Development and Validation of Fully Automatic Deep Learning-Based Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal Carcinoma

Sumit Kumar Jha, Purnendu Mishra, Shubham Mathur, Gursewak Singh, Rajiv Kumar, Kiran Aatre, Suraj Rengarajan·June 16, 2024

Summary

This paper presents a deep learning-based decision support system (DSS) for automatic immunohistochemistry (IHC) scoring of invasive ductal carcinoma in breast cancer. The system, trained on annotated image patches and cell annotations, achieves high accuracy (Ki67: 94%, HER2: 92%, ER: 88%, PR: 82%) across multiple scanners and centers, improving reproducibility and reducing subjectivity. It uses semantic segmentation and custom models like Mask-RCNN2, CNNs, and ResNet50 for tumor detection, nuclei segmentation, and stain classification, outperforming traditional methods. The study highlights the model's competitive performance with pathologists, with some cases leading to revised scores in favor of the automated system. It addresses the need for efficient and accurate IHC analysis by proposing a modular approach adaptable to other cancer types. Key findings include: 1. A novel segmentation model for IHC scoring, reducing inter-observer variability and adapting to staining variations. 2. Multi-centric trials demonstrating superior accuracy compared to alternative methods, particularly in grading and marker scoring. 3. Evaluation of different approaches for nuclei detection and stain classification, with A2 (CMYK-based) preferred for improved accuracy and efficiency. 4. AI-based systems for grading and Allred/proliferation score estimation, assisting pathologists and streamlining diagnosis. The research also discusses challenges, such as misclassification between stain categories and the impact of artifacts on accuracy. Overall, the study contributes to the advancement of automated IHC analysis in breast cancer diagnosis, with potential for broader application in histopathology.

Key findings

18

Paper digest

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

The paper aims to address the challenge of accurately assessing IHC-stained tissue samples in breast cancer diagnosis through the development and validation of fully automatic deep learning-based algorithms . This problem is not new, as manual interpretation of IHC stains is known to be laborious, time-consuming, and subject to inter-observer variability, especially with the increasing number of breast cancer cases and the limited availability of pathologists . The paper proposes a novel semantic segmentation-based training model to improve scoring accuracy and robustness in breast cancer IHC analysis, demonstrating superior performance compared to conventional methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that the developed deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma can accurately predict HER2 scores for each region of ROI and the entire slide using K-fold cross-validation with a target Kappa Score (Quadratic) . The study focuses on training a Random Forest model on whole-slide images (WSI) data to predict HER2 scores and validate the model's performance against other WSI images . The algorithms are designed to provide accurate predictions for HER2 scores based on the analysis of stained tissue regions and nuclei segmentation . The performance baseline set for the algorithm on validation slides was 85 +/- 5%, and the nuclei segmentation and classification algorithm successfully met this performance baseline .


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

The paper proposes several innovative ideas, methods, and models for the automatic reporting of invasive breast ductal carcinoma using deep learning-based algorithms :

  • Deep Learning-Based Algorithms: The study involves the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma. These algorithms utilize advanced techniques such as semantic segmentation for membrane and nuclei detection .
  • Model Architectures: The paper introduces various model architectures such as EfficientNet-UNET for semantic segmentation in HER2 image patches. This model combines EfficientNet-B4 architecture with the UNet architecture to accurately segment membrane and nuclei structures in HER2 images .
  • Training Data and Cyclic Training: The models are trained using de-identified, digitized whole-slide images of IHC breast tissue obtained from multiple hospitals/laboratories. The training data includes manually annotated patches from expert pathologists. The second model is trained using predictions from the first model in a cyclic manner, with six cycles of training performed .
  • Post-Processing Modules: To address scoring issues caused by artifacts and defects in tissue, the algorithms incorporate post-processing modules. One approach involves clustering tumorous nuclei based on their spatial location in tissue to improve performance .
  • Performance Evaluation: The paper evaluates the performance of the models by comparing predicted results with ground-truth images. The accuracy and F1 scores for nuclei detection are reported to be in the range of 75-85%, indicating satisfactory model performance .
  • Feature Extraction and Classification: The models extract morphological features at each region of interest (ROI) level and for the entire slide to classify HER2 scores. Features such as color histogram, skewness of the histogram curve, and nuclei to membrane ratio are considered for classification .
  • Knowledge Learning: The study incorporates a teacher-student architecture for knowledge learning, which involves a survey on this approach to enhance the model's learning capabilities .
  • Data Availability: The datasets used in the study were collected through internal agreements with hospitals/laboratories. While the data is not publicly available due to data-sharing restrictions, it can be accessed from the authors upon reasonable requests and permissions .
  • Institute Collaboration: The study involves collaboration with multiple institutes for data collection and validation. The development and learning phase were conducted in collaboration with Kasturba Medical College (KMC), Manipal, and the validation phase included de-identified slides from various centers .

These innovative ideas, methods, and models collectively contribute to the advancement of automated immunohistochemistry reporting for invasive breast ductal carcinoma, aiming to improve diagnostic accuracy and efficiency in pathology analysis. The paper introduces a novel semantic segmentation-based training model for breast cancer IHC scoring, offering several characteristics and advantages compared to previous methods :

  • Training Approach: Unlike transfer learning methods that utilize pre-trained models, the proposed approach involves training a Convolutional Neural Network (CNN) from scratch specifically for IHC staining analysis. This training strategy aims to capture fine-grained details and spatial relationships within stained regions, enhancing scoring accuracy and robustness.
  • Performance Evaluation: The study conducted a rigorous multi-centric trial involving multiple institutions, laboratories, and pathologists to evaluate the model's performance and robustness. The results were compared against manual scoring by experienced pathologists, demonstrating the superiority of the semantic segmentation-based training model over conventional image processing techniques, SVM/RF-based approaches, and transfer learning methods.
  • Accuracy and Reliability: The proposed model achieved higher accuracy, reduced inter-observer variability, and better adaptability to variations in staining patterns, slide preparation techniques, and scanner characteristics. This indicates improved accuracy and robustness in breast cancer diagnosis and treatment decision-making.
  • Generalizability: The multi-centric trial ensured the algorithm's reliability and generalizability across different settings, showcasing its potential for widespread clinical implementation in breast cancer diagnosis.
  • Model Stability: The model's stability and robustness were validated on the test set, with an Area Under the Curve (AUC) of 0.85 for tumors and 0.92 for other classes, indicating well-ranked predictions and good prediction quality.
  • Data Availability: While the datasets used in the study are not publicly available due to data-sharing restrictions, they can be accessed from the authors upon reasonable requests and permissions, ensuring transparency and reproducibility in research .

Overall, the semantic segmentation-based training model offers improved accuracy, reliability, and generalizability in breast cancer IHC scoring compared to conventional methods, showcasing its potential for enhancing diagnostic outcomes in pathology analysis.


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 researches exist in the field of developing and validating deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma. Noteworthy researchers in this field include S.J., A.M., G.S., S.M., R.K., Dr. Kanthilatha Pai, Dr. Brij Mohan Kumar Singh, Dr. Anil Betigeri, Dr. Vani Verma, and Dr. Madhavi Pai . The key to the solution mentioned in the paper involves the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma. The study involved the acquisition of de-identified, digitized whole-slide images of IHC breast tissue for ER, PR, HER2, and Ki67 from multiple hospitals/laboratories, followed by the training and validation of AI-based decision support systems for IHC breast tissue analysis . The algorithms developed in the study demonstrated high accuracy and performance in nuclei detection, stain classification, and clinical evaluation for ER, PR, and Ki67 markers in invasive ductal breast carcinoma grading .


How were the experiments in the paper designed?

The experiments in the paper were designed in a systematic manner involving two main phases:

  • Phase 1: Development and Learning Phase:
    • This phase was conducted in collaboration with Kasturba Medical College (KMC), Manipal.
    • Retrospective samples previously prepared by KMC, Manipal were collected for research purposes.
    • Approximately 238 slides each of ER, PR, HER2, and Ki67 were de-identified and converted to digital TIFF images.
    • The Institutional Ethics Committee approved the use of these samples.
    • A total of 920 slides were available for phase 1 after rejecting 32 slides due to poor quality.
  • Phase 2: Multi-centric Validation Study Phase:
    • The developed DSS was validated on de-identified slides from four different centers: KMC (Manipal), KMC (Mangalore), Sikkim Manipal University (SMU), and Neuberg Anand Labs (NAALM).
    • About 569 cases were targeted in this study, with 663 cases received in total.
    • 94 cases were discarded due to bad slide quality.
    • All samples were anonymized and the study was approved by each institute's Ethical Committee.

The experiments involved the validation of the developed algorithms on a diverse set of samples from multiple institutes, ensuring a robust evaluation of the deep learning-based models for immunohistochemistry reporting of invasive breast ductal carcinoma.


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

The dataset used for quantitative evaluation in the study on deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma consists of 152 manually annotated patches from two expert pathologists . The code for the algorithms developed in this study is not mentioned to be open source in the provided context .


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 to be verified. The study involved the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma . The algorithms were trained and validated using a dataset of whole-slide images (WSI) to predict HER2 scores for different regions of interest (ROI) and entire slides . The study utilized K-fold cross-validation with Kappa Score to assess the model's performance .

The paper details the methodology used for data acquisition, which involved obtaining de-identified, digitized whole-slide images of IHC breast tissue from multiple hospitals and laboratories for ER, PR, HER2, and Ki67 analysis . The study was conducted in two phases: a development and learning phase, followed by a multi-centric validation study phase . The experiments included the collection of retrospective samples, training the models, and validating them on de-identified slides from different centers .

The results of the experiments demonstrated the effectiveness of the deep learning-based algorithms in nuclei detection, stain classification, and overall performance evaluation . The algorithms showed high accuracy, precision, recall, and F1 scores for ER, PR, and Ki67 stained slides . The models achieved good agreement with ground truth scores and pathologists' annotations, indicating reliable performance . Additionally, the algorithms exhibited satisfactory performance in terms of nuclei segmentation and stain classification, meeting predefined performance baselines .

Overall, the experiments conducted in the study, along with the detailed results and performance metrics, provide robust evidence supporting the scientific hypotheses and the effectiveness of the deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma.


What are the contributions of this paper?

The paper acknowledges the contributions of various individuals and entities:

  • The management at Applied Materials supported the work in this domain .
  • Pathologists like Dr. Kanthilatha Pai, Dr. Brij Mohan Kumar Singh, Dr. Anil Betigeri, Dr. Vani Verma, and Dr. Madhavi Pai provided IHC scores annotations for the study .
  • Authors S.J., A.M., G.S., S.M., and R.K. were involved in planning, designing experiments, data acquisition, code writing, validation, results collection, and performance analysis .
  • The study involved collaboration with Kasturba Medical College (KMC), Manipal, for the collection of retrospective samples and the development of a diagnostic solution .
  • The paper highlights the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma .
  • The AI-based algorithm aimed to assist pathologists in well-informed and evidence-based reporting, improving the overall turn-around time for better patient outcomes .

What work can be continued in depth?

To further enhance the model's performance in accurately segmenting membrane and nuclei structures in HER2 images, further work can be done to maximize the Intersection over Union (IoU) values for membrane and nucleus detection. Currently, the IoU values obtained are 38.75% for membrane detection and 57.26% for nucleus detection . By conducting additional experiments and optimizations, these values can be improved to achieve more precise segmentation results .

Moreover, the model can be refined by introducing more non-linear activation-based depth in the UNet model to enhance the understanding of features and make the model more generic, especially in dealing with variations in color and width of the membrane and visibility of nuclei in the image . This approach can help the model better capture the intricate details of the membrane and nuclei structures, leading to improved segmentation accuracy.

Additionally, to address challenges related to ROI segmentation, a generic multi-level multi-tissue segmentation model inspired by the Hook-Net architecture can be further developed. This model should work effectively with all four stain types (ER, PR, Ki67, and HER2) and be robust across multiple data sources and scanners, ensuring accurate classification of different tissue regions . By refining this segmentation model, the algorithm can achieve more precise and reliable segmentation of various tissue structures in breast tissue images.


Introduction
Background
Evolution of IHC scoring in breast cancer diagnosis
Challenges in reproducibility and subjectivity
Objective
To develop and evaluate a DSS for automatic IHC scoring
Improve accuracy, reproducibility, and efficiency
Method
Data Collection
Annotated image patches and cell annotations
Multiple scanners and centers for cross-validation
Data Preprocessing
Image normalization and stain separation (A2 CMYK-based)
Model Architecture
Semantic Segmentation
Mask-RCNN2 for tumor detection
Nuclei Segmentation
Custom CNNs and ResNet50 for nuclei segmentation
Stain Classification
Custom models for accurate stain identification
Performance Evaluation
Accuracy metrics (Ki67, HER2, ER, PR)
Comparison with traditional methods and pathologists
Multi-Centric Trials
Adaptability to different scanners and centers
Impact on inter-observer variability
Key Findings
Novel segmentation model for IHC scoring
Variability reduction
Stain adaptation
Superior accuracy in grading and marker scoring
A2 CMYK-based nuclei detection and stain classification
AI-assisted grading and Allred/proliferation score estimation
Challenges and limitations (misclassification, artifacts)
Applications and Discussion
Streamlining breast cancer diagnosis
Potential for other cancer types
Future directions and research needs
Conclusion
Contribution to automated IHC analysis in breast cancer
Advancements in histopathology with AI technology
Basic info
papers
image and video processing
computer vision and pattern recognition
tissues and organs
quantitative methods
artificial intelligence
Advanced features
Insights
How does the study address the need for efficient and accurate IHC analysis, particularly in grading and marker scoring?
How accurate are the system's scores for Ki67, HER2, ER, and PR in invasive ductal carcinoma of the breast?
What is the main advantage of the proposed system compared to traditional methods in terms of IHC scoring reproducibility?
What is the primary focus of the deep learning-based decision support system (DSS) described in the paper?

Development and Validation of Fully Automatic Deep Learning-Based Algorithms for Immunohistochemistry Reporting of Invasive Breast Ductal Carcinoma

Sumit Kumar Jha, Purnendu Mishra, Shubham Mathur, Gursewak Singh, Rajiv Kumar, Kiran Aatre, Suraj Rengarajan·June 16, 2024

Summary

This paper presents a deep learning-based decision support system (DSS) for automatic immunohistochemistry (IHC) scoring of invasive ductal carcinoma in breast cancer. The system, trained on annotated image patches and cell annotations, achieves high accuracy (Ki67: 94%, HER2: 92%, ER: 88%, PR: 82%) across multiple scanners and centers, improving reproducibility and reducing subjectivity. It uses semantic segmentation and custom models like Mask-RCNN2, CNNs, and ResNet50 for tumor detection, nuclei segmentation, and stain classification, outperforming traditional methods. The study highlights the model's competitive performance with pathologists, with some cases leading to revised scores in favor of the automated system. It addresses the need for efficient and accurate IHC analysis by proposing a modular approach adaptable to other cancer types. Key findings include: 1. A novel segmentation model for IHC scoring, reducing inter-observer variability and adapting to staining variations. 2. Multi-centric trials demonstrating superior accuracy compared to alternative methods, particularly in grading and marker scoring. 3. Evaluation of different approaches for nuclei detection and stain classification, with A2 (CMYK-based) preferred for improved accuracy and efficiency. 4. AI-based systems for grading and Allred/proliferation score estimation, assisting pathologists and streamlining diagnosis. The research also discusses challenges, such as misclassification between stain categories and the impact of artifacts on accuracy. Overall, the study contributes to the advancement of automated IHC analysis in breast cancer diagnosis, with potential for broader application in histopathology.
Mind map
Custom models for accurate stain identification
Custom CNNs and ResNet50 for nuclei segmentation
Mask-RCNN2 for tumor detection
Challenges and limitations (misclassification, artifacts)
AI-assisted grading and Allred/proliferation score estimation
A2 CMYK-based nuclei detection and stain classification
Superior accuracy in grading and marker scoring
Stain adaptation
Variability reduction
Impact on inter-observer variability
Adaptability to different scanners and centers
Comparison with traditional methods and pathologists
Accuracy metrics (Ki67, HER2, ER, PR)
Stain Classification
Nuclei Segmentation
Semantic Segmentation
Image normalization and stain separation (A2 CMYK-based)
Multiple scanners and centers for cross-validation
Annotated image patches and cell annotations
Improve accuracy, reproducibility, and efficiency
To develop and evaluate a DSS for automatic IHC scoring
Challenges in reproducibility and subjectivity
Evolution of IHC scoring in breast cancer diagnosis
Advancements in histopathology with AI technology
Contribution to automated IHC analysis in breast cancer
Future directions and research needs
Potential for other cancer types
Streamlining breast cancer diagnosis
Novel segmentation model for IHC scoring
Multi-Centric Trials
Performance Evaluation
Model Architecture
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Applications and Discussion
Key Findings
Method
Introduction
Outline
Introduction
Background
Evolution of IHC scoring in breast cancer diagnosis
Challenges in reproducibility and subjectivity
Objective
To develop and evaluate a DSS for automatic IHC scoring
Improve accuracy, reproducibility, and efficiency
Method
Data Collection
Annotated image patches and cell annotations
Multiple scanners and centers for cross-validation
Data Preprocessing
Image normalization and stain separation (A2 CMYK-based)
Model Architecture
Semantic Segmentation
Mask-RCNN2 for tumor detection
Nuclei Segmentation
Custom CNNs and ResNet50 for nuclei segmentation
Stain Classification
Custom models for accurate stain identification
Performance Evaluation
Accuracy metrics (Ki67, HER2, ER, PR)
Comparison with traditional methods and pathologists
Multi-Centric Trials
Adaptability to different scanners and centers
Impact on inter-observer variability
Key Findings
Novel segmentation model for IHC scoring
Variability reduction
Stain adaptation
Superior accuracy in grading and marker scoring
A2 CMYK-based nuclei detection and stain classification
AI-assisted grading and Allred/proliferation score estimation
Challenges and limitations (misclassification, artifacts)
Applications and Discussion
Streamlining breast cancer diagnosis
Potential for other cancer types
Future directions and research needs
Conclusion
Contribution to automated IHC analysis in breast cancer
Advancements in histopathology with AI technology
Key findings
18

Paper digest

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

The paper aims to address the challenge of accurately assessing IHC-stained tissue samples in breast cancer diagnosis through the development and validation of fully automatic deep learning-based algorithms . This problem is not new, as manual interpretation of IHC stains is known to be laborious, time-consuming, and subject to inter-observer variability, especially with the increasing number of breast cancer cases and the limited availability of pathologists . The paper proposes a novel semantic segmentation-based training model to improve scoring accuracy and robustness in breast cancer IHC analysis, demonstrating superior performance compared to conventional methods .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that the developed deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma can accurately predict HER2 scores for each region of ROI and the entire slide using K-fold cross-validation with a target Kappa Score (Quadratic) . The study focuses on training a Random Forest model on whole-slide images (WSI) data to predict HER2 scores and validate the model's performance against other WSI images . The algorithms are designed to provide accurate predictions for HER2 scores based on the analysis of stained tissue regions and nuclei segmentation . The performance baseline set for the algorithm on validation slides was 85 +/- 5%, and the nuclei segmentation and classification algorithm successfully met this performance baseline .


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

The paper proposes several innovative ideas, methods, and models for the automatic reporting of invasive breast ductal carcinoma using deep learning-based algorithms :

  • Deep Learning-Based Algorithms: The study involves the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma. These algorithms utilize advanced techniques such as semantic segmentation for membrane and nuclei detection .
  • Model Architectures: The paper introduces various model architectures such as EfficientNet-UNET for semantic segmentation in HER2 image patches. This model combines EfficientNet-B4 architecture with the UNet architecture to accurately segment membrane and nuclei structures in HER2 images .
  • Training Data and Cyclic Training: The models are trained using de-identified, digitized whole-slide images of IHC breast tissue obtained from multiple hospitals/laboratories. The training data includes manually annotated patches from expert pathologists. The second model is trained using predictions from the first model in a cyclic manner, with six cycles of training performed .
  • Post-Processing Modules: To address scoring issues caused by artifacts and defects in tissue, the algorithms incorporate post-processing modules. One approach involves clustering tumorous nuclei based on their spatial location in tissue to improve performance .
  • Performance Evaluation: The paper evaluates the performance of the models by comparing predicted results with ground-truth images. The accuracy and F1 scores for nuclei detection are reported to be in the range of 75-85%, indicating satisfactory model performance .
  • Feature Extraction and Classification: The models extract morphological features at each region of interest (ROI) level and for the entire slide to classify HER2 scores. Features such as color histogram, skewness of the histogram curve, and nuclei to membrane ratio are considered for classification .
  • Knowledge Learning: The study incorporates a teacher-student architecture for knowledge learning, which involves a survey on this approach to enhance the model's learning capabilities .
  • Data Availability: The datasets used in the study were collected through internal agreements with hospitals/laboratories. While the data is not publicly available due to data-sharing restrictions, it can be accessed from the authors upon reasonable requests and permissions .
  • Institute Collaboration: The study involves collaboration with multiple institutes for data collection and validation. The development and learning phase were conducted in collaboration with Kasturba Medical College (KMC), Manipal, and the validation phase included de-identified slides from various centers .

These innovative ideas, methods, and models collectively contribute to the advancement of automated immunohistochemistry reporting for invasive breast ductal carcinoma, aiming to improve diagnostic accuracy and efficiency in pathology analysis. The paper introduces a novel semantic segmentation-based training model for breast cancer IHC scoring, offering several characteristics and advantages compared to previous methods :

  • Training Approach: Unlike transfer learning methods that utilize pre-trained models, the proposed approach involves training a Convolutional Neural Network (CNN) from scratch specifically for IHC staining analysis. This training strategy aims to capture fine-grained details and spatial relationships within stained regions, enhancing scoring accuracy and robustness.
  • Performance Evaluation: The study conducted a rigorous multi-centric trial involving multiple institutions, laboratories, and pathologists to evaluate the model's performance and robustness. The results were compared against manual scoring by experienced pathologists, demonstrating the superiority of the semantic segmentation-based training model over conventional image processing techniques, SVM/RF-based approaches, and transfer learning methods.
  • Accuracy and Reliability: The proposed model achieved higher accuracy, reduced inter-observer variability, and better adaptability to variations in staining patterns, slide preparation techniques, and scanner characteristics. This indicates improved accuracy and robustness in breast cancer diagnosis and treatment decision-making.
  • Generalizability: The multi-centric trial ensured the algorithm's reliability and generalizability across different settings, showcasing its potential for widespread clinical implementation in breast cancer diagnosis.
  • Model Stability: The model's stability and robustness were validated on the test set, with an Area Under the Curve (AUC) of 0.85 for tumors and 0.92 for other classes, indicating well-ranked predictions and good prediction quality.
  • Data Availability: While the datasets used in the study are not publicly available due to data-sharing restrictions, they can be accessed from the authors upon reasonable requests and permissions, ensuring transparency and reproducibility in research .

Overall, the semantic segmentation-based training model offers improved accuracy, reliability, and generalizability in breast cancer IHC scoring compared to conventional methods, showcasing its potential for enhancing diagnostic outcomes in pathology analysis.


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 researches exist in the field of developing and validating deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma. Noteworthy researchers in this field include S.J., A.M., G.S., S.M., R.K., Dr. Kanthilatha Pai, Dr. Brij Mohan Kumar Singh, Dr. Anil Betigeri, Dr. Vani Verma, and Dr. Madhavi Pai . The key to the solution mentioned in the paper involves the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma. The study involved the acquisition of de-identified, digitized whole-slide images of IHC breast tissue for ER, PR, HER2, and Ki67 from multiple hospitals/laboratories, followed by the training and validation of AI-based decision support systems for IHC breast tissue analysis . The algorithms developed in the study demonstrated high accuracy and performance in nuclei detection, stain classification, and clinical evaluation for ER, PR, and Ki67 markers in invasive ductal breast carcinoma grading .


How were the experiments in the paper designed?

The experiments in the paper were designed in a systematic manner involving two main phases:

  • Phase 1: Development and Learning Phase:
    • This phase was conducted in collaboration with Kasturba Medical College (KMC), Manipal.
    • Retrospective samples previously prepared by KMC, Manipal were collected for research purposes.
    • Approximately 238 slides each of ER, PR, HER2, and Ki67 were de-identified and converted to digital TIFF images.
    • The Institutional Ethics Committee approved the use of these samples.
    • A total of 920 slides were available for phase 1 after rejecting 32 slides due to poor quality.
  • Phase 2: Multi-centric Validation Study Phase:
    • The developed DSS was validated on de-identified slides from four different centers: KMC (Manipal), KMC (Mangalore), Sikkim Manipal University (SMU), and Neuberg Anand Labs (NAALM).
    • About 569 cases were targeted in this study, with 663 cases received in total.
    • 94 cases were discarded due to bad slide quality.
    • All samples were anonymized and the study was approved by each institute's Ethical Committee.

The experiments involved the validation of the developed algorithms on a diverse set of samples from multiple institutes, ensuring a robust evaluation of the deep learning-based models for immunohistochemistry reporting of invasive breast ductal carcinoma.


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

The dataset used for quantitative evaluation in the study on deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma consists of 152 manually annotated patches from two expert pathologists . The code for the algorithms developed in this study is not mentioned to be open source in the provided context .


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 to be verified. The study involved the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma . The algorithms were trained and validated using a dataset of whole-slide images (WSI) to predict HER2 scores for different regions of interest (ROI) and entire slides . The study utilized K-fold cross-validation with Kappa Score to assess the model's performance .

The paper details the methodology used for data acquisition, which involved obtaining de-identified, digitized whole-slide images of IHC breast tissue from multiple hospitals and laboratories for ER, PR, HER2, and Ki67 analysis . The study was conducted in two phases: a development and learning phase, followed by a multi-centric validation study phase . The experiments included the collection of retrospective samples, training the models, and validating them on de-identified slides from different centers .

The results of the experiments demonstrated the effectiveness of the deep learning-based algorithms in nuclei detection, stain classification, and overall performance evaluation . The algorithms showed high accuracy, precision, recall, and F1 scores for ER, PR, and Ki67 stained slides . The models achieved good agreement with ground truth scores and pathologists' annotations, indicating reliable performance . Additionally, the algorithms exhibited satisfactory performance in terms of nuclei segmentation and stain classification, meeting predefined performance baselines .

Overall, the experiments conducted in the study, along with the detailed results and performance metrics, provide robust evidence supporting the scientific hypotheses and the effectiveness of the deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma.


What are the contributions of this paper?

The paper acknowledges the contributions of various individuals and entities:

  • The management at Applied Materials supported the work in this domain .
  • Pathologists like Dr. Kanthilatha Pai, Dr. Brij Mohan Kumar Singh, Dr. Anil Betigeri, Dr. Vani Verma, and Dr. Madhavi Pai provided IHC scores annotations for the study .
  • Authors S.J., A.M., G.S., S.M., and R.K. were involved in planning, designing experiments, data acquisition, code writing, validation, results collection, and performance analysis .
  • The study involved collaboration with Kasturba Medical College (KMC), Manipal, for the collection of retrospective samples and the development of a diagnostic solution .
  • The paper highlights the development and validation of fully automatic deep learning-based algorithms for immunohistochemistry reporting of invasive breast ductal carcinoma .
  • The AI-based algorithm aimed to assist pathologists in well-informed and evidence-based reporting, improving the overall turn-around time for better patient outcomes .

What work can be continued in depth?

To further enhance the model's performance in accurately segmenting membrane and nuclei structures in HER2 images, further work can be done to maximize the Intersection over Union (IoU) values for membrane and nucleus detection. Currently, the IoU values obtained are 38.75% for membrane detection and 57.26% for nucleus detection . By conducting additional experiments and optimizations, these values can be improved to achieve more precise segmentation results .

Moreover, the model can be refined by introducing more non-linear activation-based depth in the UNet model to enhance the understanding of features and make the model more generic, especially in dealing with variations in color and width of the membrane and visibility of nuclei in the image . This approach can help the model better capture the intricate details of the membrane and nuclei structures, leading to improved segmentation accuracy.

Additionally, to address challenges related to ROI segmentation, a generic multi-level multi-tissue segmentation model inspired by the Hook-Net architecture can be further developed. This model should work effectively with all four stain types (ER, PR, Ki67, and HER2) and be robust across multiple data sources and scanners, ensuring accurate classification of different tissue regions . By refining this segmentation model, the algorithm can achieve more precise and reliable segmentation of various tissue structures in breast tissue images.

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