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.
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SOURCE PathAI