Radiomic features at Contrast-enhanced CT Predict Virus-driven Liver Fibrosis: A Multi-institutional Study.

BACKGROUND: Liver fibrosis is a major cause of morbidity and mortality among in chronic hepatitis patients. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. METHODS: In this retrospective study, we developed three radiomics models using contrast-enhanced CT scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest (RFS-RF) and the least absolute shrinkage and selection operator (LASSO) logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots and decision curve analysis. External validation was performed on 114 patients from two institutions. RESULTS: Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices: 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (RFR-score and SVMR-score) providing more significant advantages. CONCLUSION: Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis.

INTRODUCTION: Considering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA. METHODS: This retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients. RESULTS: In the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA. CONCLUSION: We developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future.

Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker.

BACKGROUND: To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images. MATERIALS AND METHODS: This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51-67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3-F4 from stage F0-F2. Optimal cutoffs to diagnose significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49-66 years; 89 men). RESULTS: In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70-87%, specificity range of 71-97%, and accuracy range of 82-86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis. CONCLUSION: Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.

Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma.

Background: Prediction models for the histological grade of hepatocellular carcinoma (HCC) remain unsatisfactory. The purpose of this study is to develop preoperative models to predict histological grade of HCC based on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) radiomics. And to compare the performance between artificial neural network (ANN) and logistic regression model. Methods: A total of 122 HCCs were randomly assigned to the training set (n=85) and the test set (n=37). There were 242 radiomic features extracted from volumetric of interest (VOI) of arterial and hepatobiliary phases images. The radiomic features and clinical parameters [gender, age, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), alanine aminotransferase (ALT), aspartate transaminase (AST)] were selected by permutation test and decision tree. ANN of arterial phase (ANN-AP), logistic regression model of arterial phase (LR-AP), ANN of hepatobiliary phase (ANN-HBP), logistic regression mode of hepatobiliary phase (LR-HBP), ANN of combined arterial and hepatobiliary phases (ANN-AP + HBP), and logistic regression model of combined arterial and hepatobiliary phases (LR-AP + HBP) were built to predict HCC histological grade. Those prediction models were assessed and compared. Results: ANN-AP and LR-AP were composed by AST and radiomic features based on arterial phase. ANN-HBP and LR-HBP were composed by AFP and radiomic features based on hepatobiliary phase. ANN-AP + HBP and LR-AP + HBP were composed by AST and radiomic features based on arterial and hepatobiliary phases. The prediction models could distinguish between high-grade tumors [Edmondson-Steiner (E-S) grade III and IV] and low-grade tumors (E-S grade I and II) in both training set and test set. In the test set, the AUCs of ANN-AP, LR-AP, ANN-HBP, LR-HBP, ANN-AP + HBP and LR-AP + HBP were 0.889, 0.777, 0.941, 0.819, 0.944 and 0.792 respectively. The ANN-HBP was significantly superior to LR-HBP (P=0.001). And the ANN-AP + HBP was significantly superior to LR-AP + HBP (P=0.007). Conclusions: Prediction models consisting of clinical parameters and Gd-EOB-DTPA-enhanced MRI radiomic features (based on arterial phase, hepatobiliary phase, and combined arterial and hepatobiliary phases) could distinguish between high-grade HCCs and low-grade HCCs. And the ANN was superior to logistic regression model in predicting histological grade of HCC.

Unexplained Distal Obstructive Biliary Dilatation: A Magnetic Resonance Cholangiopancreatography (MRCP)-based Model to Discriminate Malignant From Benign Origins.

BACKGROUND: The aim was to compare the differences of clinical-radiologic characteristics between malignant and benign causes of patients with unexplained distal obstructive biliary dilatation and to develop a logistic regression model (nomogram) based on those features to predict malignant causes preoperatively. PATIENTS AND METHODS: Clinical-radiologic characteristics of 133 patients with unexplained distal obstructive biliary dilatation were analyzed. Multivariate logistic regression analysis was performed to construct a nomogram to predict malignant causes preoperatively. The developed nomograms were externally validated by assessing their predictive accuracy in an independent set of 90 patients. RESULTS: Intrahepatic bile duct diameter, enlarged gallbladder, direct bilirubin, and carbohydrate antigen19-9 differed significantly between malignant and benign group. In the training set, the logistic regression model showed the discrimination between benign and malignant causes of distal obstructive biliary dilatation with an area under the curve of 0.965, an accuracy of 0.904, a sensitivity of 0.886, a specificity of 0.913. In the validation set, the model showed an area under the curve of 0.851, an accuracy of 0.837, a sensitivity of 0.897, a specificity of 0.750. CONCLUSIONS: Preoperative clinical-radiologic characteristics differed significantly between malignant and benign group. Nomogram based on those features performed well in predicting the malignant causes of patients with unexplained distal obstructive biliary dilatation.

Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection.

To investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840-0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637-0.828) and 0.844 (95% CI 0.751-0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.

Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma.

To explore the value of contrast-enhanced CT texture analysis in predicting isocitrate dehydrogenase (IDH) mutation status of intrahepatic cholangiocarcinomas (ICCs). Institutional review board approved this study. Contrast-enhanced CT images of 138 ICC patients (21 with IDH mutation and 117 without IDH mutation) were retrospectively reviewed. Texture analysis was performed for each lesion and compared between ICCs with and without IDH mutation. All textural features in each phase and combinations of textural features (p < 0.05) by Mann-Whitney U tests were separately used to train multiple support vector machine (SVM) classifiers. The classification generalizability and performance were evaluated using a tenfold cross-validation scheme. Among plain, arterial phase (AP), portal venous phase (VP), equilibrium phase (EP) and Sig classifiers, VP classifier showed the highest accuracy of 0.863 (sensitivity, 0.727; specificity, 0.885), with a mean area under the receiver operating characteristic curve of 0.813 in predicting IDH mutation in validation cohort. Texture features of CT images in portal venous phase could predict IDH mutation status of ICCs with SVM classifier preoperatively.

A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data.

BACKGROUND: To establish and validate a radiomics-based model for predicting liver cirrhosis in patients with hepatitis B virus (HBV) by using non-contrast computed tomography (CT). METHODS: This retrospective study developed a radiomics-based model in a training cohort of 144 HBV-infected patients. Radiomic features were extracted from abdominal non-contrast CT scans. Features selection was performed with the least absolute shrinkage and operator (LASSO) method based on highly reproducible features. Support vector machine (SVM) was adopted to build a radiomics signature. Multivariate logistic regression analysis was used to establish a radiomics-based nomogram that integrated radiomics signature and other independent clinical predictors. Performance of models was evaluated through discrimination ability, calibration and clinical benefits. An internal validation was conducted in 150 consecutive patients. RESULTS: The radiomics signature comprised 25 cirrhosis-related features and showed significant differences between cirrhosis and non-cirrhosis cohorts (P < 0.001). A radiomics-based nomogram that integrates radiomics signature, alanine transaminase, aspartate aminotransferase, globulin and international normalized ratio showed great calibration and discrimination ability in the training cohort (area under the curve [AUC]: 0.915) and the validation cohort (AUC: 0.872). Decision curve analysis confirmed the most clinical benefits can be provided by the nomogram compared with other methods. CONCLUSIONS: Our developed radiomics-based nomogram can successfully diagnose the status of cirrhosis in HBV-infected patients, that may help clinical decision-making.

Preoperative Computed Tomography Features of Intrahepatic Cholangiocarcinoma for Predicting Lymph Node Metastasis and Overall Survival.

OBJECTIVES: The aims of this study were to compare the difference of computed tomography (CT) features between intrahepatic cholangiocarcinomas (ICCs) with and without lymph node metastasis (LNM) and to construct a nomogram to predict LNM and overall survival preoperatively. METHODS: Clinicopathological and contrast-enhanced CT features of 63 patients with ICC were analyzed. Multivariate logistic regression analysis was performed to construct a nomogram to predict LNM preoperatively. Survival curves were drawn with the Kaplan-Meier method, and survival difference was compared. RESULTS: Intrahepatic cholangiocarcinomas with and without LNM differed significantly in clinical symptoms, tumor location, morphologic classification, arterial phase enhancement degree-mean, arterial phase enhancement degree-max, portal venous phase enhancement degree-max, equilibrium phase (EP) enhancement ratio, EP CT value-max, and EP CT value-max/liver. A nomogram based on morphologic classification, EP CT value-max, and EP enhancement ratio was constructed to predict LNM with an area under curve of 0.814 (P < 0.001). Patients with ICC with LNM risk of 0.20 or greater based on the nomogram showed a significantly poorer overall survival than those with LNM risk less than 0.20 (39.5 ± 5.2 vs 51.1 ± 4.7 months). CONCLUSIONS: Preoperative CT features of ICCs differed significantly between those with and without LNM. Nomogram based on those features could predict LNM and overall survival even better than the N stage.

Predicting peritumoral Glisson's sheath invasion of intrahepatic cholangiocarcinoma with preoperative CT imaging.

BACKGROUND: To investigate the differences of clinicopathological characteristics and computed tomography (CT) features between intrahepatic cholangiocarcinomas (ICC) with and without peritumoral Glisson's sheath invasion (PGSI), and to construct a nomogram to predict PGSI of ICCs preoperatively. METHODS: The clinicopathological characteristics and CT features of 84 ICCs were retrospectively analyzed and compared between ICCs with (30/84, 35.7%) and without PGSI (54/84, 64.3%). Multivariate logistic regression analysis was used to identify preoperative independent predictors of PGSI in ICCs. A nomogram was constructed to predict PGSI preoperatively. RESULTS: ICCs with and without PGSI differed significantly in the presence of abdominal pain, serum carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) levels, TNM and T stages, tumor location, intratumoral calcifications, intrahepatic bile duct dilatation, intrahepatic bile duct calculus, morphologic type and dynamic enhancement pattern on CT images (all P<0.05). Abdominal pain, serum CEA level, intrahepatic bile duct dilatation, and morphologic type were independent predictors of PGSI in ICCs. A nomogram based on those predictors was constructed to predict PGSI preoperatively with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.908 (P<0.001). CONCLUSIONS: Clinicopathological characteristics and CT features differed significantly between ICCs with and without PGSI. A nomogram including abdominal pain, serum CEA level, intrahepatic bile duct dilatation, and morphologic type could predict PGSI accurately.