Dynamic Prediction of Survival after Curative Resection of Gastric Adenocarcinoma: A landmarking-based analysis.

BACKGROUND

Accurate estimation of survival and recurrence are important to inform decisions regarding therapy and surveillance. We sought to design and validate a dynamic prognostic model for patients undergoing resection for gastric adenocarcinoma.

METHODS

Patients who underwent curative-intent surgery for gastric adenocarcinoma between 2000 and 2020 were identified using a multi-institutional database. Landmark analysis was used to create dynamic OS and DFS prediction models. Model performance was internally cross-validated via bootstrap resampling.

RESULTS

Among 895 patients, 507 (57.2%) patients underwent partial gastrectomy (n = 507, 57.2%) while 380 (42.8%) had total gastrectomy. Median tumor size was 40 mm (IQR: 25-65), most tumors were located in the antrum (n = 344, 39.5%) and infiltrated the subserosa (T3 tumors: n = 283, 31.9%) or serosa (T4 tumors: n = 253, 28.5%); lymph node metastasis occurred in 528 (59.1%) patients. Median OS and DFS were 17.5 (IQR: 7.5-42.8) and 14.3 months (IQR: 6.5-39.9), respectively. The impact of age, sex, preoperative comorbidities, tumor size and location, extent of lymphadenectomy and total number of lymph nodes examined, Lauren class, T and N category, postoperative complications, and tumor recurrence varied over time (all p < 0.05). An online tool to predict dynamic OS and DFS based on patient survival relative to time survived was developed and made available for clinical use. Discrimination ability of OS and DFS was excellent (C-index: 0.84 and 0.86, respectively) and calibration plots revealed good prediction.

CONCLUSIONS

An online dynamic prognostic tool was developed and validated to predict OS and DFS following resection of gastric adenocarcinoma. Landmark analysis to predict long-term outcomes based on follow-up time may be helpful to surgeons and patients.