Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma

Prediction of treatment response and outcome of transarterial chemoembolization in patients with hepatocellular carcinoma using artificial intelligence: A systematic review of efficacy

PURPOSE

To perform a systematic literature review of the efficacy of different AI models to predict HCC treatment response to transarterial chemoembolization (TACE), including overall survival (OS) and time to progression (TTP).

METHODS

This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines until May 2, 2024.

RESULTS

The systematic review included 23 studies with 4,486 HCC patients. The AI algorithm receiver operator characteristic (ROC) area under the curve (AUC) for predicting HCC response to TACE based on mRECIST criteria ranged from 0.55 to 0.97. Radiomics-models outperformed non-radiomics models (AUCs: 0.79, 95 %CI: 0.75-0.82 vs. 0.73, 0.61-0.77, respectively). The best ML methods used for the prediction of TACE response for HCC patients were CNN, GB, SVM, and RF with AUCs of 0.88 (0.79-0.97), 0.82 (0.71-0.89), 0.8 (0.60-0.87) and 0.8 (0.55-0.96), respectively. Of all predictive feature models, those combining clinic-radiologic features (ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement) had higher AUCs compared with models based on clinical characteristics alone (0.79, 0.73-0.89; p = 0.04 for CT + clinical, 0.81, 0.75-0.88; p = 0.017 for MRI + clinical versus 0.6, 0.55-0.75 in clinical characteristics alone).

CONCLUSION

Integrating clinic-radiologic features enhances AI models' predictive performance for HCC patient response to TACE, with CNN, GB, SVM, and RF methods outperforming others. Key predictive clinic-radiologic features include ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement. Multi-institutional studies are needed to improve AI model accuracy, address heterogeneity, and resolve validation issues.

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.

All You Need to Know About TACE: A Comprehensive Review of Indications, Techniques, Efficacy, Limits, and Technical Advancement

Transcatheter arterial chemoembolization (TACE) is a proven and widely accepted treatment option for hepatocellular carcinoma and it is recommended as first-line non-curative therapy for BCLC B/intermediate HCC (preserved liver function, multifocal, no cancer-related symptoms) in patients without vascular involvement. Different types of TACE are available nowadays, including TAE, c-TACE, DEB-TACE, and DSM-TACE, but at present there is insufficient evidence to recommend one TACE technique over another and the choice is left to the operator. This review then aims to provide a comprehensive overview of the current literature on indications, types of procedures, safety, and efficacy of different TACE treatments.

Dynamic changes of radiological and radiomics patterns based on MRI in viable hepatocellular carcinoma after transarterial chemoembolization

OBJECTIVES

This study aims to analyze the magnetic resonance imaging (MRI) change patterns of viable hepatocellular carcinomas (HCCs) following the initial transarterial chemoembolization (TACE).

METHODS

A retrospective analysis of HCC patients' initial TACE from February 2015 to October 2022 across three centers and a clinical trial (NCT03113955) was conducted. The viability of residual HCCs at one and six months after TACE was evaluated using the LI-RADS Treatment Response Algorithm (LR-TRA) v2024. The radiological and radiomics features of post-TACE viable tumors between baseline and one-month, and between one- and six- months were compared using Wilcoxon signed-rank test and McNemar's test.

RESULTS

A total of 160 viable tumors were included in the study. Viable tumors at one month after TACE exhibited higher T1WI intensity (P =.024), lower T2WI intensity (P =.005), fewer washout features (P <.001), smaller size (P <.001), and higher ADC values (P <.001) compared to baseline HCC imaging.A significant reduction in DWI intensity (P =.002) and ADC values (P <.001) were observed in viable tumors at one month compared to those at six months. There were 82 (45.1%) radiomics features that changed significantly between the baseline and one-month. Only three radiomics features showed statistically significant difference of viable tumors between one- and six-month.

CONCLUSIONS

Compared to the baseline, viable HCCs after TACE demonstrated significant changes of imaging characteristics in a series of radiological and radiomics features at one- and six-month follow-ups.

CLINICAL RELEVANCE STATEMENT

Clinically diagnosing of viable HCCs using radiological methods is challenging. A comprehensive analysis of these imaging characteristics can facilitate the accurate identification of viable tumors.

Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.

Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence

Hepatocellular Carcinoma (HCC) is a condition associated with significant morbidity and mortality. The presence of Portal Vein Tumour Thrombus (PVTT) typically signifies advanced disease stages and poor prognosis. Artificial intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has emerged as a promising tool for extracting quantitative data from medical images. AI is increasingly integrated into the imaging omics workflow and has become integral to various medical disciplines. This paper provides a comprehensive review of the mechanisms underlying the formation and progression of PVTT, as well as its impact on clinical management and prognosis. Additionally, it outlines the advancements in AI for predicting the diagnosis of HCC and the development of PVTT. The limitations of existing studies are critically evaluated, and potential future research directions in the realm of imaging for the diagnostic prediction of HCC and PVTT are discussed, with the ultimate goal of enhancing survival outcomes for PVTT patients.

Role of peritumoral tissue analysis in predicting characteristics of hepatocellular carcinoma using ultrasound-based radiomics

Predicting the biological characteristics of hepatocellular carcinoma (HCC) is essential for personalized treatment. This study explored the role of ultrasound-based radiomics of peritumoral tissues for predicting HCC features, focusing on differentiation, cytokeratin 7 (CK7) and Ki67 expression, and p53 mutation status. A cohort of 153 patients with HCC underwent ultrasound examinations and radiomics features were extracted from peritumoral tissues. Subgroups were formed based on HCC characteristics. Predictive modeling was carried out using the XGBOOST algorithm in the differentiation subgroup, logistic regression in the CK7 and Ki67 expression subgroups, and support vector machine learning in the p53 mutation status subgroups. The predictive models demonstrated robust performance, with areas under the curves of 0.815 (0.683-0.948) in the differentiation subgroup, 0.922 (0.785-1) in the CK7 subgroup, 0.762 (0.618-0.906) in the Ki67 subgroup, and 0.849 (0.667-1) in the p53 mutation status subgroup. Confusion matrices and waterfall plots highlighted the good performance of the models. Comprehensive evaluation was carried out using SHapley Additive exPlanations plots, which revealed notable contributions from wavelet filter features. This study highlights the potential of ultrasound-based radiomics, specifically the importance of peritumoral tissue analysis, for predicting HCC characteristics. The results warrant further validation of peritumoral tissue radiomics in larger, multicenter studies.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.