Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study

• Published in: Radiologia medica 2023-12, Vol.128 (12), p.1460-1471
• Main Authors: Zheng, Chao, Gu, Xin-tao, Huang, Xiao-li, Wei, Yu-chen, Chen, Lu, Luo, Ning-bin, Lin, Hua-shan, Jin-yuan, Liao
• Format: Article
• Language: English
• Subjects: Abdominal Radiology; Bile Duct Neoplasms - diagnostic imaging; Bile Duct Neoplasms - surgery; Bile Ducts, Intrahepatic; Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - surgery; Cholangiocarcinoma; Cholangiocarcinoma - diagnostic imaging; Cholangiocarcinoma - surgery; Computed tomography; Decision analysis; Diagnostic Radiology; Humans; Imaging; Interventional Radiology; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - surgery; Medicine; Medicine & Public Health; Neuroradiology; Nomograms; Performance prediction; Prediction models; Radiology; Regression analysis; Retrospective Studies; Tomography, X-Ray Computed; Training; Tumors; Ultrasound
• ISSN: 1826-6983; 0033-8362; 1826-6983
• DOI: 10.1007/s11547-023-01726-2

Purpose To establish and validate a multiparameter prediction model for early recurrence after radical resection in patients diagnosed with combined hepatocellular-cholangiocarcinoma (cHCC-CC). Materials and methods This study reviewed the clinical characteristics and preoperative CT images of 143 cHCC-CC patients who underwent radical resection from three institutions. A total of 110 patients from institution 1 were randomly divided into training set ( n  = 78) and testing set ( n  = 32) in the ratio of 7–3. Univariate and multivariate logistic regression analysis were used to construct a nomogram prediction model in the training set, which was internally and externally validated in the testing set and the validation set ( n  = 33) from institutions 2 and 3. The area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration analysis were used to evaluate the model’s performance. Results The combined model demonstrated superior predictive performance compared to the clinical model, the CT model, the pathological model and the clinic-CT model in predicting the early postoperative recurrence. The nomogram based on the combined model included AST, ALP, tumor size, tumor margin, arterial phase peritumoral enhancement, and MVI (Microvascular invasion). The model had AUCs of 0.89 (95% CI 0.81–0.96), 0.85 (95% CI 0.70–0.99), and 0.86 (95% CI 0.72–1.00) in the training, testing, and validation sets, respectively, indicating high predictive power. DCA showed that the combined model had good clinical value and correction effect. Conclusion A nomogram incorporating clinical characteristics and preoperative CT features can be utilized to effectively predict the early postoperative recurrence in patients with cHCC-CC.