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BACKGROUND: Inflammatory responses, immune status, and nutritional conditions are critical determinants of tumor progression and treatment outcomes in rectal cancer. However, the prognostic value of integrated inflammatory-nutritional scores, including the modified Gustave Roussy Immune (mGRIm) score and modified Naples Prognostic Score (M-NPS), remains underexplored in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). This study aimed to evaluate the prognostic significance of these scores and to develop a nomogram for identifying patient groups who may benefit from nCRT. METHODS: A retrospective cohort study analyzed 157 rectal cancer patients who received nCRT, followed by total mesorectal excision (TME) and adjuvant chemotherapy at a single institution. The mGRIm score (based on lactate dehydrogenase LDH, serum albumin, and neutrophil-to-lymphocyte ratio NLR) and M-NPS (incorporating albumin, total cholesterol, NLR, and lymphocyte-to-monocyte ratio LMR) were calculated. Patients were stratified into high- and low-score groups based on these prognostic scores. Kaplan–Meier analysis and Cox regression were used to assess associations with overall survival (OS) and progression-free survival (PFS). A nomogram integrating independent prognostic factors was developed and validated. RESULTS: High mGRIm and M-NPS scores were significantly associated with worse OS (p 5 cm, pre-radiotherapy metastasis, and high M-NPS as independent predictors of OS, while high mGRIm, advanced N stage, and metastasis predicted inferior PFS. The nomogram demonstrated robust predictive accuracy for 1-, 2-, and 3-year OS (AUC: 0.790, 0.739, 0.708) and PFS (AUC: 0.763, 0.727, 0.786), with excellent calibration. CONCLUSION: The mGRIm score and M-NPS are independent prognostic indicators for rectal cancer patients receiving nCRT. The novel nomogram, integrating these biomarker scores with clinical staging parameters, enables the quantification of individual prognosis before treatment initiation and facilitates the identification of optimal nCRT candidates through multivariable probability estimation.
Wang et al. (Tue,) studied this question.