Development and Validation of a Radiomic-Based Model for Prediction of Intrahepatic Cholangiocarcinoma in Patients With Intrahepatic Lithiasis Complicated by Imagologically Diagnosed Mass

Abstract

BackgroundThis 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).MethodsA 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.ResultsThe 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).ConclusionsThe 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.

Survival Prediction in Intrahepatic Cholangiocarcinoma: A Proof of Concept Study Using Artificial Intelligence for Risk Assessment

“…However, they may not cover the clinical complexity because they are all based only on a few, mainly tumor burden-associated factors. Knowledge about novel risk factors, such as the tumor microenvironment, the influence of inflammation and immune reactions, body composition assessment, tumor standardized uptake in hybrid positron emission tomography/computed tomography imaging, and image-based texture analysis has continuously been increasing [ 25 , 32 , 33 , 34 , 35 , 36 , 37 ]. Therefore, the integration of these factors into scoring systems has great potential.…”

Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis

“…After at least 6 h of fasting, the patients received an intravenous injection of 18 F-FDG (3.7 MBq/kg). Blood glucose was controlled below 110 ml/dl.…”

“…Computed tomography (CT) is a common method in the diagnosis of GC, but its sensitivity in the evaluation of PM is low (8). 18 F-fluorodeoxyglucose positron emission tomography/CT ( 18 F-FDG PET/CT) is a powerful, noninvasive tool to evaluate various tumors (9)(10)(11). The sensitivity of detecting PM by PET/CT is higher than CT (12).…”

“…The emerging field of "radiomics" has great potential in disease diagnosis, prognosis evaluation, and prediction of treatment (15). It successfully showed favorable abilities in clinical management (16)(17)(18)(19). However, no studies have used the PET-based radiomics tool to predicting the PM of GC.…”

“…In this study, we attempt to further explore a novel model based on 18 F-FDG PET combined with clinical and metabolic factors to predict the PM of GC.…”