Longitudinal CT Radiomics to Predict Progression-free Survival in Patients with Locally Advanced Gastric Cancer After Neoadjuvant Chemotherapy.

RATIONALE AND OBJECTIVES

To develop and validate a radiomics signature, utilizing baseline and restaging CT, for preoperatively predicting progression-free survival (PFS) after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC).

METHODS

A total of 316 patients with LAGC who received NAC followed by gastrectomy were retrospectively included in this single-center study; these patients were split into two cohorts, one for training (n = 243) and the other for validation (n = 73), based on the different districts of our hospital. A total of 1316 radiomics features were extracted from the volume of interest of the gastric-cancer lesion on venous phase CT images. Four radiomics signatures were built for predicting PFS based on baseline CT (Pre-Rad), restaging CT (Post-Rad), delta radiomics (Delta-Rad) and multi-time radiomics (PrePost-Rad), respectively. Then the PrePost-Rad was combined with clinical factors to establish a nomogram (Rad-clinical model). Kaplan-Meier survival curves with log-rank tests were used to assess the prognostic usefulness of the Rad-clinical model.

RESULTS

All baseline characteristics were not statistically different between the two cohorts. The PrePost-Rad achieved improved predictive value by a C-index of 0.724 (95% CI: 0.639-0.809) in the validation cohort [Pre-Rad: 0.715 (0.632-0.798); Post-Rad: 0.632 (0.538-0.725), Delta-Rad: 0.549 (0.447-0.651)]. In terms of clinical benefit, calibration capability, and prediction efficacy, the Rad-clinical model performed well for PFS prediction, with a C-index of 0.754 (95% CI: 0.707-0.800) and 0.719 (95% CI: 0.639-0.800) in the training and validation cohorts, respectively, superior to the clinical model (cN stage and CA199) but comparable to the PrePost-Rad. Moreover, the Rad-clinical model could accurately classify gastric-cancer patients after NAC into three PFS risk groups in both training and validation cohorts. The risk stratification also performed well in most subgroups (good responders, poor responders, ypTNM Ⅱ, and ypTNM Ⅲ/Ⅳ).

CONCLUSIONS

The Rad-clinical model integrating longitudinal radiomics score and clinical factors performed well in preoperatively predicting PFS of LAGC patients after NAC and surgery.