Preoperative prediction model for microvascular invasion in HBV-related intrahepatic cholangiocarcinoma
Liang Yu,
Mu-Gen Dai,
Wen-Feng Lu,
Dong-Dong Wang,
Tai-Wei Ye,
Fei-Qi Xu,
Si-Yu Liu,
Lei Liang,
Du-Jin Feng
Affiliations
Liang Yu
Department of Radiology, Cancer Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College
Mu-Gen Dai
Department of Gastroenterology, The Fifth Affiliated Hospital of Wenzhou Medical University
Wen-Feng Lu
Department of Hepatobiliary Surgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University
Dong-Dong Wang
Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery , General Surgery, Cancer Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College
Tai-Wei Ye
Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery , General Surgery, Cancer Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College
Fei-Qi Xu
Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery , General Surgery, Cancer Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College
Si-Yu Liu
Department of Gastroenterology, The Fifth Affiliated Hospital of Wenzhou Medical University
Lei Liang
Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery , General Surgery, Cancer Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College
Du-Jin Feng
Department of Clinical Laboratory, Laboratory Medicine Center, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College
Abstract Background and aims Preoperative prediction of microvascular invasion (MVI) using a noninvasive method remain unresolved, especially in HBV-related in intrahepatic cholangiocarcinoma (ICC). This study aimed to build and validate a preoperative prediction model for MVI in HBV-related ICC. Methods Patients with HBV-associated ICC undergoing curative surgical resection were identified. Univariate and multivariate logistic regression analyses were performed to determine the independent risk factors of MVI in the training cohort. Then, a prediction model was built by enrolling the independent risk factors. The predictive performance was validated by receiver operator characteristic curve (ROC) and calibration in the validation cohort. Results Consecutive 626 patients were identified and randomly divided into the training (418, 67%) and validation (208, 33%) cohorts. Multivariate analysis showed that TBIL, CA19-9, tumor size, tumor number, and preoperative image lymph node metastasis were independently associated with MVI. Then, a model was built by enrolling former fiver risk factors. In the validation cohort, the performance of this model showed good calibration. The area under the curve was 0.874 (95% CI: 0.765–0.894) and 0.729 (95%CI: 0.706–0.751) in the training and validation cohort, respectively. Decision curve analysis showed an obvious net benefit from the model. Conclusion Based on clinical data, an easy model was built for the preoperative prediction of MVI, which can assist clinicians in surgical decision-making and adjuvant therapy.
