Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.396
Peer-review started: August 25, 2023
First decision: September 29, 2023
Revised: November 5, 2023
Accepted: January 19, 2024
Article in press: January 19, 2024
Published online: February 27, 2024
Processing time: 184 Days and 12.1 Hours
Neoadjuvant chemotherapy (NAC) has an unclear therapeutic effect on advanced gastric cancer (GC).
This work focused on identifying factors related to chemosensitivity to NAC treatment to be able to offer the best treatments for GC patients receiving NAC.
To find factors associated with chemosensitivity to NAC treatment and to provide the optimal therapeutic strategies for GC patients receiving NAC.
Predicting factors were identified by least absolute shrinkage and selection operator logistic regression. Additionally, a nomogram model was employed to predict the response to NAC.
We enrolled 230 patients, consisting of 154 males (67.0%) and 76 females (33.0%). These patients were aged 24-80 years (average, 59.37 ± 10.60). According to the TRG standard, 95 patients were assigned into the obvious response group (grades 0-1) and 135 into the poor response group (grades 2-3), yielding an obvious response rate of 41.3%. As revealed by the least absolute shrinkage and selection operator regression, tumor location (P < 0.001), histological differentiation (P = 0.001), clinical T stage (P = 0.008), and carbohydrate antigen 724 (P = 0.008) were significant risk factors for NAC efficacy. The C-index of the prediction nomogram was 0.806. According to calibration curve analysis, the predicted value was highly consistent with real measurement. Moreover, decision curve analysis revealed the high application value of this nomogram clinically.
Our nomogram combining tumor location, histological differentiation, clinical T stage, and carbohydrate antigen 724 showed a high performance in predicting NAC response, which can be applied in identifying the best therapeutic strategies for advanced GC patients by gastrointestinal surgeons.
Candidate predictive factors were identified by the least absolute shrinkage and selection operator logistic regression. The response to NAC was predicted by a nomogram model.