Beihui Xue1, Sunjie Wu1, Minghua Zheng2, Huanchang Jiang3, Jun Chen3, Zhenghao Jiang3, Tian Tian3, Yifan Tu3, Huanhu Zhao4, Xian Shen5, Kuvaneshan Ramen6, Xiuling Wu7, Qiyu Zhang8, Qiqiang Zeng5, Xiangwu Zheng1. 1. Radiological Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 2. Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 3. The First Clinical Medical College of Wenzhou Medical University, Wenzhou, China. 4. School of Pharmacy, Minzu University of China, Beijing, China. 5. The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China. 6. Dr A. G Jeetoo Hospital, Port Louis, Mauritius. 7. Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 8. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Abstract
BACKGROUND: This study was conducted with the intent to develop and validate a radiomic model capable of predicting intrahepatic cholangiocarcinoma (ICC) in patients with intrahepatic lithiasis (IHL) complicated by imagologically diagnosed mass (IM). METHODS: A radiomic model was developed in a training cohort of 96 patients with IHL-IM from January 2005 to July 2019. Radiomic characteristics were obtained from arterial-phase computed tomography (CT) scans. The radiomic score (rad-score), based on radiomic features, was built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-score and other independent predictors were incorporated into a novel comprehensive model. The performance of the Model was determined by its discrimination, calibration, and clinical usefulness. This model was externally validated in 35 consecutive patients. RESULTS: The rad-score was able to discriminate ICC from IHL in both the training group (AUC 0.829, sensitivity 0.868, specificity 0.635, and accuracy 0.723) and the validation group (AUC 0.879, sensitivity 0.824, specificity 0.778, and accuracy 0.800). Furthermore, the comprehensive model that combined rad-score and clinical features was great in predicting IHL-ICC (AUC 0.902, sensitivity 0.771, specificity 0.923, and accuracy 0.862). CONCLUSIONS: The radiomic-based model holds promise as a novel and accurate tool for predicting IHL-ICC, which can identify lesions in IHL timely for hepatectomy or avoid unnecessary surgical resection.
BACKGROUND: This study was conducted with the intent to develop and validate a radiomic model capable of predicting intrahepatic cholangiocarcinoma (ICC) in patients with intrahepatic lithiasis (IHL) complicated by imagologically diagnosed mass (IM). METHODS: A radiomic model was developed in a training cohort of 96 patients with IHL-IM from January 2005 to July 2019. Radiomic characteristics were obtained from arterial-phase computed tomography (CT) scans. The radiomic score (rad-score), based on radiomic features, was built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-score and other independent predictors were incorporated into a novel comprehensive model. The performance of the Model was determined by its discrimination, calibration, and clinical usefulness. This model was externally validated in 35 consecutive patients. RESULTS: The rad-score was able to discriminate ICC from IHL in both the training group (AUC 0.829, sensitivity 0.868, specificity 0.635, and accuracy 0.723) and the validation group (AUC 0.879, sensitivity 0.824, specificity 0.778, and accuracy 0.800). Furthermore, the comprehensive model that combined rad-score and clinical features was great in predicting IHL-ICC (AUC 0.902, sensitivity 0.771, specificity 0.923, and accuracy 0.862). CONCLUSIONS: The radiomic-based model holds promise as a novel and accurate tool for predicting IHL-ICC, which can identify lesions in IHL timely for hepatectomy or avoid unnecessary surgical resection.
Authors: F Donato; U Gelatti; A Tagger; M Favret; M L Ribero; F Callea; C Martelli; A Savio; P Trevisi; G Nardi Journal: Cancer Causes Control Date: 2001-12 Impact factor: 2.506
Authors: A Guglielmi; A Ruzzenente; A Valdegamberi; F Bagante; S Conci; A D Pinna; G Ercolani; F Giuliante; L Capussotti; L Aldrighetti; C Iacono Journal: Eur J Surg Oncol Date: 2013-12-18 Impact factor: 4.424
Authors: Lukas Müller; Aline Mähringer-Kunz; Simon Johannes Gairing; Friedrich Foerster; Arndt Weinmann; Fabian Bartsch; Lisa-Katharina Heuft; Janine Baumgart; Christoph Düber; Felix Hahn; Roman Kloeckner Journal: J Clin Med Date: 2021-05-12 Impact factor: 4.241