Predicting response to therapy in pancreatic ductal adenocarcinoma using convolutional neural networks

Pancreatic cancer is a uniformly deadly disease. Prediction of response to neoadjuvant therapy is critical in determining which patients should undergo invasive surgery. Non-invasive biomarkers of response would address gaps in the management of patients. This study employs pre-trained convolutional neural networks (CNNs) to predict response from baseline computed tomography (CT) scans alone, prior to neoadjuvant therapy. ResNet50, InceptionV3, VGG16, and Xception were trained on a dataset of annotated CT scans of patients with pancreatic ductal adenocarcinoma (PDAC). Our results demonstrate that the ResNet50 model achieves the highest performance among the models predicting response, with an average (average ± margin of error at 95% confidence level) accuracy of 0.679 ± 0.057, an F1-score of 0.665 ± 0.072, recall of 0.717 ± 0.081, precision of 0.698 ± 0.074, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.781 ± 0.162 across 5-fold cross-validation. These findings highlight the potential for non-invasive imaging biomarkers in predicting response to neoadjuvant therapy in PDAC.