Predicting Chemotherapy Response from Staging Laparoscopy Images

Background: For patients with metastatic gastrointestinal cancers, chemotherapy resistance is a common phenomenon that, if known in advance, would allow for individualized treatment decisions. This study aimed to test the feasibility of developing a deep learning computer vision system that uses laparoscopy images depicting peritoneal surface metastases (i.e., capturing the in-vivo optical appearance of metastases as a summary of their molecular makeup) to predict whether a patient is resistant to standard chemotherapy. Methods: The retrospective observational feasibility study included 35 adult patients who underwent staging laparoscopy for non-colon gastrointestinal adenocarcinoma with biopsy-confirmed peritoneal surface metastases and who underwent chemotherapy as their only treatment modality. Chemotherapy resistance was determined based on each patient's observed cancer-specific survival after controlling for confounders. Results: Of 35 patients, 17 were assigned to the chemotherapy sensitive group and 18 to the chemotherapy resistant group. The study cohort provided 1010 laparoscopy image patches of 101 biopsy-confirmed metastases. A densely connected convolutional neural network with cross-validation provided the best results for correctly predicting chemotherapy resistance at the patient level (accuracy 0.80 (95%CI 0.63-0.92), sensitivity 0.72, specificity 0.88, AUC-ROC 0.78). Saliency maps demonstrated the system's trustworthiness. Conclusion: In this study, a prototype surgical computer vision system designed to determine chemotherapy resistance from operative images of peritoneal surface metastases demonstrated its technical feasibility. Further development and validation in a multi-institutional clinical study are pending.