Development and validation of a nomogram using transaminase levels, cycle cost, and hair loss as predictive factors for the response to neoadjuvant chemotherapy in esophageal cancer.

OBJECTIVE

This study aimed to identify clinical parameters influencing treatment efficacy during neoadjuvant chemotherapy (NAC) for esophageal cancer (EC) patients, with a particular focus on demographic and clinical variables, to establish predictive biomarkers for chemotherapeutic response.

METHODS

A total of 194 EC patients undergoing NAC were retrospectively included. Significant predictors of pathological response were identified through univariate and multivariate logistic regression analyses, followed by the construction of a predictive nomogram. The model’s discriminative performance was validated using receiver operating characteristic (ROC) curve analysis, while calibration accuracy and clinical utility were assessed using bootstrap-corrected calibration curves (1,000 resamples) and decision curve analysis (DCA), respectively. External validation was performed on an independent cohort of 74 EC patients.

RESULTS

Both univariate and multivariate logistic regression analyses identified abnormal transaminase levels, average cycle cost, and hair loss as significant risk factors affecting the effectiveness of NAC (p < 0.05). The constructed nomogram model demonstrated strong predictive performance, with a concordance index (C-index) and area under the curve (AUC) of 0.735. Calibration curves showed a high degree of fit between predicted and observed outcomes, while DCA confirmed the clinical utility of these predictive factors. External validation yielded an AUC of 0.865, further supporting the model’s predictive accuracy.

CONCLUSION

By incorporating the indicators of abnormal transaminase levels, average chemotherapy cycle cost, and the occurrence of hair loss, the developed nomogram effectively identifies EC patients who are more likely to benefit from NAC.