Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study

Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who might benefit from intensive treatment.
Authors developed a multitask deep learning model for the simultaneous prediction of peritoneal recurrence and disease-free survival using preoperative CT images. When informed by the artificial intelligence (AI) model, clinician performance was significantly enhanced for predicting peritoneal recurrence. Additionally, the AI model was able to identify which patients with stage II and stage III gastric cancer were most likely to benefit from adjuvant chemotherapy.