State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations

Combined Hounsfield units of hepatocellular carcinoma on computed tomography and PET as a noninvasive predictor of early recurrence after living donor liver transplantation: Time-to-recurrence survival analysis

BACKGROUND

Liver transplantation is an effective treatment for preventing hepatocellular carcinoma (HCC) recurrence. This retrospective study aimed to quantitatively evaluate the attenuation in Hounsfield units (HU) on contrast-enhanced computed tomography (CECT) as a prognostic factor for hepatocellular carcinoma (HCC) following liver transplantation as a treatment. Our goal is to optimize its predictive ability for early tumor recurrence and compare it with the other imaging modality-positron emission tomography (PET).

METHODS

In 618 cases of LDLT for HCC, only 131 patients with measurable viable HCC on preoperative CECT and preoperative positron emission tomography (PET) evaluations were included, with a minimum follow-up period of 6 years. Cox regression models were developed to identify predictors of postoperative recurrence. Performance metrics for both CT and PET were assessed. The correlation between these two imaging modalities was also evaluated. Survival analyses were conducted using time-dependent receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) to assess accuracy and determine optimized cut-off points.

RESULTS

Univariate and multivariate analyses revealed that both arterial-phase preoperative tumor attenuation (HU) and PET were independent prognostic factors for recurrence-free survival. Both lower arterial tumor enhancement (Cut-off value = 59.2, AUC 0.88) on CT and PET positive (AUC 0.89) increased risk of early tumor recurrence 0.5-year time-dependent ROC. Composites with HU < 59.2 and a positive PET result exhibited significantly higher diagnostic accuracy in detecting early tumor recurrence (AUC = 0.96).

CONCLUSION

Relatively low arterial tumor enhancement values on CECT effectively predict early HCC recurrence after LDLT. The integration of CT and PET imaging may serve as imaging markers of early tumor recurrence in HCC patients after LDLT.

Development of a multi-phase CT-based radiomics model to differentiate heterotopic pancreas from gastrointestinal stromal tumor

BACKGROUND

To investigate whether CT-based radiomics can effectively differentiate between heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST), and whether different resampling methods can affect the model's performance.

METHODS

Multi-phase CT radiological data were retrospectively collected from 94 patients. Of these, 40 with HP and 54 with GISTs were enrolled between April 2017 and November 2021. One experienced radiologist manually delineated the volume of interest and then resampled the voxel size of the images to 0.5 × 0.5 × 0.5 mm3, 1 × 1 × 1 mm3, and 2 × 2 × 2 mm3, respectively. Radiomics features were extracted using PyRadiomics, resulting in 1218 features from each phase image. The datasets were randomly divided into training set (n = 66) and validation set (n = 28) at a 7:3 ratio. After applying multiple feature selection methods, the optimal features were screened. Radial basis kernel function-based support vector machine (RBF-SVM) was used as the classifier, and model performance was evaluated using the area under the receiver operating curve (AUC) analysis, as well as accuracy, sensitivity, and specificity.

RESULTS

The combined phase model performed better than the other phase models, and the resampling method of 0.5 × 0.5 × 0.5 mm3 achieved the highest performance with an AUC of 0.953 (0.881-1), accuracy of 0.929, sensitivity of 0.938, and specificity of 0.917 in the validation set. The Delong test showed no significant difference in AUCs among the three resampling methods, with p > 0.05.

CONCLUSIONS

Radiomics can effectively differentiate between HP and GISTs on CT images, and the diagnostic performance of radiomics is minimally affected by different resampling methods.

Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurrence is crucial for determining appropriate treatment strategies and improving patient outcomes. A certain amount of radiomics models for HCC recurrence prediction have been proposed. This study aimed to assess the role of radiomics models in the prediction of HCC recurrence and to evaluate their methodological quality.

MATERIALS AND METHODS

Databases Cochrane Library, Web of Science, PubMed, and Embase were searched until July 11, 2023 for studies eligible for the meta-analysis. Their methodological quality was evaluated using the Radiomics Quality Score (RQS). The predictive ability of the radiomics model, clinical model, and the combined model integrating the clinical characteristics with radiomics signatures was measured using the concordance index (C-index), sensitivity, and specificity. Radiomics models in included studies were compared based on different imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound/sonography (US), contrast-enhanced ultrasound (CEUS).

RESULTS

A total of 49 studies were included. On the validation cohort, radiomics model performed better (CT: C-index = 0.747, 95% CI: 0.70-0.79; MRI: C-index = 0.788, 95% CI: 0.75-0.83; CEUS: C-index = 0.763, 95% CI: 0.60-0.93) compared to the clinical model (C-index = 0.671, 95% CI: 0.65-0.70), except for ultrasound-based models (C-index = 0.560, 95% CI: 0.53-0.59). The combined model outperformed other models (CT: C-index = 0.790, 95% CI: 0.76-0.82; MRI: C-index = 0.826, 95% CI: 0.79-0.86; US: C-index = 0.760, 95% CI: 0.65-0.87), except for CEUS-based combined models (C-index = 0.707, 95% CI: 0.44-0.97).

CONCLUSION

Radiomics holds the potential to predict HCC recurrence and demonstrates enhanced predictive value across various imaging modalities when integrated with clinical features. Nevertheless, further studies are needed to optimize the radiomics approach and validate the results in larger, multi-center cohorts.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score

BACKGROUND

Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies.

AIM

To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS).

METHODS

A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05.

RESULTS

One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072).

CONCLUSION

Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

Consensus report from the 10th Global Forum for Liver Magnetic Resonance Imaging: developments in HCC management

The 10th Global Forum for Liver Magnetic Resonance Imaging (MRI) was held as a virtual 2-day meeting in October 2021, attended by delegates from North and South America, Asia, Australia, and Europe. Most delegates were radiologists with experience in liver MRI, with representation also from specialists in liver surgery, oncology, and hepatology. Presentations, discussions, and working groups at the Forum focused on the following themes: • Gadoxetic acid in clinical practice: Eastern and Western perspectives on current uses and challenges in hepatocellular carcinoma (HCC) screening/surveillance, diagnosis, and management • Economics and outcomes of HCC imaging • Radiomics, artificial intelligence (AI) and deep learning (DL) applications of MRI in HCC. These themes are the subject of the current manuscript. A second manuscript discusses multidisciplinary tumor board perspectives: how to approach early-, mid-, and late-stage HCC management from the perspectives of a liver surgeon, interventional radiologist, and oncologist (Taouli et al, 2023). Delegates voted on consensus statements that were developed by working groups on these meeting themes. A consensus was considered to be reached if at least 80% of the voting delegates agreed on the statements. CLINICAL RELEVANCE STATEMENT: This review highlights the clinical applications of gadoxetic acid-enhanced MRI for liver cancer screening and diagnosis, as well as its cost-effectiveness and the applications of radiomics and AI in patients with liver cancer. KEY POINTS: • Interpretation of gadoxetic acid-enhanced MRI differs slightly between Eastern and Western guidelines, reflecting different regional requirements for sensitivity vs specificity. • Emerging data are encouraging for the cost-effectiveness of gadoxetic acid-enhanced MRI in HCC screening and diagnosis, but more studies are required. • Radiomics and artificial intelligence are likely, in the future, to contribute to the detection, staging, assessment of treatment response and prediction of prognosis of HCC-reducing the burden on radiologists and other specialists and supporting timely and targeted treatment for patients.

Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients

BACKGROUND

Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC).

METHODS

Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups.

RESULTS

We used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134).

CONCLUSIONS

Parameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.

Radiomics models for preoperative prediction of the histopathological grade of hepatocellular carcinoma: A systematic review and radiomics quality score assessment

OBJECTIVE

To systematically review the efficacy of radiomics models derived from computed tomography (CT) or magnetic resonance imaging (MRI) in preoperative prediction of the histopathological grade of hepatocellular carcinoma (HCC).

METHODS

Systematic literature search was performed at databases of PubMed, Web of Science, Embase, and Cochrane Library up to 30 December 2022. Studies that developed a radiomics model using preoperative CT/MRI for predicting the histopathological grade of HCC were regarded as eligible. A pre-defined table was used to extract the data related to study and patient characteristics, characteristics of radiomics modelling workflow, and the model performance metrics. Radiomics quality score and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were applied for research quality evaluation.

RESULTS

Eleven eligible studies were included in this review, consisting of 2245 patients (range 53-494, median 165). No studies were prospectively designed and only two studies had an external test cohort. Half of the studies (five) used CT images and the other half MRI. The median number of extracted radiomics features was 328 (range: 40-1688), which was reduced to 11 (range: 1-50) after feature selection. The commonly used classifiers were logistic regression and support vector machine (both 4/11). When evaluated on the two external test cohorts, the area under the curve of the radiomics models was 0.70 and 0.77. The median radiomics quality score was 10 (range 2-13), corresponding to 28% (range 6-36%) of the full scale. Most studies showed an unclear risk of bias as evaluated by QUADAS-2.

CONCLUSION

Radiomics models based on preoperative CT or MRI have the potential to be used as an imaging biomarker for prediction of HCC histopathological grade. However, improved research and reporting quality is required to ensure sufficient reliability and reproducibility prior to implementation into clinical practice.

Radiomics-Clinical AI Model with Probability Weighted Strategy for Prognosis Prediction in Non-Small Cell Lung Cancer

In this study, we propose a radiomics clinical probability-weighted model for the prediction of prognosis for non-small cell lung cancer (NSCLC). The model combines radiomics features extracted from radiotherapy (RT) planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volumes (GTVs). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM) were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). The Radiomic model, clinical factor model, and combined radiomic clinical probability-weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949, respectively. The combined radiomics clinical probability-weighted enhanced model achieved significantly better performance than the radiomic model in 1-year survival prediction (chi-square test, p < 0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.

Unsupervised Machine Learning of MRI Radiomics Features Identifies Two Distinct Subgroups with Different Liver Function Reserve and Risks of Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma

OBJECTIVE

To identify subgroups of patients with hepatocellular carcinoma (HCC) with different liver function reserves using an unsupervised machine-learning approach on the radiomics features from preoperative gadoxetic-acid-enhanced MRIs and to evaluate their association with the risk of post-hepatectomy liver failure (PHLF).

METHODS

Clinical data from 276 consecutive HCC patients who underwent liver resections between January 2017 and March 2019 were retrospectively collected. Radiomics features were extracted from the non-tumorous liver tissue at the gadoxetic-acid-enhanced hepatobiliary phase MRI. The reproducible and non-redundant features were selected for consensus clustering analysis to detect distinct subgroups. After that, clinical variables were compared between the identified subgroups to evaluate the clustering efficacy. The liver function reserve of the subgroups was compared and the correlations between the subgroups and PHLF, postoperative complications, and length of hospital stay were evaluated.

RESULTS

A total of 107 radiomics features were extracted and 37 were selected for unsupervised clustering analysis, which identified two distinct subgroups (138 patients in each subgroup). Compared with subgroup 1, subgroup 2 had significantly more patients with older age, albumin-bilirubin grades 2 and 3, a higher indocyanine green retention rate, and a lower indocyanine green plasma disappearance rate (all p < 0.05). Subgroup 2 was also associated with a higher risk of PHLF, postoperative complications, and longer hospital stays (>18 days) than that of subgroup 1, with an odds ratio of 2.83 (95% CI: 1.58-5.23), 2.41(95% CI: 1.15-5.35), and 2.14 (95% CI: 1.32-3.47), respectively. The odds ratio of our method was similar to the albumin-bilirubin grade for postoperative complications and length of hospital stay (2.41 vs. 2.29 and 2.14 vs. 2.16, respectively), but was inferior for PHLF (2.83 vs. 4.55).

CONCLUSIONS

Based on the radiomics features of gadoxetic-acid-enhanced MRI, unsupervised clustering analysis identified two distinct subgroups with different liver function reserves and risks of PHLF in HCC patients. Future studies are required to validate our findings.

Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging

Introduction

The successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature.

Methods

This study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary.

Results and discussion

The experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.

Gadoxetic Acid-Enhanced MRI-Based Radiomics Signature: A Potential Imaging Biomarker for Identifying Cytokeratin 19-Positive Hepatocellular Carcinoma

Purpose

One subtype of hepatocellular carcinoma (HCC), with cytokeratin 19 expression (CK19+), has shown to be more aggressive and has a poor prognosis. However, CK19+ is determined by immunohistochemical examination using a surgically resected specimen. This study is aimed at establishing a radiomics signature based on preoperative gadoxetic acid-enhanced MRI for predicting CK19 status in HCC. Patients and Methods. Clinicopathological and imaging data were retrospectively collected from patients who underwent hepatectomy between February 2015 and December 2020. Patients who underwent gadoxetic acid-enhanced MRI and had CK19 results of histopathological examination were included. Radiomics features of the manually segmented lesion during the arterial, portal venous, and hepatobiliary phases were extracted. The 10 most reproducible and robust features at each phase were selected for construction of radiomics signatures, and their performance was evaluated by analyzing the area under the curve (AUC). The goodness of fit of the model was assessed by the Hosmer-Lemeshow test.

Results

A total of 110 patients were included. The incidence of CK19(+) HCC was 17% (19/110). Alpha fetoprotein was the only significant clinicopathological variable different between CK19(-) and CK19(+) groups. A majority of the selected radiomics features were wavelet filter-derived features. The AUCs of the three radiomics signatures based on arterial, portal venous, and hepatobiliary phases were 0.70 (95% CI: 0.56-0.83), 0.83 (95% CI: 0.73-0.92), and 0.89 (95% CI: 0.82-0.96), respectively. The three radiomics signatures were integrated, and the fusion signature yielded an AUC of 0.92 (95% CI: 0.86-0.98) and was used as the final model for CK19(+) prediction. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the fusion signature was 0.84, 0.89, 0.88, 0.62, and 0.96, respectively. The Hosmer-Lemeshow test showed a good fit of the fusion signature (p > 0.05).

Conclusion

The established radiomics signature based on preoperative gadoxetic acid-enhanced MRI could be an accurate and potential imaging biomarker for HCC CK19(+) prediction.

Development and External Validation of a Radiomics Model Derived from Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Histopathologic Grade of Hepatocellular Carcinoma

Histopathologic grade of hepatocellular carcinoma (HCC) is an important predictor of early recurrence and poor prognosis after curative treatments. This study aims to develop a radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting HCC histopathologic grade and to validate its predictive performance in an independent external cohort. Clinical and imaging data of 403 consecutive HCC patients were retrospectively collected from two hospitals (265 and 138, respectively). Patients were categorized into poorly differentiated HCC and non-poorly differentiated HCC groups. A total of 851 radiomics features were extracted from the segmented tumor at the hepatobiliary phase images. Three classifiers, logistic regression (LR), support vector machine, and Adaboost were adopted for modeling. The areas under the curve of the three models were 0.70, 0.67, and 0.61, respectively, in the external test cohort. Alpha-fetoprotein (AFP) was the only significant clinicopathological variable associated with HCC grading (odds ratio: 2.75). When combining AFP, the LR+AFP model showed the best performance, with an AUC of 0.71 (95%CI: 0.59-0.82) in the external test cohort. A radiomics model based on gadoxetic acid-enhanced MRI was constructed in this study to discriminate HCC with different histopathologic grades. Its good performance indicates a promise in the preoperative prediction of HCC differentiation levels.

Current status and future perspectives of radiomics in hepatocellular carcinoma

Given the frequent co-existence of an aggressive tumor and underlying chronic liver disease, the management of hepatocellular carcinoma (HCC) patients requires experienced multidisciplinary team discussion. Moreover, imaging plays a key role in the diagnosis, staging, restaging, and surveillance of HCC. Currently, imaging assessment of HCC entails the assessment of qualitative characteristics which are prone to inter-reader variability. Radiomics is an emerging field that extracts high-dimensional mineable quantitative features that cannot be assessed visually with the naked eye from medical imaging. The main potential applications of radiomic models in HCC are to predict histology, response to treatment, genetic signature, recurrence, and survival. Despite the encouraging results to date, there are challenges and limitations that need to be overcome before radiomics implementation in clinical practice. The purpose of this article is to review the main concepts and challenges pertaining to radiomics, and to review recent studies and potential applications of radiomics in HCC.

Primary Tumor Radiomic Model for Identifying Extrahepatic Metastasis of Hepatocellular Carcinoma Based on Contrast Enhanced Computed Tomography

This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.

Radiomics-Based Prediction of Future Portal Vein Tumor Infiltration in Patients with HCC-A Proof-of-Concept Study

Portal vein infiltration (PVI) is a typical complication of HCC. Once diagnosed, it leads to classification as BCLC C with an enormous impact on patient management, as systemic therapies are henceforth recommended. Our aim was to investigate whether radiomics analysis using imaging at initial diagnosis can predict the occurrence of PVI in the course of disease. Between 2008 and 2018, we retrospectively identified 44 patients with HCC and an in-house, multiphase CT scan at initial diagnosis who presented without CT-detectable PVI but developed it in the course of disease. Accounting for size and number of lesions, growth type, arterial enhancement pattern, Child-Pugh stage, AFP levels, and subsequent therapy, we matched 44 patients with HCC who did not develop PVI to those developing PVI in the course of disease (follow-up ended December 2021). After segmentation of the tumor at initial diagnosis and texture analysis, we used LASSO regression to find radiomics features suitable for PVI detection in this matched set. Using an 80:20 split between training and holdout validation dataset, 17 radiomics features remained in the fitted model. Applying the model to the holdout validation dataset, sensitivity to detect occurrence of PVI was 0.78 and specificity was 0.78. Radiomics feature extraction had the ability to detect aggressive HCC morphology likely to result in future PVI. An additional radiomics evaluation at initial diagnosis might be a useful tool to identify patients with HCC at risk for PVI during follow-up benefiting from a closer surveillance.

Contrast phase recognition in liver computer tomography using deep learning

Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams. However, the lack of standardization in the naming of the CT contrast phases in the DICOM metadata is a problem for real-life deployment of machine learning tools. Therefore, it is important to correctly identify the exam phase based only on the image and not on the exam metadata, which is unreliable. Motivated by this problem, we successfully created an annotation platform and implemented a convolutional neural network (CNN) to automatically identify the CT scan phases in the HCFMUSP database in the city of São Paulo, Brazil. We improved this algorithm with hyperparameter tuning and evaluated it with cross validation methods. Comparing its predictions with the radiologists annotation, it achieved an accuracy of 94.6%, 98% and 100% in the testing dataset for the slice, volume and exam evaluation, respectively.

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

Local-Regional Treatment of Hepatocellular Carcinoma: A Primer for Radiologists

The treatment planning for patients with hepatocellular carcinoma (HCC) relies predominantly on tumor burden, clinical performance, and liver function test results. Curative treatments such as resection, liver transplantation, and ablative therapies of small lesions should be considered for all patients with HCC. However, many patients are ineligible for these treatments owing to advanced disease stage and comorbidities. Despite efforts to increase screening, early-stage HCC remains difficult to diagnose, which decreases the possibility of curative therapies. In this context, local-regional treatment of HCC is accepted as a form of curative therapy in selected patients with early-stage disease, as a therapeutic option in patients who are not eligible to undergo curative therapies, as a downstaging approach to decrease tumor size toward meeting the criteria for liver transplantation, and as a bridging therapy to avoid tumor growth while the patient is on the waiting list for liver transplantation. The authors review the indications, types, mechanism of action, and possible complications of local-regional treatment, as well as the expected postprocedural imaging features of HCC. Furthermore, they discuss the role of imaging in pre- and postprocedural settings, provide guidance on how to assess treatment response, and review the current limitations of imaging assessment. Finally, the authors summarize the potential future directions with imaging tools that may add value to contemporary practice at response assessment and imaging biomarkers for patient selection, treatment response, and prognosis. ©RSNA, 2022.

A primer on texture analysis in abdominal radiology

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.

Radiomics: a primer on high-throughput image phenotyping

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.

Evaluation of perfusion CT and dual-energy CT for predicting microvascular invasion of hepatocellular carcinoma

PURPOSE

Evaluation of perfusion CT and dual-energy CT (DECT) quantitative parameters for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) prior to surgery.

METHODS

This prospective single-center study included fifty-six patients (44 men; median age 67; range 31-84) who provided written informed consent. Inclusion criteria were (1) treatment-naïve patients with a diagnosis of HCC, (2) an indication for hepatic resection, and (3) available arterial DECT phase and perfusion CT (GE revolution HD-GSI). Iodine concentrations (IC), arterial density (AD), and 9 quantitative perfusion parameters for HCC were correlated to pathological results. Radiological parameters based principal component analysis (PCA), corroborated by unsupervised heatmap classification, was meant to deliver a model for predicting MVI in HCC. Survival analysis was performed using univariable log-rank test and multivariable Cox model, both censored at time of relapse.

RESULTS

58 HCC lesions were analyzed (median size 42.3 mm; range of 20-140). PCA showed that the radiological model was predictive of tumor grade (p = 0.01), intratumoral MVI (p = 0.004), peritumoral MVI (p = 0.04), MTM (macrotrabecular-massive) subtype (p = 0.02), and capsular invasion (p = 0.02) in HCC. Heatmap classification of HCC showed tumor heterogeneity, stratified into three main clusters according to the risk of relapse. Survival analysis confirmed that permeability surface-area product (PS) was the only significant independent parameter, among all quantitative tumoral CT parameters, for predicting a risk of relapse (Cox p value = 0.004).

CONCLUSION

A perfusion CT and DECT-based quantitative imaging profile can provide a diagnosis of histological MVI in HCC. PS is an independent parameter for relapse.

CLINICAL TRIALS

ClinicalTrials.gov: NCT03754192.

MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contrast images. Radiomic features from T2 images and arterial, portal and tardive post-contrast images from DCE-MRI were extracted by using Pyradiomics. Feature selection was performed using correlation filter, Wilcoxon-rank sum test and mutual information. Predictive models were constructed for HCC differentiation with respect to HT and HCC histopathologic grading used at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. Promising results were obtained from radiomic prediction models, with best AUCs ranging from 71% to 96%. Radiomics MRI based on T2 and DCE-MRI revealed promising results concerning both HCC detection and grading. It may be a suitable tool for personalized treatment of HCC patients and could also be used to develop new prognostic biomarkers useful for HCC assessment without the need for invasive procedures.

Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma

BACKGROUND

Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology.

AIMS

To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation.

METHODS

We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies.

RESULTS

A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard.

CONCLUSIONS

Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.

Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response

Radiomics refers to the process of conversion of conventional medical images into quantifiable data ("features") which can be further mined to reveal complex patterns and relationships between the voxels in the image. These high throughput features can potentially reflect the histology of biologic tissues at macroscopic and microscopic levels. Several studies have investigated radiomics of hepatocellular carcinoma (HCC) before and after treatment. HCC is a heterogeneous disease with diverse phenotypical and genotypical landscape. Due to this inherent heterogeneity, HCC lesions can manifest variable aggressiveness with different response to treatment options, including the newer targeted therapies. Hence, radiomics can be used as a potential tool to enable patient selection for therapies and to predict response to treatments and outcome. Additionally, radiomics may serve as a tool for earlier and more efficient assessment of response to treatment. Radiomics, radiogenomics, and radio-immunoprofiling and their potential roles in management of patients with HCC will be discussed and critically reviewed in this article.

Artificial intelligence in assessment of hepatocellular carcinoma treatment response

Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor.

Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases

Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2-3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.

Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice

Magnetic resonance imaging (MRI) is highly important for the detection, characterization, and follow-up of focal liver lesions. Several quantitative MRI-based methods have been proposed in addition to qualitative imaging interpretation to improve the diagnostic work-up and prognostics in patients with focal liver lesions. This includes DWI with apparent diffusion coefficient measurements, intravoxel incoherent motion, perfusion imaging, MR elastography, and radiomics. Multiple research studies have reported promising results with quantitative MRI methods in various clinical settings. Nevertheless, applications in everyday clinical practice are limited. This review describes the basic principles of quantitative MRI-based techniques and discusses the main current applications and limitations for the assessment of focal liver lesions.

The Growing Skyline of Advanced Hepatocellular Carcinoma Treatment: A Review

Hepatocellular carcinoma (HCC) is the main type of liver cancer. In the majority of cases, HCC is diagnosed at the advanced stage, leading to poor prognosis. In recent years, many efforts have been devoted to investigating potential new and more effective drugs and, indeed, the treatment armamentarium for advanced HCC has broadened tremendously, with targeted- and immune-therapies, and probably the combination of both, playing pivotal roles. Together with new established knowledge, many issues are emerging, with the role of neoadjuvant/adjuvant settings, the definition of the best transitioning time from loco-regional treatments to systemic therapy, the identification of potential predictive biomarkers, and radiomics being just some of the topics that will have to be further explored in the next future. Clearly, the current COVID-19 pandemic has influenced the management of HCC patients and some considerations about this topic will be elucidated.

Radiomic analysis of MRI to Predict Sustained Complete Response after Radiofrequency Ablation in Patients with Hepatocellular Carcinoma - A Pilot Study

OBJECTIVES

To investigate whether quantitative textural features, extracted from pretreatment MRI, can predict sustained complete response to radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC).

METHODS

In this IRB-approved study, patients were selected from a maintained six-year database of consecutive patients who underwent both pretreatment MRI imaging with a probable or definitive imaging diagnosis of HCC (LI-RADS 4 or 5) and loco-regional treatment with RFA. An experienced radiologist manually segmented the hepatic nodules in MRI arterial and equilibrium phases to obtain the volume of interest (VOI) for extraction of 107 quantitative textural features, including shape and first- and second-order features. Statistical analysis was performed to evaluate associations between textural features and complete response.

RESULTS

The study consisted of 34 patients with 51 treated hepatic nodules. Sustained complete response was achieved by 6 patients (4 with single nodule and 2 with multiple nodules). Of the 107 features from the arterial and equilibrium phases, 20 (18%) and 25 (23%) achieved AUC >0.7, respectively. The three best performing features were found in the equilibrium phase: Dependence Non-Uniformity Normalized and Dependence Variance (both GLDM class, with AUC of 0.78 and 0.76, respectively) and Maximum Probability (GLCM class, AUC of 0.76).

CONCLUSIONS

This pilot study demonstrates that a radiomic analysis of pre-treatment MRI might be useful in identifying patients with HCC who are most likely to have a sustained complete response to RFA. Second-order features (GLDM and GLCM) extracted from equilibrium phase obtained highest discriminatory performance.

Texture analysis on preoperative contrast-enhanced magnetic resonance imaging identifies microvascular invasion in hepatocellular carcinoma

BACKGROUND

Radiomic texture analysis quantifies tumor heterogeneity. The aim of this study is to determine if radiomics can predict biologic aggressiveness in HCC and identify tumors with MVI.

METHODS

Single-center, retrospective review of HCC patients undergoing resection/ablation with curative intent from 2009 to 2017. DICOM images from preoperative MRIs were analyzed with texture analysis software. Texture analysis parameters extracted on T1, T2, hepatic arterial phase (HAP) and portal venous phase (PVP) images. Multivariate logistic regression analysis evaluated factors associated with MVI.

RESULTS

MVI was present in 52.2% (n = 133) of HCCs. On multivariate analysis only T1 mean (OR = 0.97, 95%CI 0.95-0.99, p = 0.043) and PVP entropy (OR = 4.7, 95%CI 1.37-16.3, p = 0.014) were associated with tumor MVI. Area under ROC curve was 0.83 for this final model. Empirical optimal cutpoint for PVP tumor entropy and T1 tumor mean were 5.73 and 23.41, respectively. At these cutpoint values, sensitivity was 0.68 and 0.5, respectively and specificity was 0.64 and 0.86. When both criteria were met, the probability of MVI in the tumor was 87%.

CONCLUSION

Tumor entropy and mean are both associated with MVI. Texture analysis on preoperative imaging correlates with microscopic features of HCC and can be used to predict patients with high-risk tumors.

Recent Updates of Transarterial Chemoembolilzation in Hepatocellular Carcinoma

Transarterial chemoembolization (TACE) is a standard treatment for intermediate-stage hepatocellular carcinoma (HCC). In this review, we summarize recent updates on the use of TACE for HCC. TACE can be performed using two techniques; conventional TACE (cTACE) and drug-eluting beads using TACE (DEB-TACE). The anti-tumor effect of the two has been reported to be similar; however, DEB-TACE carries a higher risk of hepatic artery and biliary injuries and a relatively lower risk of post-procedural pain than cTACE. TACE can be used for early stage HCC if other curative treatments are not feasible or as a neoadjuvant treatment before liver transplantation. TACE can also be considered for selected patients with limited portal vein thrombosis and preserved liver function. When deciding to repeat TACE, the ART (Assessment for Retreatment with TACE) score and ABCR (AFP, BCLC, Child-Pugh, and Response) score can guide the decision process, and TACE refractoriness needs to be considered. Studies on the combination therapy of TACE with other treatment modalities, such as local ablation, radiation therapy, or systemic therapy, have been actively conducted and are still ongoing. Recently, new prognostic models, including analysis of the neutrophil-lymphocyte ratio, radiomics, and deep learning, have been developed to help predict survival after TACE.

The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

Recently, radiomic texture quantification of tumors has received much attention from radiologists, scientists, and stakeholders because several results have shown the feasibility of using the technique to diagnose and manage oncological conditions. In patients with hepatocellular carcinoma, radiomics has been applied in all stages of tumor evaluation, including diagnosis and characterization of the genotypic behavior of the tumor, monitoring of treatment responses and prediction of various clinical endpoints. It is also useful in selecting suitable candidates for specific treatment strategies. However, the clinical validation of hepatocellular carcinoma radiomics is limited by challenges in imaging protocol and data acquisition parameters, challenges in segmentation techniques, dimensionality reduction, and modeling methods. Identification of the best segmentation and optimal modeling methods, as well as texture features most stable to imaging protocol variability would go a long way in harmonizing HCC radiomics for personalized patient care. This article reviews the process of HCC radiomics, its clinical applications, associated challenges, and current optimization strategies.

New advances in radiomics of gastrointestinal stromal tumors

Gastrointestinal stromal tumors (GISTs) are uncommon neoplasms of the gastrointestinal tract with peculiar clinical, genetic, and imaging characteristics. Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’ treatment. Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography (CT) and magnetic resonance imaging (MRI) evaluation is challenging, unless the lesions have already metastasized at the time of diagnosis. Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images, extracting additional data that cannot be assessed by visual analysis. Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms, risk stratification and prediction of prognosis after surgical resection, and evaluation of mutational status in GISTs. The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI. However, lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice. The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.

Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection

BACKGROUND

This study aimed to evaluate the efficiency of gadoxetic acid-enhanced MRI-based radiomics features for prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients after surgical resection.

METHODS

This prospective study approved by the Institutional Review Board enrolled 120 patients with pathologically confirmed HCC. Radiomics signatures (rad-scores) were built from radiomics features in 3 different regions of interest (ROIs) with the least absolute shrinkage and selection operator (LASSO) cox regression analysis. Preoperative clinical characteristics and semantic imaging features potentially associated with patient survival were evaluated to develop a clinic-radiological model. The radiomics features and clinic-radiological predictors were integrated into a joint model using multivariable Cox regression analysis. Kaplan-Meier analysis and log-rank tests were performed to compare the discriminative performance and evaluated on the validation cohort.

RESULTS

The radiomics signatures showed a significant association with patient survival in both cohorts (all P<0.001). The BCLC (Barcelona clinic liver cancer) stage, non-smooth tumor margin, and the combined rad-score were independently associated with OS. Moreover, the combined model incorporating with clinic-radiological and radiomics features showed an improved predictive performance with C-index of 0.92 [95% confidence interval (CI): 0.87-0.97], compared to the clinic-radiological model (C-index, 0.86, 95% CI: 0.79-0.94; P=0.039) or the combined rad-score (C-index, 0.88, 95% CI: 0.81-0.95; P=0.016).

CONCLUSIONS

Radiomics features along with clinic-radiological predictors can efficiently aid in preoperative HCC prognosis prediction after surgical resection and enable a step forward precise medicine.