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

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature

Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.

Diagnostic performance of microRNAs for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis

Purpose

To provide a detailed pooled analysis of the diagnostic accuracy of microRNAs (miRNAs) in predicting the response to transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC).

Methods

A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, and Web of Science to identify studies assessing the diagnostic performance of miRNAs in predicting TACE response in HCC. Two independent reviewers performed quality assessment and data extraction using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic (SROC) curve were calculated using a bivariate random-effects model. Subgroup analyses and meta-regression were performed to explore potential sources of heterogeneity, including sample size, response criteria, specimen source, response evaluation methods, TACE efficacy interval window, and geographical location.

Results

Seven studies, comprising 320 HCC responders and 187 non-responders, were included in this meta-analysis. The miRNAs studied included miR-373, miR-210, miR-4492, miR-1271, miR-214, miR-133b, and miR-335. The pooled sensitivity of miRNAs in predicting recurrence after TACE was 0.79 [95% CI: 0.72-0.84], and the pooled specificity was 0.82 [95% CI: 0.74-0.88]. The DOR was 17 [95% CI: 9-33], and the pooled area under the SROC curve (AUC) was 0.85 [95% CI: 0.81-0.88], indicating excellent diagnostic accuracy. Subgroup analyses revealed significant differences in diagnostic performance based on response criteria and geographical location. Meta-regression did not identify any significant sources of interstudy heterogeneity.

Conclusion

MiRNAs show promise as diagnostic tools for predicting TACE response in HCC patients. However, their clinical application requires further validation in larger cohorts. Future research should focus on standardizing RNA extraction methods, selecting consistent endogenous controls, and adopting uniform response evaluation criteria to improve reliability and reduce variability.

Comparison of various liver cancer staging systems in predicting prognosis after initial transcatheter arterial chemoembolization: a retrospective study from China

Background

Hepatocellular carcinoma (HCC) constitutes approximately 75-85% of primary liver cancers and is a heavy burden on public health. Many innovative prediction systems have integrated radiomics, artificial intelligence, pathological information, or even genetic information for the stratification and prognosis prediction of patients with HCC. However, these systems still lack practical and clinical applications. Classical HCC staging systems remain the mainstream tool for stratification and prediction of treatment efficacy to date; although, variable characteristics and emphases between different classical HCC staging systems render its clinical selection inconsistent and therefore may be unreliable. In this study, we aimed to compare the predictive performance of classical liver cancer staging systems, including China Liver Cancer (CNLC), Barcelona Clinic Liver Cancer (BCLC), Hong Kong Liver Cancer (HKLC), modified Japanese Integrated Staging (mJIS), modified Cancer of the Liver Italian Program (mCLIP), and Tumor-Node-Metastasis (TNM) staging system, for the efficacy and prognosis of transcatheter arterial chemoembolization (TACE) in HCC patients.

Methods

A total of 148 patients with HCC who received TACE as the initial therapy between 02/01/2019 and 08/31/2022 were retrospectively included. Patients' clinical information, laboratory and imaging data, were collected. Cox regression analysis was applied to identify independent risk factors for progression-free survival (PFS) and overall survival (OS). Six liver cancer staging systems, including the CNLC, BCLC, HKLC, mJIS, mCLIP, and TNM staging system, were applied for the staging of every enrolled patient. The PFS and OS of patients with HCC following initial TACE in different staging systems were assessed, and the predictive performance of different systems was evaluated using the concordance index.

Results

The presence of portal vein tumor thrombus (PVT), alpha fetoprotein (AFP) ≥400 ng/mL, and ineffective initial TACE treatment were independent risk factors for overall disease progression, while the presence of PVT and ineffective initial TACE treatment were independent risk factors for death. In the prediction of PFS and OS, CNLC, BCLC, HKLC, mJIS, and mCLIP all showed good predictive ability, but the predictive ability of the TNM staging system was relatively poor.

Conclusions

The CNLC, BCLC, HKLC, mJIS, and mCLIP staging systems provide comparable predictive value for the prognosis after the initial TACE, while the TNM staging system has poor predictive ability due to its exclusion of hepatic function.

Contributing to the prediction of prognosis for treated hepatocellular carcinoma: Imaging aspects that sculpt the future

A novel nomogram model to predict the prognosis of hepatocellular carcinoma (HCC) treated with radiofrequency ablation and transarterial chemoembolization was recently published in the World Journal of Gastrointestinal Surgery. This model includes clinical and laboratory factors, but emerging imaging aspects, particularly from magnetic resonance imaging (MRI) and radiomics, could enhance the predictive accuracy thereof. Multiparametric MRI and deep learning radiomics models significantly improve prognostic predictions for the treatment of HCC. Incorporating advanced imaging features, such as peritumoral hypointensity and radiomics scores, alongside clinical factors, can refine prognostic models, aiding in personalized treatment and better predicting outcomes. This letter underscores the importance of integrating novel imaging techniques into prognostic tools to better manage and treat HCC.

Can Patient Characteristics and Pre-Treatment MRI Features Predict Survival After Stereotactic Ablative Radiotherapy (SABR) Treatment in Hepatocellular Carcinoma (HCC): Preliminary Assessment

BACKGROUND

The study purpose was to develop a machine learning (ML)-based predictive model for event-free survival (EFS) in patients with hepatocellular carcinoma (HCC) undergoing stereotactic ablative radiotherapy (SABR).

METHODS

Patients receiving SABR for HCC at a single institution, between 2017 and 2020, were included in the study. They were split into training and test (85%:15%) cohorts. Events of interest were HCC recurrence or death. Three ML models were trained, the features were selected, and the hyperparameters were tuned. The performance was measured using Harrell's C index with the best-performing model being tested on the unseen cohort.

RESULTS

Overall, 41 patients were included (training = 34, test = 7) and 64 lesions were analysed (training = 50, test = 14), resulting in 30 events (60% rate) in the training set (death = 6, recurrence = 24) and 8 events (57% rate) in the test set (death = 5, recurrence = 3). A Cox regression model, using age at treatment, albumin, and intra-lesional fat identified through MRI as variables, had the best performance with a mean training score of 0.78 (standard deviation (SD) 0.02), a mean validation of 0.78 (SD 0.18), and a test score of 0.94.

CONCLUSIONS

Predicting the outcomes in patients with HCC, following SABR, using a novel model is feasible and warrants further evaluation.

Non-invasive assessment of response to transcatheter arterial chemoembolization for hepatocellular carcinoma with the deep neural networks-based radiomics nomogram

BACKGROUND

Transcatheter arterial chemoembolization (TACE) is a mainstay treatment for intermediate and advanced hepatocellular carcinoma (HCC), with the potential to enhance patient survival. Preoperative prediction of postoperative response to TACE in patients with HCC is crucial.

PURPOSE

To develop a deep neural network (DNN)-based nomogram for the non-invasive and precise prediction of TACE response in patients with HCC.

MATERIAL AND METHODS

We retrospectively collected clinical and imaging data from 110 patients with HCC who underwent TACE surgery. Radiomics features were extracted from specific imaging methods. We employed conventional machine-learning algorithms and a DNN-based model to construct predictive probabilities (RScore). Logistic regression helped identify independent clinical risk factors, which were integrated with RScore to create a nomogram. We evaluated diagnostic performance using various metrics.

RESULTS

Among the radiomics models, the DNN_LASSO-based one demonstrated the highest predictive accuracy (area under the curve [AUC] = 0.847, sensitivity = 0.892, specificity = 0.791). Peritumoral enhancement and alkaline phosphatase were identified as independent risk factors. Combining RScore with these clinical factors, a DNN-based nomogram exhibited superior predictive performance (AUC = 0.871, sensitivity = 0.844, specificity = 0.873).

CONCLUSION

In this study, we successfully developed a deep learning-based nomogram that can noninvasively and accurately predict TACE response in patients with HCC, offering significant potential for improving the clinical management of HCC.

Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study

INTRODUCTION

Transarterial radioembolization (TARE) is one of the most promising therapeutic options for hepatic masses. Radiomics features, which are quantitative numeric features extracted from medical images, are considered to have potential in predicting treatment response in TARE. This article aims to provide meta-analytic evidence and critically appraise the methodology of radiomics studies published in this regard.

METHODS

A systematic search was performed on PubMed, Scopus, Embase, and Web of Science. All relevant articles were retrieved, and the characteristics of the studies were extracted. The Radiomics Quality Score and Checklist for Evaluation of Radiomics Research were used to assess the methodologic quality of the studies. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve in predicting objective response were determined.

RESULTS

The systematic review included 15 studies. The average Radiomics Quality Score of these studies was 11.4 ± 2.1, and the average Checklist for Evaluation of Radiomics Research score was 33± 6.7. There was a notable correlation (correlation coefficient = 0.73) between the two metrics. Adherence to quality measures differed considerably among the studies and even within different components of the same studies. The pooled sensitivity and specificity of the radiomics models in predicting complete or partial response were 83.5% (95% confidence interval 76%-88.9%) and 86.7% (95% confidence interval 78%-92%), respectively.

CONCLUSION

Radiomics models show great potential in predicting treatment response in TARE of hepatic lesions. However, the heterogeneity seen between the methodologic quality of studies may limit the generalizability of the results. Future initiatives should aim to develop radiomics signatures using multiple external datasets and adhere to quality measures in radiomics methodology.