MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm

Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis

INTRODUCTION

Transarterial chemoembolization (TACE) is one of the predominant locoregional therapeutic modalities for addressing hepatocellular carcinoma (HCC). However, achieving precise prognostic predictions and effective patient selection remains a challenging pursuit. The primary objective of this systematic review and meta-analysis is to evaluate the efficacy of radiomics in forecasting the prognosis associated with TACE treatment.

METHODS

A comprehensive exploration of pertinent original studies was undertaken, encompassing databases of PubMed, Web of Science and Embase. The studies' quality was meticulously evaluated employing the quality assessment of diagnostic accuracy studies 2 (QUADAS-2), the radiomics quality score (RQS) and the METhodological RadiomICs Score (METRICS). Pooled statistics, along with 95% confidence intervals (95% CI), were computed for sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Additionally, a summary receiver operating characteristic curve (sROC) was generated. To discern potential sources of heterogeneity, meta-regression and subgroup analyses were performed.

RESULTS

The systematic review incorporated 29 studies, comprising a total of 5483 patients, with 14 studies involving 2691 patients qualifying for inclusion in the meta-analysis. The assessed studies exhibited commendable quality with regard to bias risk, with mean RQS of 12.90 ± 5.13 (35.82% ± 14.25%) and mean METRICS of 62.98% ± 14.58%. The pooled sensitivity was 0.83 (95% CI: 0.78-0.87), specificity was 0.86 (95% CI: 0.79-0.92), PLR was 6.13 (95% CI: 3.79-9.90), and NLR was 0.20 (95% CI: 0.15-0.27). The area under the sROC was 0.90 (95% CI: 0.87-0.93). Significant heterogeneity within all the included studies was observed, while meta-regression and subgroup analyses revealed homogeneous and promising findings in subgroups where principal methodological variables such as modeling algorithms, imaging modalities, and imaging phases were specified.

CONCLUSION

Radiomics models have exhibited robust predictive capabilities concerning prognosis subsequent to TACE, thereby presenting promising prospects for clinical translation.

Predicting Survival Using Whole-Liver MRI Radiomics in Patients with Hepatocellular Carcinoma After TACE Refractoriness

PURPOSE

To develop a model based on whole-liver radiomics features of pre-treatment enhanced MRI for predicting the prognosis of hepatocellular carcinoma (HCC) patients undergoing continued transarterial chemoembolization (TACE) after TACE-resistance.

MATERIALS AND METHODS

Data from 111 TACE-resistant HCC patients between January 2014 and March 2018 were retrospectively collected. At a ratio of 7:3, patients were randomly assigned to developing and validation cohorts. The whole-liver were manually segmented, and the radiomics signature was extracted. The tumor and liver radiomics score (TLrad-score) was calculated. Models were trained by machine learning algorithms and their predictive efficacies were compared.

RESULTS

Tumor stage, tumor burden, body mass index, alpha-fetoprotein, and vascular invasion were revealed as independent risk factors for survival. The model trained by Random Forest algorithms based on tumor burden, whole-liver radiomics signature, and clinical features had the highest predictive efficacy, with c-index values of 0.85 and 0.80 and areas under the ROC curve of 0.96 and 0.83 in the developing cohort and validation cohort, respectively. In the high-rad-score group (TLrad-score > - 0.34), the median overall survival (mOS) was significantly shorter than in the low-rad-score group (17 m vs. 37 m, p < 0.001). A shorter mOS was observed in patients with high tumor burden compared to those with low tumor burden (14 m vs. 29 m, p = 0.007).

CONCLUSION

The combined radiomics model from whole-liver signatures may effectively predict survival for HCC patients continuing TACE after TACE refractoriness. The TLrad-score and tumor burden are potential prognostic markers for TACE therapy following TACE-resistance.

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

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.

The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review

Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions. Furthermore, the review addresses the challenges associated with the implementation of AI and texture analysis in clinical practice, including data acquisition, standardization of imaging protocols, and model validation. Future directions and potential advancements in this field are discussed. Integration of multi-modal imaging data, incorporation of genomics and clinical data, and the development of predictive models with enhanced interpretability are proposed as potential avenues for further research. In conclusion, the application of AI and texture analysis in predicting outcomes of interventional radiological treatment for liver lesions shows great promise in augmenting clinical decision-making and improving patient care. By leveraging these technologies, clinicians can potentially enhance treatment planning, optimize intervention strategies, and ultimately improve patient outcomes in the management of liver lesions.

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma

BACKGROUND

Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients.

METHODS

A total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman's rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively.

RESULTS

The T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821-0.936) and 0.911 (95%CI, 0.825-0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854-0.958) and 0.918 (95%CI, 0.831-0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709-0.863) and 0.782 (95%CI, 0.660-0.902)] in the both cohorts and achieved good calibration capability and clinical utility.

CONCLUSIONS

CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score

Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model's efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE.

Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis

Purpose

To examine the methodological quality of radiomics-related studies and evaluate the ability of radiomics to predict treatment response to transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC).

Methods

A systematic review was performed on radiomics-related studies published until October 15, 2022, predicting the effectiveness of TACE for HCC. Methodological quality and risk of bias were assessed using the Radiomics Quality Score (RQS) and Quality Assessment of Diagnostic Accuracy Studies-2 tools, respectively. Pooled sensitivity, pooled specificity, and area under the curve (AUC) were determined to evaluate the utility of radiomics in predicting the response to TACE for HCC.

Results

In this systematic review, ten studies were eligible, and six of these studies were used in our meta-analysis. The RQS ranged from 7-21 (maximum possible score: 36). The pooled sensitivity and specificity were 0.89 (95% confidence interval (CI) = 0.79-0.95) and 0.82 (95% CI = 0.64-0.92), respectively. The overall AUC was 0.93 (95% CI = 0.90-0.95).

Conclusion

Radiomics-related studies evaluating the efficacy of TACE in patients with HCC revealed promising results. However, prospective and multicenter trials are warranted to make radiomics more feasible and acceptable.

Radiomic advances in the transarterial chemoembolization related therapy for hepatocellular carcinoma

Radiomics is a hot topic in the research on customized oncology treatment, efficacy evaluation, and tumor prognosis prediction. To achieve the goal of mining the heterogeneity information within the tumor tissue, the image features concealed within the tumoral images are turned into quantifiable data features. This article primarily describes the research progress of radiomics and clinical-radiomics combined model in the prediction of efficacy, the choice of treatment modality, and survival in transarterial chemoembolization (TACE) and TACE combination therapy for hepatocellular carcinoma.

Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

Machine-Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant

Background: The aim was to investigate the role of pre-ablation tumor radiomics in predicting pathologic treatment response in patients with early-stage hepatocellular carcinoma (HCC) who underwent liver transplant. Methods: Using data collected from 2005–2015, we included adult patients who (1) had a contrast-enhanced MRI within 3 months prior to ablation therapy and (2) underwent liver transplantation. Demographics were obtained for each patient. The treated hepatic tumor volume was manually segmented on the arterial phase T1 MRI images. A vector with 112 radiomic features (shape, first-order, and texture) was extracted from each tumor. Feature selection was employed through minimum redundancy and maximum relevance using a training set. A random forest model was developed based on top radiomic and demographic features. Model performance was evaluated by ROC analysis. SHAP plots were constructed in order to visualize feature importance in model predictions. Results: Ninety-seven patients (117 tumors, 31 (32%) microwave ablation, 66 (68%) radiofrequency ablation) were included. The mean model for end-stage liver disease (MELD) score was 10.5 ± 3. The mean follow-up time was 336.2 ± 179 days. Complete response on pathology review was achieved in 62% of patients at the time of transplant. Incomplete pathologic response was associated with four features: two first-order and two GLRM features using univariate logistic regression analysis (p < 0.05). The random forest model included two radiomic features (diagnostics maximum and first-order maximum) and four clinical features (pre-procedure creatinine, pre-procedure albumin, age, and gender) achieving an AUC of 0.83, a sensitivity of 82%, a specificity of 67%, a PPV of 69%, and an NPV of 80%. Conclusions: Pre-ablation MRI radiomics could act as a valuable imaging biomarker for the prediction of tumor pathologic response in patients with HCC.

Comparison of the radiomics-based predictive models using machine learning and nomogram for epidermal growth factor receptor mutation status and subtypes in lung adenocarcinoma

The purpose of this study is to develop the predictive models for epidermal growth factor receptor (EGFR) mutation status and subtypes [exon 21-point mutation (L858R) and exon 19 deletion mutation (19Del)] and evaluate their clinical usefulness. Total 172 patients with lung adenocarcinoma were retrospectively analyzed. The analysis of variance and the least absolute shrinkage were used for feature selection from plain computed tomography images. Then, radiomic score (rad-score) was calculated for the training and test cohorts. Two machine learning (ML) models with 5-fold were applied to construct the predictive models with rad-score, clinical features, and the combination of rad-score and clinical features. The nomogram was developed using rad-score and clinical features. The prediction performance was evaluated by the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis (DCA) was performed using the best ML and nomogram models. In the test cohorts, the AUC of the best ML and the nomogram model were 0.73 (95% confidence interval, 0.59-0.87) and 0.79 (0.65-0.92) in the EGFR mutation groups, 0.83 (0.67-0.99) and 0.85 (0.72-0.97) in the L858R mutation groups, as well as 0.77 (0.58-0.97) and 0.77 (0.60-0.95) in the 19Del groups. The DCA showed that the nomogram models have comparable results with ML models. We constructed two predictive models for EGFR mutation status and subtypes. The nomogram models had comparable results to the ML models. Because the superiority of the performance of ML and nomogram models varied depending on the prediction groups, appropriate model selection is necessary.

Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI

OBJECTIVES

This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance.

METHODS

The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN).

RESULTS

DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735).

CONCLUSIONS

DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.

Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.

Prediction of therapeutic response of advanced hepatocellular carcinoma to combined targeted immunotherapy by MRI

PURPOSE

To assess the value of pre-treatment MRI in predicting treatment response to combined targeted immunotherapy in advanced hepatocellular carcinoma (HCC).

METHODS

Totally 35 HCC participants who underwent pre-treatment contrast-enhanced MRI and received combined tyrosine kinase inhibitor (TKI) and anti-PD-1 antibody treatment were enrolled. Univariable and multivariable logistic regression analyses were carried out for comparing clinical and MRI characteristics between patients with therapeutic response and those without. A predictive model based on MRI data and the corresponding nomogram were developed using data generated by multivariate analysis, and the diagnostic performance was evaluated. A cutoff for the combined index was measured by receiver operating characteristic curve analysis, and progression-free survival (PFS) rates were compared between cases with high and low combined index values.

RESULTS

Fifteen (42.86%) cases achieved overall response during treatment. Multivariable analysis revealed that homogeneous signal (odds ratio [OR] = 13.51, P = 0.010) and no arterial peritumoral enhancement (APE; OR = 10.29, P = 0.024) independently predicted treatment response. The combined model including both significant MRI parameters showed a satisfactory predictive performance with the largest area under the curve of 0.837 (95%CI 0.673-0.939), and both sensitivity and specificity of 80.0%. HCCs with high-combined index had higher PFS rate compared with those showing a low value (P = 0.034).

CONCLUSION

The combination of pre-treatment MRI features of homogeneous signal and no APE could be used for predicting treatment response to combined targeted immunotherapy in advanced HCC.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

We aimed to identify hepatocellular carcinoma (HCC) patients who will respond to repetitive transarterial chemoembolization (TACE) to improve the treatment algorithm. Retrospectively, 61 patients (mean age, 65.3 years ± 10.0 [SD]; 49 men) with 94 HCC mRECIST target-lesions who had three consecutive TACE between 01/2012 and 01/2020 were included. Robust and non-redundant radiomics features were extracted from the 24 h post-embolization CT. Five different clinical TACE-scores were assessed. Seven different feature selection methods and machine learning models were used. Radiomics, clinical and combined models were built to predict response to TACE on a lesion-wise and patient-wise level as well as its impact on overall-survival prognostication. 29 target-lesions of 19 patients were evaluated in the test set. Response rates were 37.9% (11/29) on the lesion-level and 42.1% (8/19) on the patient-level. Radiomics top lesion-wise response prognostications was AUC 0.55-0.67. Clinical scores revealed top AUCs of 0.65-0.69. The best working model combined the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical score mHAP_II_score_group with AUC = 0.70, accuracy = 0.72. We transferred this model on a patient-level to achieve AUC = 0.62, CI = 0.41-0.83. The two radiomics-clinical features revealed overall-survival prognostication of C-index = 0.67. In conclusion, a random forest model using the radiomic feature LargeDependenceHighGrayLevelEmphasis and the clinical mHAP-II-score-group seems promising for TACE response prognostication.

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 Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC

Objectives: To explore the potential of radiomics on gadoxetate disodium-enhanced MRI for predicting hepatocellular carcinoma (HCC) response after transarterial embolization (TAE). Methods: This retrospective study included cirrhotic patients treated with TAE for unifocal HCC naïve to treatments. Each patient underwent gadoxetate disodium-enhanced MRI. Radiomics analysis was performed by segmenting the lesions on portal venous (PVP), 3-min transitional, and 20-min hepatobiliary (HBP) phases. Clinical data, laboratory variables, and qualitative features based on LI-RADSv2018 were assessed. Reference standard was based on mRECIST response criteria. Two different radiomics models were constructed, a statistical model based on logistic regression with elastic net penalty (model 1) and a computational model based on a hybrid descriptive-inferential feature extraction method (model 2). Areas under the ROC curves (AUC) were calculated. Results: The final population included 51 patients with HCC (median size 20 mm). Complete and objective responses were obtained in 14 (27.4%) and 29 (56.9%) patients, respectively. Model 1 showed the highest performance on PVP for predicting objective response with an AUC of 0.733, sensitivity of 100%, and specificity of 40.0% in the test set. Model 2 demonstrated similar performances on PVP and HBP for predicting objective response, with an AUC of 0.791, sensitivity of 71.3%, specificity of 61.7% on PVP, and AUC of 0.790, sensitivity of 58.8%, and specificity of 90.1% on HBP. Conclusions: Radiomics models based on gadoxetate disodium-enhanced MRI can achieve good performance for predicting response of HCCs treated with TAE.

Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma

Purpose

This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE.

Patients and Methods

The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan–Meier (K-M) analysis and the Log rank test.

Results

The median OS was 439 days (95% CI: 215.795–662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172–932.828), 395 days (95% CI: 309.415–480.585), respectively (P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285–3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310–2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496–1.000) and combined model (C-index: 0.770, 95% CI: 0.581–0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508–0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort.

Conclusion

Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.

An update on radiomics techniques in primary liver cancers

BACKGROUND

Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting.

METHODS

This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed.

RESULTS

In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring.

CONCLUSIONS

Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model.

TRIAL REGISTRATION

Not applicable.

Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study

Purpose: To develop and validate a random forest (RF) based predictive model of early refractoriness to transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC).

Methods: A total of 227 patients with unresectable HCC who initially treated with TACE from three independent institutions were retrospectively included. Following a random split, 158 patients (70%) were assigned to a training cohort and the remaining 69 patients (30%) were assigned to a validation cohort. The process of variables selection was based on the importance variable scores generated by RF algorithm. A RF predictive model incorporating the selected variables was developed, and five-fold cross-validation was performed. The discrimination and calibration of the RF model were measured by a receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test.

Results: The potential variables selected by RF algorithm for developing predictive model of early TACE refractoriness included patients' age, number of tumors, tumor distribution, platelet count (PLT), and neutrophil-to-lymphocyte ratio (NLR). The results showed that the RF predictive model had good discrimination ability, with an area under curve (AUC) of 0.863 in the training cohort and 0.767 in the validation cohort, respectively. In Hosmer-Lemeshow test, the RF model had a satisfactory calibration with P values of 0.538 and 0.068 in training cohort and validation cohort, respectively.

Conclusion: The RF algorithm-based model has a good predictive performance in the prediction of early TACE refractoriness, which may easily be deployed in clinical routine and help to determine the optimal patient of care.

Progress of MRI Radiomics in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.

Objective

This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.

Methods

A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.

Results

Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.

Conclusion

Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.