Related Experiment Video
Updated: Nov 23, 2025
Ultrasound-Guided Orthotopic Implantation of Murine Pancreatic Ductal Adenocarcinoma
Published on: November 19, 2019
Pure and Hybrid Deep Learning Models can Predict Pathologic Tumor Response to Neoadjuvant Therapy in Pancreatic
Michael D Watson1, Maria R Baimas-George1, Keith J Murphy1
1Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, 2351Atrium Health, Charlotte, NC, USA.
Background:
Neoadjuvant therapy may improve survival of patients with pancreatic adenocarcinoma; however, determining response to therapy is difficult. Artificial intelligence allows for novel analysis of images. We hypothesized that a deep learning model can predict tumor response to NAC.
Methods:
Patients with pancreatic cancer receiving neoadjuvant therapy prior to pancreatoduodenectomy were identified between November 2009 and January 2018. The College of American Pathologists Tumor Regression Grades 0-2 were defined as pathologic response (PR) and grade 3 as no response (NR). Axial images from preoperative computed tomography scans were used to create a 5-layer convolutional neural network and LeNet deep learning model to predict PRs. The hybrid model incorporated decrease in carbohydrate antigen 19-9 (CA19-9) of 10%. Accuracy was determined by area under the curve.
Results:
A total of 81 patients were included in the study. Patients were divided between PR (333 images) and NR (443 images). The pure model had an area under the curve (AUC) of .738 (P < .001), whereas the hybrid model had an AUC of .785 (P < .001). CA19-9 decrease alone was a poor predictor of response with an AUC of .564 (P = .096).
Conclusions:
A deep learning model can predict pathologic tumor response to neoadjuvant therapy for patients with pancreatic adenocarcinoma and the model is improved with the incorporation of decreases in serum CA19-9. Further model development is needed before clinical application.
