Intra- and Peri-tumoral Radiomics Based on Dynamic Contrast Enhanced-MRI to Identify Lymph Node Metastasis and Prognosis in Intrahepatic Cholangiocarcinoma.

BACKGROUND: Lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (iCCA) affects treatment strategies and prognosis. However, preoperative imaging is not reliable enough for identifying LNM. PURPOSE: To develop and validate a radiomics nomogram based on dynamic contrast enhanced (DCE)-MR images for identifying LNM and prognosis in iCCA. STUDY TYPE: Retrospective. SUBJECTS: Two hundred four patients with pathologically proven iCCA who underwent curative-intent resection and lymphadenectomy (training cohort: N = 107, internal test cohort: N = 46, and external test cohort: N = 51). FIELD STRENGTH/SEQUENCE: T1- and T2-weighted imaging, diffusion-weighted imaging and DCE imaging at 1.5 T or 3.0 T. ASSESSMENT: Radiomics features were extracted from intra- and peri-tumoral regions on preoperative DCE-MR images. Imaging features were evaluated by three radiologists, and significant variables in univariable and multivariable regression analysis were included in clinical model. The best-performing radiomics signature and clinical characteristics (intrahepatic duct dilatation, MRI-reported LNM) were combined to build a nomogram. Patients were divided into high-risk and low-risk groups based on their nomogram scores (cutoff = 0.341). Patients were followed up for 1-102 months (median 12) after surgery, the overall survival (OS) and recurrence-free survival (RFS) were calculated. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, Kaplan-Meier curves, log rank test. Two tailed P < 0.05 was considered statistically significant. RESULTS: The nomogram incorporating intra- and peri-tumoral radiomics features, intrahepatic duct dilatation and MRI-reported LNM obtained the best discrimination for LNM, with areas under the ROC curves of 0.946, 0.913, and 0.859 in the training, internal, and external test cohorts. In the entire cohort, high-risk patients had significantly lower RFS and OS than low-risk patients. High-risk of LNM was an independent factor of unfavorable OS and RFS. DATA CONCLUSION: The nomogram integrating intra- and peri-tumoral radiomics signatures has potential to identify LNM and prognosis in iCCA. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

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

OBJECTIVES: The aim of this study was to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting peritoneal metastasis (PM) of gastric cancer (GC). METHODS: In this study, a total of 355 patients (109PM+, 246PM-) who underwent preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images were retrospectively analyzed. According to a 7:3 ratio, patients were randomly divided into a training set and a validation set. Radiomics features and metabolic parameters data were extracted from PET images. The radiomics features were selected by logistic regression after using maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator (LASSO) method. The radiomics models were based on the rest of these features. The performance of the models was determined by their discrimination, calibration, and clinical usefulness in the training and validation sets. RESULTS: After dimensionality reduction, 12 radiomics feature parameters were obtained to construct radiomics signatures. According to the results of the multivariate logistic regression analysis, only carbohydrate antigen 125 (CA125), maximum standardized uptake value (SUVmax), and the radiomics signature showed statistically significant differences between patients (P<0.05). A radiomics model was developed based on the logistic analyses with an AUC of 0.86 in the training cohort and 0.87 in the validation cohort. The clinical prediction model based on CA125 and SUVmax was 0.76 in the training set and 0.69 in the validation set. The comprehensive model, which contained a rad-score and the clinical factor (CA125) as well as the metabolic parameter (SUVmax), showed promising performance with an AUC of 0.90 in the training cohort and 0.88 in the validation cohort, respectively. The calibration curve showed the actual rate of the nomogram-predicted probability of peritoneal metastasis. Decision curve analysis (DCA) also demonstrated the good clinical utility of the radiomics nomogram. CONCLUSIONS: The comprehensive model based on the rad-score and other factors (SUVmax, CA125) can provide a novel tool for predicting peritoneal metastasis of gastric cancer patients preoperatively.

A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is hard to distinguish from inflammatory mass (IM) complicated with hepatolithiasis in clinical practice preoperatively. This study looked to develop and confirm the radiomics models to make a distinction between ICC with hepatolithiasis from IM and to compare the results of different contrast-enhanced computed tomography (CT) phase. METHODS: The models were developed in a training cohort of 110 patients from January 2005 to June 2020. Radiomics features were extracted from both arterial phase and portal venous phase contrast-enhanced computed tomography (CT) scans. The radiomics scores based on radiomics features, were built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-scores of two contrast -enhanced CT phases and clinical features were incorporated into a novel model. The performance of the models were determined by theirs discrimination, calibration, and clinical usefulness. The models were externally validated in 35 consecutive patients. RESULTS: The radiomics signature comprised two features in arterial phase (training cohort, AUC = 0.809, sensitivity 0.700, specificity 0.848, and accuracy 0.774;validation cohort, AUC = 0.790, sensitivity 0.714, specificity 0.800, and accuracy 0.757) and three related features in portal venous phase (training cohort, AUC = 0.801, sensitivity 0.800, specificity 0.717, and accuracy 0.759; validation cohort, AUC = 0.830, sensitivity 0.700, specificity 0.750, and accuracy 0.775) showed significant association with ICC in both cohorts (P < 0.05).We also developed a model only based on clinical variables (training cohort, AUC = 0.778, sensitivity 0.567, specificity 0.891, and accuracy 0.729; validation cohort, AUC = 0.788, sensitivity 0.571, specificity 0.950, and accuracy 0.761). The radiomics-based model contained rad-score of two phases and two clinical factors (CEA and CA19-9) showed the best performance (training cohort, AUC = 0.864, sensitivity 0.867, specificity 0.804, and accuracy 0.836; validation cohort, AUC = 0.843, sensitivity 0.643, specificity 0.980, and accuracy 0.821). CONCLUSIONS: Our radiomics-based models provided a diagnostic tool for differentiate intrahepatic cholangiocarcinoma (ICC) from inflammatory mass (IM) with hepatolithiasis both in arterial phase and portal venous phase. To go a step further, the diagnostic accuracy will improved by a clinico-radiologic model.

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

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.