A LASSO-Based Nomogram for the Risk Assessment of Cancer Anorexia in Postoperative Gastric Cancer Patients.

INTRODUCTION &

Aims

Cancer anorexia (CA) is a prevalent and debilitating complication in postoperative patients with gastric cancer (GC). Standardized tools for the early prediction of CA risk in this specific population remain lacking. This study aimed to identify predictor domains retained in a TRIPOD-compliant multivariable model and to develop a nomogram for CA risk assessment in postoperative patients with GC.

Methods

We performed a cross-sectional predictive modeling study of 440 postoperative patients with GC at a tertiary cancer center. Using a date-level temporal split by questionnaire assessment date (cut date: 2024-09-08), patients assessed on or before the cut (2023-04-07 to 2024-09-08; n = 309) formed the training cohort and those assessed after the cut (2024-09-09 to 2024-11-30; n = 131) formed the temporal validation cohort. CA was defined using the Anorexia/Cachexia Subscale-12 (A/CS-12) of the FAACT questionnaire, a validated screening instrument with a cut-off of total score 37. A pool of 30 candidate predictors spanning sociodemographic, lifestyle, AJCC 8th edition pathological staging (pT/pN/pM), surgical access, multi-organ resection, chemotherapy status at assessment, nutritional (GLIM), psychosocial (K-10, PSSS), and symptom (MDASI-GI) domains was screened by LASSO regression with 10-fold cross-validation, restricted to the training cohort in accordance with TRIPOD guidance. Surviving variables were entered into a multivariable logistic regression with AIC-based backward elimination. A nomogram was constructed from the final model. Discrimination, calibration (Hosmer-Lemeshow chi-square, calibration slope and intercept, Brier score), and clinical utility (decision curve analysis) were evaluated in the temporal validation cohort..

Results

The prevalence of A/CS-12-defined CA was 39.55%. LASSO retained 15 variables on the training cohort; AIC backward elimination produced a final model with 11 predictor domains: sex, education level, commercial medical insurance, albumin level, scope of gastrectomy, multi-organ resection, malnutrition (GLIM), psychological distress (K-10 total), symptom severity (MDASI-GI mean), symptom interference (MDASI-GI mean), perceived social support (categorical). Domain-level likelihood-ratio tests are reported in Table 4b. The nomogram, constructed in one-to-one correspondence with the AIC-final model, achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (0.793-0.881) in training and 0.75 (0.664-0.831) in temporal validation. Decision curve analysis showed net benefit over default strategies across a clinically meaningful range of threshold probabilities. Calibration in the validation cohort was imperfect (Hosmer-Lemeshow chi-square = 14.79, p = 0.063; calibration slope = 0.66; intercept = -0.42). Intercept + slope recalibration in the validation cohort improved the slope to 1.00 and Hosmer-Lemeshow p to 0.698.

Conclusions

The nomogram provides good discrimination for A/CS-12-defined cancer-anorexia risk in postoperative gastric-cancer patients and may be useful as a screening aid to direct early nutritional and psychosocial interventions toward the highest-risk patients. The model is best regarded as a potentially useful screening tool that requires recalibration and external multicenter validation before clinical implementation.