"AI-Based Tool Offers Exciting Advancement in Pancreatic Cancer Diagnostics," Researchers Find

Researchers have successfully developed a deep learning model that classifies pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, into molecular subtypes using histopathology images. This approach achieves high accuracy and offers a rapid, cost-effective alternative to current methods that rely on expensive molecular assays. The new study 打開新的分頁／視窗 in The American Journal of Pathology 打開新的分頁／視窗, published by Elsevier, holds promise to advance personalized treatment strategies and improve patient outcomes.

PDACs have recently surpassed breast cancer as the third leading cause of cancer mortality in Canada and the United States. Surgery can cure approximately one-fifth of PDAC cases if they are detected early. Although surgical intervention is provided to these patients, the five-year survival rate remains at 20%. Approximately 80% of patients have already developed metastatic disease at diagnosis, and most of these patients succumb to the disease within a year.

The aggressiveness of PDAC poses a formidable challenge when using sequencing technologies to determine a patient care plan. The disease’s rapid clinical deterioration demands swift action to identify eligible individuals for targeted therapies and inclusion in clinical trials. However, current turnaround times for molecular profiling, which range from 19 to 52 days from the time of biopsy, fall short of meeting these time-sensitive demands.

Co-lead investigator David Schaeffer, MD, Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver General Hospital, and Pancreas Centre BC, explains, "More and more potentially actionable subtypes to personalize treatment for pancreatic cancer patients are being discovered. However, the subtyping is still entirely based on genomic methodology based on DNA and RNA extracted from tissue. This methodology is outstanding if sufficient tissue is present, which is not always the case for PDAC tumors given the difficult anatomical location of this organ. Our study provides a promising method to cost-effectively and rapidly classify PDAC molecular subtypes based on routine hematoxylin-eosin–stained slides, potentially leading to more effective clinical management of this disease."

The study involved training deep learning AI models on whole-slide pathology images to identify the molecular subtypes of PDAC—basal-like and classical—using hematoxylin and eosin-(H&E) stained slides. H&E staining is a cost-effective and widely available technique that is routinely performed with fast turnaround times in pathology laboratories for diagnostics and prognostication. The models were trained on 97 slides from The Cancer Genome Atlas (TCGA) and tested on 110 slides from 44 patients in a local cohort. The best-performing model achieved an accuracy of 96.19% in identifying the classical and basal subtypes in the TCGA dataset and 83.03% on the local cohort, highlighting its robustness across different datasets.

Co-lead investigator Ali Bashashati, PhD, School of Biomedical Engineering, and Department of Pathology and Laboratory Medicine, University of British Columbia, notes, "The sensitivity and specificity of the model was 85% and 100%, respectively, making this AI tool a highly applicable tool for triaging patients for molecular testing. Also, the main achievement of this study is the fact that the AI model was able to detect the subtypes from biopsy images, making it a highly useful tool that can be deployed at the time of diagnosis."

Dr. Bashashati concludes, "This AI-based approach offers an exciting advancement in pancreatic cancer diagnostics, enabling us to identify key molecular subtypes rapidly and cost-effectively.”

Notes for editors

The article is “A Deep Learning Approach for the Identification of the Molecular Subtypes of Pancreatic Ductal Adenocarcinoma Based on Whole Slide Pathology Images,” by Pouya Ahmadvand, Hossein Farahani, David Farnell, Amirali Darbandsari, James Topham, Joanna Karasinska, Jessica Nelson, Julia Naso, Steven J.M. Jones, Daniel Renouf, David F. Schaeffer, and Ali Bashashati (https://doi.org/10.1016/j.ajpath.2024.08.006 打開新的分頁／視窗). It appears in The American Journal of Pathology, volume 194, issue 12 (December 2024), published by Elsevier 打開新的分頁／視窗.

The article is openly available at https://ajp.amjpathol.org/article/S0002-9440(24)00325-0/fulltext 打開新的分頁／視窗.

Full text of the article is also available to credentialed journalists upon request. Contact Eileen Leahy at +1 732 406 1313 or [email protected] 打開新的分頁／視窗 to request a PDF of the article or more information. To reach the study’s authors contact David Schaeffer, MD, at [email protected] 打開新的分頁／視窗, or Ali Bashashati, PhD, at [email protected] 打開新的分頁／視窗.

The study was supported by the Canadian Institutes of Health Research number 418734; the Natural Sciences and Engineering Research Council of Canada RGPIN-2019-04896; the Michael Smith Foundation for Health Research; the BC (British Columbia) Cancer Foundation; and the VGH (Vancouver General Hospital) and UBC (University of British Columbia) Hospital Foundation.

About The American Journal of Pathology

The American Journal of Pathology 打開新的分頁／視窗, official journal of the American Society for Investigative Pathology 打開新的分頁／視窗, published by Elsevier, seeks high-quality original research reports, reviews, and commentaries related to the molecular and cellular basis of disease. The editors will consider basic, translational, and clinical investigations that directly address mechanisms of pathogenesis or provide a foundation for future mechanistic inquiries. Examples of such foundational investigations include data mining, identification of biomarkers, molecular pathology, and discovery research. High priority is given to studies of human disease and relevant experimental models using molecular, cellular, and organismal approaches. https://ajp.amjpathol.org 打開新的分頁／視窗

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