImageNet pre-trained ResNet-18 backbone to output a predicted vascular mask with two classes (CD31 +/-) .
  • The Angio score prediction arm shares the encoder arm of the U-Net and several convolutional layers to output a single number representing the Angio score .
  • The consistency arm calculates the fractional CD31 positive activation from the mask output to predict the RNA output, aiming to predict the Angioscore solely from H&E images .

Furthermore, the paper discusses the challenges faced by transcriptomic assays in clinical adoption, such as standardization, time delay, and high cost, and highlights the heterogeneity of ccRCC tumors, making sampling multiple areas for sequencing impractical . The DL model presented in the paper offers a solution to these challenges by providing a more accessible and interpretable method for predicting treatment response based on histopathology images . The deep learning (DL) model proposed in the paper offers several key characteristics and advantages compared to previous methods :

  • Novel Approach: The DL model aims to predict the Angioscore, an RNA-based quantification of angiogenesis, solely from histopathology images, addressing the lack of predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC) .
  • Model Architecture: The model consists of three main arms: the mask prediction arm, the Angio score prediction arm, and the consistency arm. The mask prediction arm uses a U-Net architecture with an ImageNet pre-trained ResNet-18 backbone to output a predicted vascular mask. The Angio score prediction arm outputs a single number representing the Angio score, while the consistency arm predicts the RNA output from the mask output .
  • Training Strategies: The model adopts novel loss functions and training strategies, such as batching with different batch sizes for RNA and CD31 ground truth patches, and utilizing a combination of loss functions for the two types of batches .
  • Performance Comparison: The DL-based predictions of Sunitinib response from H&E images are comparable and superior to CD31 IHC-based assays, demonstrating the effectiveness of the model in predicting treatment response. The model captures the inverse relationship between angiogenesis and response to immune checkpoint inhibitors (ICI) .
  • Interpretability: To overcome the lack of interpretability in typical DL models, this model produces a visual representation of vascular features from histopathology images, providing a more accessible and interpretable method for predicting treatment response based on visual cues .
  • Clinical Adoption: The DL model offers a solution to the challenges faced by transcriptomic assays in clinical adoption, such as standardization, time delay, and high cost, by providing a more practical and efficient method for predicting treatment response in ccRCC tumors .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of predicting anti-angiogenic therapy response in renal cancer. Noteworthy researchers in this area include Jay Jasti, Hua Zhong, Vandana Panwar, Jeffrey Miyata, Dinesh Rakheja, James Brugarolas, Payal Kapur, Satwik Rajaram, and others . These researchers have focused on developing predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC) using innovative approaches such as deep learning models applied to histopathology slides .

The key solution mentioned in the paper involves the development of a novel deep learning approach to predict the Angioscore, which is an RNA-based quantification of angiogenesis, from ubiquitous histopathology slides. This approach aims to overcome the limitations of transcriptomic assays, such as standardization, time delay, and high cost, by providing a visual representation of vascular morphology directly from histopathology images. By utilizing computational models to segment endothelial cells visible in H&E stained slides, the model can predict the Angioscore, which is crucial for predicting anti-angiogenic therapy response in renal cancer .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • Training of Models: The models were trained by alternating between patches of data with CD31 and RNA ground truth, updating the network weights to maximize agreement with the appropriate ground truth and consistency between the Angioscore and CD31 predictions .
  • Validation of Models: The performance of the DL model was tested on held-out portions of the training sets. The RNA Angioscore and the percentage of positive pixels from the CD31 mask arm were used to correlate with the RNA Angioscore. The H&E DL Angioscore was compared to the RNA Angioscore on the held-out portion of the training TCGA cohort .
  • Model Inference: Stain normalization was performed to reduce the impact of slide color variations, and patches were randomly selected from tumor areas for model inference. The model was applied to individual patches from a sample, and the median score across all patches in the slide was reported .
  • Survival Analysis: Survival analysis involved stratifying patients into groups based on a given threshold level for the H&E DL Angioscore. Univariate Cox proportional hazard models were used to determine the characteristics associated with overall survival, and Kaplan Meier curves were generated .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the UTSW TKI Response Dataset, which consists of 145 H&E-stained slides from patients undergoing Anti-VEGF treatment for metastatic renal cell carcinoma . The final H&E DL Angio model and all code used in the manuscript will be released at the Rajaram Lab’s public GitHub page upon publication .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study utilized a deep learning (DL) approach to predict the Angioscore from histopathology slides, aiming to overcome the limitations of interpretability in typical DL models . The model demonstrated reliable performance by correlating with the RNA-based Angioscore across multiple cohorts, showing a strong correlation between the predicted scores and the actual RNA-based Angioscore . Additionally, the study compared the predictions from the DL model with traditional CD31 IHC-based assays and found that the DL-based predictions of Sunitinib response were comparable to RNA-based predictions and superior to CD31 IHC-based assays in both real-world and clinical trial data . These findings indicate that the DL model can effectively predict Angioscore solely from H&E images, showcasing its potential to rival the gold-standard RNA-based Angioscore .


What are the contributions of this paper?

The paper makes several significant contributions:

  • It presents a novel deep learning approach to predict the Angioscore from ubiquitous histopathology slides, aiming to overcome the lack of interpretability in typical deep learning models .
  • The study leverages transcriptomics for precision diagnosis, particularly focusing on cancer and sepsis, providing valuable insights into leveraging molecular data for accurate diagnosis .
  • The research reveals clinically significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type through ontological analyses, shedding light on the heterogeneity and progression of renal cancer .
  • It predicts oncologic outcomes in small renal tumors and explores the effects on survival of specific mutations in clear-cell renal-cell carcinoma, contributing to the understanding of genetic factors influencing tumor behavior and patient outcomes .
  • The paper also discusses the development and validation of a vascularity-based architectural classification for clear cell renal cell carcinoma, highlighting the correlation with pathological prognostic factors, gene expression patterns, and clinical outcomes .

What work can be continued in depth?

Further work that can be continued in depth based on the provided context includes:

  • Enhancing the reliability and robustness of the deep learning model by increasing training data across diverse cohorts to account for variations in staining or microscopes .
  • Improving region identification models to enhance the performance of the model, as the current model is only applied to tissue from the tumor regions in whole slide images .
  • Validating the performance of the model on data from additional clinical trials that include anti-angiogenic therapy arms to ensure its effectiveness across different treatment scenarios .

Introduction
Background
Overview of renal cell carcinoma (ccRCC) and angiogenesis
Importance of predictive biomarkers in treatment
Objective
Development and evaluation of a deep learning model
Aim to improve treatment response prediction and cost-effectiveness
Method
Data Collection
Source and composition of histopathology slide dataset (H&E-stained images)
Inclusion of multiple cohorts for model validation
Data Preprocessing
Image preprocessing techniques (e.g., resizing, normalization)
Annotation and extraction of relevant features for model training
Model Architecture
Description of the deep learning model (e.g., convolutional neural network)
Integration of visual interpretability techniques
Model Training and Evaluation
Performance metrics (correlation coefficients, 0.77-0.73)
Comparison with CD31 marker for anti-angiogenic therapy response
Model Validation
Cross-validation across different cohorts
Assessment of associations with tumor grade, stage, and driver mutations
Results and Discussion
Model Performance
Outperformance of CD31 marker in predicting treatment response
Cost-effectiveness compared to transcriptomic biomarkers
Clinical Implications
Streamlining clinical adoption with accessible and consistent Angioscore
Potential for personalized treatment recommendations
Limitations and Future Work
Refinement of the model for improved accuracy
Data expansion to enhance generalizability
Exploration of multi-modal approaches for enhanced predictive capabilities
Conclusion
Summary of the model's impact on ccRCC treatment and angiogenesis assessment
Future directions for research and clinical implementation
Basic info
papers
computer vision and pattern recognition
quantitative methods
machine learning
artificial intelligence
Advanced features
Insights
How well does the model correlate with RNA Angioscore, and what is the range of these correlations?
What is the primary purpose of the developed deep learning model?
What does the study suggest about the need for predictive biomarkers in ccRCC treatment, and what is its potential impact on clinical adoption?
How does the model compare to the CD31 marker in predicting anti-angiogenic therapy response?

Histopathology Based AI Model Predicts Anti-Angiogenic Therapy Response in Renal Cancer Clinical Trial

Jay Jasti, Hua Zhong, Vandana Panwar, Vipul Jarmale, Jeffrey Miyata, Deyssy Carrillo, Alana Christie, Dinesh Rakheja, Zora Modrusan, Edward Ernest Kadel III, Niha Beig, Mahrukh Huseni, James Brugarolas, Payal Kapur, Satwik Rajaram·May 28, 2024

Summary

A deep learning model has been developed to predict the RNA-based Angioscore, a measure of angiogenesis in renal cell carcinoma, from histopathology slides using H&E-stained images. The model, with visual interpretability, shows high correlations (0.77-0.73) with RNA Angioscore across multiple cohorts and reveals associations with tumor grade, stage, and driver mutations. It outperforms CD31 marker in predicting anti-angiogenic therapy response and offers a cost-effective alternative to transcriptomic biomarkers. The study emphasizes the need for predictive biomarkers in ccRCC treatment, particularly for combination therapies, and demonstrates the model's potential to streamline clinical adoption by providing a more accessible and consistent measure of angiogenesis and treatment response. Future work includes refining the model, expanding data, and exploring multi-modal approaches for improved predictive capabilities.
Mind map
Exploration of multi-modal approaches for enhanced predictive capabilities
Data expansion to enhance generalizability
Refinement of the model for improved accuracy
Potential for personalized treatment recommendations
Streamlining clinical adoption with accessible and consistent Angioscore
Cost-effectiveness compared to transcriptomic biomarkers
Outperformance of CD31 marker in predicting treatment response
Assessment of associations with tumor grade, stage, and driver mutations
Cross-validation across different cohorts
Comparison with CD31 marker for anti-angiogenic therapy response
Performance metrics (correlation coefficients, 0.77-0.73)
Integration of visual interpretability techniques
Description of the deep learning model (e.g., convolutional neural network)
Annotation and extraction of relevant features for model training
Image preprocessing techniques (e.g., resizing, normalization)
Inclusion of multiple cohorts for model validation
Source and composition of histopathology slide dataset (H&E-stained images)
Aim to improve treatment response prediction and cost-effectiveness
Development and evaluation of a deep learning model
Importance of predictive biomarkers in treatment
Overview of renal cell carcinoma (ccRCC) and angiogenesis
Future directions for research and clinical implementation
Summary of the model's impact on ccRCC treatment and angiogenesis assessment
Limitations and Future Work
Clinical Implications
Model Performance
Model Validation
Model Training and Evaluation
Model Architecture
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Overview of renal cell carcinoma (ccRCC) and angiogenesis
Importance of predictive biomarkers in treatment
Objective
Development and evaluation of a deep learning model
Aim to improve treatment response prediction and cost-effectiveness
Method
Data Collection
Source and composition of histopathology slide dataset (H&E-stained images)
Inclusion of multiple cohorts for model validation
Data Preprocessing
Image preprocessing techniques (e.g., resizing, normalization)
Annotation and extraction of relevant features for model training
Model Architecture
Description of the deep learning model (e.g., convolutional neural network)
Integration of visual interpretability techniques
Model Training and Evaluation
Performance metrics (correlation coefficients, 0.77-0.73)
Comparison with CD31 marker for anti-angiogenic therapy response
Model Validation
Cross-validation across different cohorts
Assessment of associations with tumor grade, stage, and driver mutations
Results and Discussion
Model Performance
Outperformance of CD31 marker in predicting treatment response
Cost-effectiveness compared to transcriptomic biomarkers
Clinical Implications
Streamlining clinical adoption with accessible and consistent Angioscore
Potential for personalized treatment recommendations
Limitations and Future Work
Refinement of the model for improved accuracy
Data expansion to enhance generalizability
Exploration of multi-modal approaches for enhanced predictive capabilities
Conclusion
Summary of the model's impact on ccRCC treatment and angiogenesis assessment
Future directions for research and clinical implementation
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the lack of predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC), specifically focusing on the response to anti-angiogenic (AA) therapy . This is not a new problem as existing therapies for ccRCC, such as immune checkpoint inhibitors, mTOR inhibitors, and HIF-2 inhibitors, have shown heterogeneous outcomes and do not uniformly benefit all patients . The challenge lies in the absence of evidence-based therapeutic decisions between regimens combining immune checkpoint inhibitors and anti-angiogenic agents, and the uncertainty regarding the synergistic effects of this combination . The paper highlights the critical need for predictive biomarkers of treatment response in ccRCC, emphasizing the limitations of current approaches and the potential of histopathology-based AI models to overcome these challenges .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a deep learning (DL) model can predict the Angioscore solely from histopathology images, specifically H&E images, to predict the response to anti-angiogenic therapy in renal cell carcinoma . The study focuses on overcoming the limitations of transcriptomic assays by using DL models to predict the Angioscore from ubiquitous histopathology slides, aiming to provide a more interpretable and practical approach for predicting treatment response in metastatic clear cell renal cell carcinoma (ccRCC) . The hypothesis is centered around the idea that DL models can accurately predict the Angioscore, which quantifies angiogenesis, from histopathology images, offering a potential solution to the challenges faced by transcriptomic assays in predicting response to anti-angiogenic therapy in ccRCC .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel deep learning (DL) approach to predict the Angioscore from ubiquitous histopathology slides in order to address the lack of predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC) . This DL model aims to predict the Angioscore, an RNA-based quantification of angiogenesis, from histopathology images, providing a visual representation of vascular features . The model consists of three main arms: the mask prediction arm, the Angio score prediction arm, and the consistency arm .

  • The mask prediction arm utilizes a U-Net architecture with an ImageNet pre-trained ResNet-18 backbone to output a predicted vascular mask with two classes (CD31 +/-) .
  • The Angio score prediction arm shares the encoder arm of the U-Net and several convolutional layers to output a single number representing the Angio score .
  • The consistency arm calculates the fractional CD31 positive activation from the mask output to predict the RNA output, aiming to predict the Angioscore solely from H&E images .

Furthermore, the paper discusses the challenges faced by transcriptomic assays in clinical adoption, such as standardization, time delay, and high cost, and highlights the heterogeneity of ccRCC tumors, making sampling multiple areas for sequencing impractical . The DL model presented in the paper offers a solution to these challenges by providing a more accessible and interpretable method for predicting treatment response based on histopathology images . The deep learning (DL) model proposed in the paper offers several key characteristics and advantages compared to previous methods :

  • Novel Approach: The DL model aims to predict the Angioscore, an RNA-based quantification of angiogenesis, solely from histopathology images, addressing the lack of predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC) .
  • Model Architecture: The model consists of three main arms: the mask prediction arm, the Angio score prediction arm, and the consistency arm. The mask prediction arm uses a U-Net architecture with an ImageNet pre-trained ResNet-18 backbone to output a predicted vascular mask. The Angio score prediction arm outputs a single number representing the Angio score, while the consistency arm predicts the RNA output from the mask output .
  • Training Strategies: The model adopts novel loss functions and training strategies, such as batching with different batch sizes for RNA and CD31 ground truth patches, and utilizing a combination of loss functions for the two types of batches .
  • Performance Comparison: The DL-based predictions of Sunitinib response from H&E images are comparable and superior to CD31 IHC-based assays, demonstrating the effectiveness of the model in predicting treatment response. The model captures the inverse relationship between angiogenesis and response to immune checkpoint inhibitors (ICI) .
  • Interpretability: To overcome the lack of interpretability in typical DL models, this model produces a visual representation of vascular features from histopathology images, providing a more accessible and interpretable method for predicting treatment response based on visual cues .
  • Clinical Adoption: The DL model offers a solution to the challenges faced by transcriptomic assays in clinical adoption, such as standardization, time delay, and high cost, by providing a more practical and efficient method for predicting treatment response in ccRCC tumors .

Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of predicting anti-angiogenic therapy response in renal cancer. Noteworthy researchers in this area include Jay Jasti, Hua Zhong, Vandana Panwar, Jeffrey Miyata, Dinesh Rakheja, James Brugarolas, Payal Kapur, Satwik Rajaram, and others . These researchers have focused on developing predictive biomarkers for treatment response in metastatic clear cell renal cell carcinoma (ccRCC) using innovative approaches such as deep learning models applied to histopathology slides .

The key solution mentioned in the paper involves the development of a novel deep learning approach to predict the Angioscore, which is an RNA-based quantification of angiogenesis, from ubiquitous histopathology slides. This approach aims to overcome the limitations of transcriptomic assays, such as standardization, time delay, and high cost, by providing a visual representation of vascular morphology directly from histopathology images. By utilizing computational models to segment endothelial cells visible in H&E stained slides, the model can predict the Angioscore, which is crucial for predicting anti-angiogenic therapy response in renal cancer .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • Training of Models: The models were trained by alternating between patches of data with CD31 and RNA ground truth, updating the network weights to maximize agreement with the appropriate ground truth and consistency between the Angioscore and CD31 predictions .
  • Validation of Models: The performance of the DL model was tested on held-out portions of the training sets. The RNA Angioscore and the percentage of positive pixels from the CD31 mask arm were used to correlate with the RNA Angioscore. The H&E DL Angioscore was compared to the RNA Angioscore on the held-out portion of the training TCGA cohort .
  • Model Inference: Stain normalization was performed to reduce the impact of slide color variations, and patches were randomly selected from tumor areas for model inference. The model was applied to individual patches from a sample, and the median score across all patches in the slide was reported .
  • Survival Analysis: Survival analysis involved stratifying patients into groups based on a given threshold level for the H&E DL Angioscore. Univariate Cox proportional hazard models were used to determine the characteristics associated with overall survival, and Kaplan Meier curves were generated .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the UTSW TKI Response Dataset, which consists of 145 H&E-stained slides from patients undergoing Anti-VEGF treatment for metastatic renal cell carcinoma . The final H&E DL Angio model and all code used in the manuscript will be released at the Rajaram Lab’s public GitHub page upon publication .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study utilized a deep learning (DL) approach to predict the Angioscore from histopathology slides, aiming to overcome the limitations of interpretability in typical DL models . The model demonstrated reliable performance by correlating with the RNA-based Angioscore across multiple cohorts, showing a strong correlation between the predicted scores and the actual RNA-based Angioscore . Additionally, the study compared the predictions from the DL model with traditional CD31 IHC-based assays and found that the DL-based predictions of Sunitinib response were comparable to RNA-based predictions and superior to CD31 IHC-based assays in both real-world and clinical trial data . These findings indicate that the DL model can effectively predict Angioscore solely from H&E images, showcasing its potential to rival the gold-standard RNA-based Angioscore .


What are the contributions of this paper?

The paper makes several significant contributions:

  • It presents a novel deep learning approach to predict the Angioscore from ubiquitous histopathology slides, aiming to overcome the lack of interpretability in typical deep learning models .
  • The study leverages transcriptomics for precision diagnosis, particularly focusing on cancer and sepsis, providing valuable insights into leveraging molecular data for accurate diagnosis .
  • The research reveals clinically significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type through ontological analyses, shedding light on the heterogeneity and progression of renal cancer .
  • It predicts oncologic outcomes in small renal tumors and explores the effects on survival of specific mutations in clear-cell renal-cell carcinoma, contributing to the understanding of genetic factors influencing tumor behavior and patient outcomes .
  • The paper also discusses the development and validation of a vascularity-based architectural classification for clear cell renal cell carcinoma, highlighting the correlation with pathological prognostic factors, gene expression patterns, and clinical outcomes .

What work can be continued in depth?

Further work that can be continued in depth based on the provided context includes:

  • Enhancing the reliability and robustness of the deep learning model by increasing training data across diverse cohorts to account for variations in staining or microscopes .
  • Improving region identification models to enhance the performance of the model, as the current model is only applied to tissue from the tumor regions in whole slide images .
  • Validating the performance of the model on data from additional clinical trials that include anti-angiogenic therapy arms to ensure its effectiveness across different treatment scenarios .
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