Development of a Deep Learning System for Intra-Operative Identification
  of Cancer Metastases
Thomas Schnelldorfer Janil Castro Atoussa Goldar-Najafi Liping Liu
For several cancer patients, operative resection with curative intent can end
up in early recurrence of the cancer. Current limitations in peri-operative
cancer staging and especially intra-operative misidentification of visible
metastases is likely the main reason leading to unnecessary operative
interventions in the affected individuals. Here, we evaluate whether an
artificial intelligence (AI) system can improve recognition of peritoneal
surface metastases on routine staging laparoscopy images from patients with
gastrointestinal malignancies. In a simulated setting evaluating biopsied
peritoneal lesions, a prototype deep learning surgical guidance system
outperformed oncologic surgeons in identifying peritoneal surface metastases.
In this environment the developed AI model would have improved the
identification of metastases by 5% while reducing the number of unnecessary
biopsies by 28% compared to current standard practice. Evaluating non-biopsied
peritoneal lesions, the findings support the possibility that the AI system
could identify peritoneal surface metastases that were falsely deemed benign in
clinical practice. Our findings demonstrate the technical feasibility of an AI
system for intra-operative identification of peritoneal surface metastases, but
require future assessment in a multi-institutional clinical setting.