Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolization

Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review

BACKGROUND

Major society guidelines recommend transarterial chemoembolization (TACE) as the standard of care for intermediate-stage hepatocellular carcinoma (HCC) patients. However, predicting treatment response remains challenging.

AIMS

As artificial intelligence (AI) may predict therapeutic responses, this systematic review aims to assess the performance and effectiveness of radiomics and AI-based models in predicting TACE outcomes in patients with HCC.

METHODS

A systemic search was conducted on Medline and Embase databases from inception to 7th April 2024. Included studies generated a predictive model for TACE response and evaluated its performance by area under the curve (AUC), specificity, or sensitivity analysis. Systematic reviews, meta-analyses, case series and reports, pediatric, and animal studies were excluded. Secondary search of references of included articles ensured comprehensiveness.

RESULTS

64 articles, with 13,412 TACE-treated patients, were included. AI in pre-treatment CT scans provided value in predicting the efficacy of TACE in HCC treatment. A positive association was observed for AI in pre-treatment MRI scans. Models incorporating radiomics had numerically better performance than those incorporating manual measured radiological variables. 39 studies demonstrated that combined predictive models had numerically better performance than models based solely on imaging or non-imaging features.

CONCLUSION

A combined predictive model incorporating clinical features, laboratory investigations, and radiological characteristics may effectively predict response to TACE treatment for HCC.

Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow

PURPOSE

AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance.

MATERIALS AND METHODS

The study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard.

RESULTS

AI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80-0.84 vs. 0.81-0.82) and decreased inter-reader variability (median Dice 0.85-1.0 vs. 0.80-0.82; ICC 0.84 vs. 0.80), compared to manual segmentation.

CONCLUSION

The findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation.

Early post-treatment MRI predicts long-term hepatocellular carcinoma response to radiation segmentectomy

OBJECTIVES

Radiation segmentectomy using yttrium-90 plays an emerging role in the management of early-stage HCC. However, the value of early post-treatment MRI for response assessment is uncertain. We assessed the value of response criteria obtained early after radiation segmentectomy in predicting long-term response in patients with HCC.

MATERIALS AND METHODS

Patients with HCC who underwent contrast-enhanced MRI before, early, and 12 months after radiation segmentectomy were included in this retrospective single-center study. Three independent radiologists reviewed images at baseline and 1st follow-up after radiation segmentectomy and assessed lesion-based response according to mRECIST, LI-RADS treatment response algorithm (TRA), and image subtraction. The endpoint was response at 12 months based on consensus readout of two separate radiologists. Diagnostic accuracy for predicting complete response (CR) at 12 months based on the 1st post-treatment MRI was calculated.

RESULTS

Eighty patients (M/F 60/20, mean age 67.7 years) with 80 HCCs were assessed (median size baseline, 1.8 cm [IQR, 1.4-2.9 cm]). At 12 months, 74 patients were classified as CR (92.5%), 5 as partial response (6.3%), and 1 as progressive disease (1.2%). Diagnostic accuracy for predicting CR was fair to good for all readers with excellent positive predictive value (PPV): mRECIST (range between 3 readers, accuracy: 0.763-0.825, PPV: 0.966-1), LI-RADS TRA (accuracy: 0.700-0.825, PPV: 0.983-1), and subtraction (accuracy: 0.775-0.825, PPV: 0.967-1), with no difference in accuracy between criteria (p range 0.053 to > 0.9).

CONCLUSION

mRECIST, LI-RADS TRA, and subtraction obtained on early post-treatment MRI show similar performance for predicting long-term response in patients with HCC treated with radiation segmentectomy.

CLINICAL RELEVANCE STATEMENT

Response assessment extracted from early post-treatment MRI after radiation segmentectomy predicts complete response in patients with HCC with high PPV (≥ 0.96).

KEY POINTS

• Early post-treatment response assessment on MRI predicts response in patients with HCC treated with radiation segmentectomy with fair to good accuracy and excellent positive predictive value. • There was no difference in diagnostic accuracy between mRECIST, LI-RADS, and subtraction for predicting HCC response to radiation segmentectomy.

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

Response Assessment of Primary Liver Tumors to Novel Therapies: an Imaging Perspective

The latest developments in cancer immunotherapy, namely the introduction of immune checkpoint inhibitors, have led to a fundamental change in advanced cancer treatments. Imaging is crucial to identify tumor response accurately and delineate prognosis in immunotherapy-treated patients. Simultaneously, advances in image acquisition techniques, notably functional and molecular imaging, have facilitated more accurate pretreatment evaluation, assessment of response to therapy, and monitoring for tumor recurrence. Traditional approaches to assessing tumor progression, such as RECIST, rely on changes in tumor size, while new strategies for evaluating tumor response to therapy, such as the mRECIST and the EASL, rely on tumor enhancement. Moreover, the assessment of tumor volume, enhancement, cellularity, and perfusion are some novel techniques that have been investigated. Validation of these novel approaches should rely on comparing their results with those of standard evaluation methods (EASL, mRECIST) while considering the ultimate outcome, which is patient survival. More recently, immunotherapy has been used in the management of primary liver tumors. However, little is known about its efficacy. This article reviews imaging modalities and techniques for assessing tumor response and survival in immunotherapy-treated patients with primary hepatic malignancies.

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

Role of multislice computed tomography 3D volumetric analysis in the assessment of the therapeutic response of hepatocellular carcinoma after transarterial chemoembolization

Background

Volumetric analysis is a novel radiological technique used in the measurement of target lesions in three dimensions in order to estimate the lesion’s volume. Recently, it has been used for evaluation of the remaining liver volume after partial hepatectomy and also for evaluation of the response of tumours to treatment. It has been proven to be more accurate than the standard one or two-dimensional measurements, and it is especially useful for the evaluation of complex tumours after radiological interventional methods when the use of standard methods is limited. In the current study, our aim was to evaluate the value of the three-dimensional (3D) volumetric method “Response Evaluation Criteria in Solid Tumours (vRECIST)” and to compare it with the non–three-dimensional methods (RECIST) and modified RECIST (mRECIST) in the assessment of the therapeutic response of hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE).

Results

A retrospective study was conducted on 50 patients with confirmed radiological or pathological diagnosis of hepatocellular carcinoma (HCC) who underwent TACE as the only interventional procedure and follwed up by triphasic CT 1 and 4 months after treatment. The study revealed a significant difference between mRECIST and vRECIST in the assessment of the therapeutic response of HCC after TACE, a weak agreement was found between both methods in the detection of complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD). Also, there was no significant agreement between mRECIST and vRECIST regarding the assessment by classifying the patients into responders or nonresponders.

Conclusion

Volumetric analysis is an effective method for measuring the HCC lesions and evaluating its response to locoregional treatment with a significant difference between vRECIST and mRECIST in the assessment of therapeutic response, which in turn help the interventional radiologist to decide the future treatments and change the therapeutic plans. Based on these results, we recommend vRECIST to be an essential part of the assessment of therapeutic response after locoregional therapy.

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).