PathAI to Present Machine Learning-based Quality Control Tool for HER2 Testing in Breast Cancer at the American Society of Clinical Oncology Virtual Scientific Program 2021

Rhea-AI Summary

PathAI announced its participation in the ASCO Virtual Scientific Program 2021, presenting new data on a quality control tool for HER2 testing in digital pathology images. The presentation includes the poster Machine learning models to quantify HER2 for real-time tissue image analysis in prospective clinical trials (Abstract #3061). Together with AstraZeneca (AZN) and Daiichi Sankyo, PathAI developed ML-based models demonstrating high accuracy in HER2 scoring, consistent with ASCO/CAP guidelines, enhancing testing quality in breast cancer clinical trials.

BOSTON, June 8, 2021 /PRNewswire/ -- PathAI, a global leader of AI-powered technology applied to pathology, today announced that new data highlighting a quality control tool for HER2 testing in digital pathology images captured in clinical trials will be presented in the American Society of Clinical Oncology (ASCO) Virtual Scientific Program 2021, held from June 4-8, 2021. These results will be shared in the poster presentation, Machine learning models to quantify HER2 for real-time tissue image analysis in prospective clinical trials (Abstract #3061), in the session, Developmental Therapeutics —Molecularly Targeted Agents and Tumor Biology.

Together, PathAI, AstraZeneca (LSE/STO/Nasdaq: AZN)  and Daiichi Sankyo Company, Limited have developed ML-based models for the automated quantification of HER2 IHC images in breast cancer tissue. Expression of HER2, a protein localized in the cell membrane, is typically assessed by pathologists to evaluate patient eligibility for anti-HER2 targeted therapies. ML-based models trained to identify and quantify tumor histology features can provide highly accurate and reproducible scores that are highly concordant with manual pathology.

The PathAI HER2 models were developed to generate HER2 scores consistent with the 2018 ASCO/CAP HER2 scoring guidelines. The models also produce metrics that reflect the quality of HER2 testing, such as the area and number of tumor cells, the presence of ductal carcinoma in situ (DCIS), background staining and artifact content. In a test set including diverse tissue-types across a wide range of breast cancer types, ML quantification of HER2 was consistent with manual scores from a consensus of pathologists (ICC 0.88, 95% CI 0.82-0.92). ML scores were even more closely aligned with pathologist scores after further training to learn pathologist scoring methods (ICC 0.91, 95% CI 0.89-0.94). By providing consistent, automated HER2 IHC image analysis, PathAI ML models can provide real-time QC read-outs enabling identification of drifts or inconsistencies in HER2 testing data and images captured during clinical trials.

PathAI's broad approach towards integrating AI-powered tools into oncology clinical trial workflows is also represented by a separate study that PathAI is presenting at ASCO (Abstract #106). Both presentations are examples of how AI can enhance pathologist performance by generating accurate and reproducible clinically relevant scores that can be scaled to levels that are currently unachievable.

About PathAI:
PathAI is a leading provider of AI-powered research tools and services for pathology. PathAI's platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine and deep learning. Based in Boston, PathAI works with leading life sciences companies and researchers to advance precision medicine. To learn more, visit pathai.com.

View original content to download multimedia:http://www.prnewswire.com/news-releases/pathai-to-present-machine-learning-based-quality-control-tool-for-her2-testing-in-breast-cancer-at-the-american-society-of-clinical-oncology-virtual-scientific-program-2021-301307942.html

SOURCE PathAI

FAQ

What is the focus of PathAI's presentation at ASCO 2021 regarding HER2 testing?

PathAI's presentation focuses on a machine learning-based quality control tool for HER2 testing in digital pathology images.

Who collaborated with PathAI on the HER2 IHC image quantification models?

PathAI collaborated with AstraZeneca (AZN) and Daiichi Sankyo to develop the HER2 IHC image quantification models.

How accurate are the machine learning models for HER2 testing developed by PathAI?

The machine learning models achieved an ICC of 0.88 initially and 0.91 after further training, reflecting high concordance with manual pathology scores.

What guidelines do PathAI's HER2 models adhere to?

PathAI's HER2 models are consistent with the 2018 ASCO/CAP HER2 scoring guidelines.

Where can I find more information on PathAI's research tools?

More information on PathAI's research tools can be found at their website, pathai.com.