Development of an artificial intelligence-derived histologic signature associated with adjuvant gemcitabine treatment outcomes in pancreatic cancer

• DOI: 10.1016/j.xcrm.2023.101013
• Publication Date: 2023-04-11T14:42:12Z
• Authors: Vivek Nimgaonkar; Viswesh Krishna; Vrishab Krishna; Ekin Tiu; Anirudh Joshi; Damir Vrabac; Hriday Bhambhvani; Katelyn Smith; Julia S. Johansen; Shalini Makawita; Benjamin Musher; Arnav Mehta; Andrew Hendifar; Zev Wainberg; Davendra Sohal; Christos Fountzilas; Aatur Singhi; Pranav Rajpurkar; Eric A. Collisson
• Keywords: screening and diagnosis; Carcinoma; pancreatic cancer; 610; Gemcitabine; Deoxycytidine; 3. Good health; 4.1 Discovery and preclinical testing of markers and technologies; Pancreatic Neoplasms; Pancreatic Cancer; Detection; Rare Diseases; Orphan Drug; Good Health and Well Being; Treatment Outcome; Pancreatic Ductal; Artificial Intelligence; Report; Humans; Digestive Diseases; digital pathology; predictive biomarker; Biomarkers; Cancer; Carcinoma, Pancreatic Ductal

Pancreatic ductal adenocarcinoma (PDAC) has been left behind in the evolution of personalized medicine. Predictive markers of response to therapy are lacking in PDAC despite various histological and transcriptional classification schemes. We report an artificial intelligence (AI) approach to histologic feature examination that extracts a signature predictive of disease-specific survival (DSS) in patients with PDAC receiving adjuvant gemcitabine. We demonstrate that this AI-generated histologic signature is associated with outcomes following adjuvant gemcitabine, while three previously developed transcriptomic classification systems are not (n = 47). We externally validate this signature in an independent cohort of patients treated with adjuvant gemcitabine (n = 46). Finally, we demonstrate that the signature does not stratify survival outcomes in a third cohort of untreated patients (n = 161), suggesting that the signature is specifically predictive of treatment-related outcomes but is not generally prognostic. This imaging analysis pipeline has promise in the development of actionable markers in other clinical settings where few biomarkers currently exist.