Differentiation of Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma through MRI Radiomics

Research progress of MRI-based radiomics in hepatocellular carcinoma

Background

Primary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspecific early symptoms and late-stage detection, contributing to its poor prognosis. Moreover, the limited efficacy of existing treatments and high recurrence rates post-surgery compound the challenges in managing this disease. While histopathologic examination remains the cornerstone for HCC diagnosis, its utility in guiding preoperative decisions is constrained. Radiomics, an emerging field, harnesses high-throughput imaging data, encompassing shape, texture, and intensity features, alongside clinical parameters, to elucidate disease characteristics through advanced computational techniques such as machine learning and statistical modeling. MRI radiomics specifically holds significant importance in the diagnosis and treatment of hepatocellular carcinoma (HCC).

Objective

This study aims to evaluate the methodology of radiomics and delineate the clinical advancements facilitated by MRI-based radiomics in the realm of hepatocellular carcinoma diagnosis and treatment.

Methods

A systematic review of the literature was conducted, encompassing peer-reviewed articles published between July 2018 and Jan 2025, sourced from PubMed and Google Scholar. Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.

Results

A comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. These encompass tumor differentiation, subtype classification, histopathological grading, prediction of microvascular invasion (MVI), assessment of treatment response, early recurrence prognostication, and metastasis prediction.

Conclusion

MRI radiomics emerges as a promising adjunctive tool for early HCC detection and personalized preoperative decision-making, with the overarching goal of optimizing patient outcomes. Nevertheless, the current lack of interpretability within the field underscores the imperative for continued research and validation efforts.

CT- and MRI-based Factors Associated with Rapid Growth in Early-Stage Hepatocellular Carcinoma

Background Prediction of the tumor growth rates is clinically important in patients with hepatocellular carcinoma (HCC), but previous studies have presented conflicting results and generally lacked radiologic evaluations. Purpose To evaluate the percentage of rapidly growing early-stage HCCs in each Liver Imaging Reporting and Data System (LI-RADS) category and to identify prognostic factors associated with rapid growth. Materials and Methods Retrospective study of patients with risk factors for HCC and those with surgically proven early-stage HCC who underwent two or more preoperative multiphasic CT or MRI examinations between January 2016 and December 2020. LI-RADS categories were assigned according to the baseline CT or MRI results. The tumor volume doubling time (TVDT) was calculated from the tumor volumes measured at the two examinations. The growth rate was classified as rapid (TVDT < 3 months), intermediate (TVDT = 3-9 months), or indolent (TVDT > 9 months). The percentage of rapidly growing HCCs was compared among the LI-RADS categories, and multivariable logistic regression was used to identify factors associated with rapidly growing HCC. Results In 322 patients (mean age, 61 years ± 9 [SD]; 249 men) with 345 HCCs (30 LR-3, 64 LR-4, 221 LR-5, and 30 LR-M category), the median TVDT of HCC was 131 days (IQR, 87-233) and 27.0% of HCCs showed rapid growth. The growth rates differed among the LI-RADS categories, with a higher percentage of rapidly growing HCCs observed for LR-M HCCs than for LR-3 (70.0% vs 3.3%, P < .001), LR-4 (70.0% vs 12.5%, P < .001), or LR-5 (70.0% vs 28.5%, P < .001) HCCs. An α-fetoprotein level greater than 400 ng/mL (adjusted odds ratio [OR], 2.54; 95% CI: 1.16, 5.54; P = .02), baseline tumor diameter (adjusted OR, 0.65; 95% CI: 0.48, 0.87; P = .004), and LR-M category (adjusted OR, 9.26; 95% CI: 3.70, 23.16; P < .001) were independently associated with higher odds of rapid growth. Conclusion Among early-stage HCCs, LR-M category was an independent factor for rapid growth, observed in 70% of HCCs. © RSNA, 2024 Supplemental material is available for this article.

Multisequence MRI-Based Radiomic Features Combined with Inflammatory Indices for Predicting the Overall Survival of HCC Patients After TACE

Objective

To develop a model for predicting the overall survival (OS) of hepatocellular carcinoma (HCC) patients after transarterial chemoembolization (TACE) on the basis of multisequence MRI radiomic features and clinical variables.

Methods

The DCE-MRI and clinical data of 116 HCC patients treated with TACE for the first time were retrospectively analyzed. The included patients were randomly divided into training and validation cohorts at a ratio of 7:3. Univariate and multivariate Cox proportional hazards regression models were used to identify independent risk factors that affect the OS of patients with HCC after TACE. Radiomic features were extracted from the sequences of FS-T2W images and arterial-phase (A) and portal venous-phase (P) axial DCE-MR images. The LASSO method was used to select the best radiomic features. Logistic regression was used to establish a radiomic model of each sequence, a joint model of MRI features (M model) combined the radiomic features of all the sequences, and a radiomic-clinical model (M-C model) that integrated the radiomic signatures and clinically independent predictors. The diagnostic performance of each model was evaluated as the area under the receiver operating characteristic (ROC) curve (AUC).

Results

The Child-Turcotte-Pugh (CTP) score and neutrophil-to-lymphocyte ratio (NLR) -platelet-to-lymphocyte ratio (PLR) were found to be independent risk factors that affect the OS of patients with HCC treated with TACE. The AUCs of the FS-T2WI, A, P, M, and M-C models for predicting the OS of HCC patients after TACE treatment were 0.779, 0.803, 0.745, 0.858 and 0.893, respectively, in the training group and 0.635, 0.651, 0.644, 0.778 and 0.803, respectively, in the validation group. The M-C model had the best predictive performance.

Conclusion

Multiparameter MRI-based radiomic features may be helpful for predicting OS after TACE treatment in HCC patients. The inclusion of clinical indicators such as inflammation scores can improve the predictive performance.

MRI Radiomics in Imaging of Focal Hepatic Lesions: A Narrative Review

Magnetic resonance imaging (MRI) is generally used to identify, describe, and evaluate treatment responses for focal hepatic lesions. However, the diagnosis and differentiation of such lesions require considerable input from radiologists. In order to reduce these difficulties, radiomics is an artificial intelligence (AI)-based quantitative method that employs the extraction of image features to reliably detect and differentiate focal hepatic lesions. MRI radiomics is a novel technique for the characterization of focal hepatic lesions. It can aid in preoperative evaluation, treatment approach, and forecast microvascular invasion. Although many studies have illustrated its efficiency there are certain limitations such as the absence of a large diverse dataset, comparison with other AI models, integration with histopathological findings, clinical utility, and feasibility.

Differentiation of testicular seminomas from nonseminomas based on multiphase CT radiomics combined with machine learning: A multicenter study

BACKGROUND

Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose.

METHODS

In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility.

RESULTS

Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas.

CONCLUSION

The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.