Development of a Deep Learning System for Intra-Operative Identification of Cancer Metastases

Objective: The aim of this study was to develop and test a prototype of a deep learning surgical guidance system (CASL) that can intra-operative identify peritoneal surface metastases on routine laparoscopy images. Background: For a number of cancer patients, operative resection with curative intent can end up in early recurrence of the cancer. Surgeons misidentifying visible peritoneal surface metastases is likely a common reason. Methods: CASL was developed and tested using staging laparoscopy images recorded from 132 patients with histologically-confirmed adenocarcinoma involving the gastrointestinal tract. The data included images depicting 4287 visible peritoneal surface lesions and 3650 image patches of 365 biopsied peritoneal surface lesions. The prototype’s diagnostic performance was compared to results from a national survey evaluating 111 oncologic surgeons in a simulated clinical environment. Results: In a simulated environment, surgeons’ accuracy of correctly recommending a biopsy for metastases while omitting a biopsy for benign lesions was only 52%. In this environment, the prototype of a deep learning surgical guidance system demonstrated improved performance in identifying peritoneal surface metastases compared to oncologic surgeons with an area under the receiver operating characteristic curve of 0.69 (oncologic surgeon) versus 0.78 (CASL) versus 0.79 (human-computer combined). A proposed model would have improved the identification of metastases by 5% while reducing the number of unnecessary biopsies by 28% compared to current standard practice. Conclusions: Our findings demonstrate a pathway for an artificial intelligence system for intra-operative identification of peritoneal surface metastases, but still requires additional development and future validation in a multi-institutional clinical setting.