Development and validation of a nomogram to predict the difficulty of minimally invasive liver resection for hepatocellular carcinoma: A retrospective multicenter study

Objective Minimally invasive liver resection (MILR) is a common surgical treatment for hepatocellular carcinoma (HCC). However, the current difficulty scoring system for MILR is limited to small samples or a single center, and a scoring system to assess surgery only for HCC is rare. Thus, we developed and validated a novel difficulty prediction system based on data from multiple centers to assess the surgical difficulty of MILR for treating HCC. Methods This retrospective multicenter cohort study included patients who underwent MILR for resectable HCC between June 2011 and November 2022 at eight minimally invasive surgical centers in China. The cohort was randomly divided into a training dataset and a validation dataset at a 3:1 ratio. Single and multiple logistic regression analyses were performed on the training dataset to identify independent risk factors for surgical difficulty and construct a prediction model. The performance of the model was validated by the validation dataset, and its discriminative ability, calibration, and clinical effectiveness were assessed using receiver operating characteristic curves and decision curve analysis. Results This study included 776 patients. Cirrhosis (OR, 1.91; 95% CI, 1.12–2.64), maximum tumor diameter (OR, 1.4; 95% CI, 1.26–1.56), and surgical resection extent (grade B: OR, 11.92; 95% CI, 3.53–38.32; grade C: OR, 46.97; 95% CI, 9.31–228.18; grade D: OR, 32.85; 95% CI, 9.25–111.29) were identified as independent risk factors influencing the challenge of MILR for HCC. The AUC was 0.783 (95% CI, 0.746–0.820) for the training cohort and 0.717 (95% CI, 0.643–0.791) for the validation cohort. Decision curve analysis revealed that this clinical model has strong practicality. Conclusions This novel nomogram performed well in predicting the difficulty of MILR in treating HCC on the basis of Eastern data.