Development and validation of a multifeature-integrated nomogram for predicting postoperative survival in macrotrabecular-massive hepatocellular carcinoma

Abstract

Macrotrabecular-Massive Hepatocellular Carcinoma (MTM-HCC) represents a highly aggressive histologic subtype associated with a poor prognosis. This study aimed to construct and validate a Multifeature-Integrated Nomogram model based on clinical, pathological, and imaging characteristics to predict the prognosis of patients following the resection of primary hepatocellular carcinoma. This study employed a retrospective cohort design. Baseline data, preoperative laboratory tests, imaging studies, and postoperative pathology were collected from patients diagnosed with hepatocellular carcinoma. Samples were meticulously screened according to exclusion criteria to ensure consistency. Statistical analyses were conducted using SPSS (version 18.0) and R (version 4.5.0). Independent predictors influencing overall survival (OS) were identified through univariate and multivariate Cox regression models, and corresponding nomogram were constructed. The models’ discriminatory ability and accuracy were evaluated using receiver operating characteristic (ROC) and calibration curves. Decision curve analysis (DCA) and clinical impact curves (CIC) were used to visualize the net benefit and real-world implications of the model, thereby assessing its clinical utility. The study cohort comprised 88 patients with MTM-HCC and 176 patients with non-MTM-HCC. Multivariate analysis indicated that direct bilirubin (DB) > 7 µmol/L (HR = 1.63), high neutrophil-lymphocyte ratio (NLR) (HR = 2.11), multiple tumor foci (HR = 3.65), tumor diameter > 5 cm (HR = 2.00), macrovascular invasion (HR = 3.28), Edmonson-Steiner grades III-IV (HR = 2.01), and the MTM-HCC subtype (HR = 2.14) were independent predictors of OS. The nomogram prediction model, which incorporated these factors, achieved a C-index of 0.804, indicating favorable predictive performance. DCA demonstrated that the model could provide a net benefit across a broad range of thresholds, confirming its clinical utility. The MTM-HCC subtype is a robust independent predictor of poor survival after hepatectomy. We developed and validated a single-center multifeature-integrated nomogram that demonstrated high predictive performance in our internal validation. In this single-center, retrospective cohort, the model shows potential as a practical tool for individual postoperative risk stratification. However, external validation in multi-center prospective cohorts is required before any clinical implementation. This trial was registered at www.chictr.org.cn (ChiCTR2500103101). This study was approved by the institutional review board of the First Hospital of Xian Jiaotong University (XJTU1AF2025LSYY354).