Published online Sep 15, 2021. doi: 10.4251/wjgo.v13.i9.1184
Peer-review started: April 12, 2021
First decision: June 23, 2021
Revised: July 1, 2021
Accepted: July 27, 2021
Article in press: July 27, 2021
Published online: September 15, 2021
Processing time: 150 Days and 21.8 Hours
It remains controversial as to which pathological classification is most valuable in predicting overall survival (OS) in patients with gastric cancer (GC).
Recently, it has been proposed that the Lauren classification be modified to include both the Lauren classification and the anatomical location of GC, thus yielding at least three entirely distinct types, namely, the proximal non-diffuse type, distal non-diffuse type, and diffuse type.
To assess the prognostic performances of three pathological classifications in GC and develop a novel prognostic nomogram for individually predicting OS.
We retrospectively reviewed and analyzed the data identified from the Surveillance, Epidemiology, and End Results program.
A total of 2718 eligible GC patients were identified. The modified Lauren classification was identified as one of the independent prognostic factors for OS. It showed superior model discriminative ability and model-fitting performance over the other pathological classifications, and similar results were obtained in various patient settings. In addition, it showed superior net benefits over the Lauren classification and tumor differentiation grade in predicting 3- and 5-year OS. A novel prognostic nomogram incorporating the modified Lauren classification showed superior model discriminative ability, model-fitting performance, and net benefits over the American Joint Committee on Cancer 8th edition tumor-node-metastasis classification.
The modified Lauren classification shows superior net benefits over the Lauren classification and tumor differentiation grade in predicting OS. A novel prognostic nomogram incorporating the modified Lauren classification shows good model discriminative ability, model-fitting performance, and net benefits.
A large prospective study is needed to validate our findings.