Deep Learning Radio-clinical Signatures for Predicting Neoadjuvant Chemotherapy Response and Prognosis From Pretreatment CT Images of LAGC Patients
Deep Learning Radio-clinical Signatures for Predicting Neoadjuvant Chemotherapy Response and Prognosis From Pretreatment CT Images of LAGC Patients
The early noninvasive screening of patients suitable for neoadjuvant chemotherapy (NCT) is essential for personalized treatment in locally advanced gastric cancer (LAGC). The aim of this study was to develop and visualized a radio-clinical biomarker from pretreatment oversampled CT images to predict the response and prognosis to NCT in LAGC patients.
Inclusion Criteria:
1) patients with GC/EGJC confirmed by pathological examination; 2) patients who underwent D2 lymphadenectomy; 3) patients who received at least two cycles of preoperative chemotherapy; 4) patients with negative resection margins; and 5) patients with complete CT image data and clinical data.
Exclusion Criteria:
1) patients unable to undergo D2 radical gastrectomy after neoadjuvant therapy; and 2) patients with incomplete CT images and clinical data.