Published online Oct 26, 2022. doi: 10.12998/wjcc.v10.i30.10882
Peer-review started: June 19, 2022
First decision: July 13, 2022
Revised: July 24, 2022
Accepted: September 16, 2022
Article in press: September 16, 2022
Published online: October 26, 2022
Processing time: 123 Days and 12 Hours
The risk factors affecting the cancer-specific survival (CSS) of non-small cell lung cancer (NSCLC) patients with liver metastasis (LM) (NSCLC-LM) are not well known.
A nomographic chart transforms complex patient information into a visual graph, which is characterized by its excellent predictive accuracy and definite reliability when generally applied to decision-making by clinicians.
To build a forecasting model to predict the survival time of NSCLC-LM patients.
Joinpoint analysis was used to estimate the incidence trend of NSCLC-LM. Cox regression was applied to identify the independent prognostic predictors of CSS. A survival prediction model was constructed for predicting 3-, 6-, and 12-mo CSS. The predictive ability of the nomogram was estimated using calibration curves and decision curve analyses (DCAs).
Clinical variables including age, marital status, sex, race, histological type, T stage, metastatic pattern, and whether the patient received chemotherapy or were identified as independent prognostic factors for CSS (P < 0.05) and were further used to construct a nomogram. The results of DCAs and calibration curves showed that the nomogram was well-discriminated and had great clinical utility.
A convenient and credible nomogram model was constructed, which could aid in guiding treatment strategies and prognostic evaluation for clinicians.
Our study may serve as a reference for clinicians to identify high-risk populations for providing individualized therapy.