Krishnan A. Imaging tumor heterogeneity to predict response to transarterial chemoembolization in hepatocellular carcinoma. World J Gastroenterol 2026; 32(39): 120606 [DOI: 10.3748/wjg.120606]
Corresponding Author of This Article
Arunkumar Krishnan, MD, Department of Supportive Oncology, Atrium Health Levine Cancer, Atrium Health Wake Forest Baptist Comprehensive Cancer Center, 1021 Morehead Medical Dr, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
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Oncology
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editorial
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Krishnan A. Imaging tumor heterogeneity to predict response to transarterial chemoembolization in hepatocellular carcinoma. World J Gastroenterol 2026; 32(39): 120606 [DOI: 10.3748/wjg.120606]
World J Gastroenterol. Oct 21, 2026; 32(39): 120606 Published online Oct 21, 2026. doi: 10.3748/wjg.120606
Imaging tumor heterogeneity to predict response to transarterial chemoembolization in hepatocellular carcinoma
Arunkumar Krishnan
Arunkumar Krishnan, Department of Supportive Oncology, Atrium Health Levine Cancer, Atrium Health Wake Forest Baptist Comprehensive Cancer Center, Charlotte, NC 28204, United States
Author contributions: Krishnan A conceptualized the manuscript and conducted the assessment, prepared the manuscript draft, which was subsequently reviewed and approved for final publication.
AI contribution statement: AI-based tool (Microsoft Copilot) was used in this manuscript for language refinement and editorial polishing - improving grammar and readability - consistent with most current publisher policies on AI-assisted language editing. No AI tool was used to generate the substantive analysis, conclusions, or scientific arguments or writing.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
Corresponding author: Arunkumar Krishnan, MD, Department of Supportive Oncology, Atrium Health Levine Cancer, Atrium Health Wake Forest Baptist Comprehensive Cancer Center, 1021 Morehead Medical Dr, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
Received: March 3, 2026 Revised: April 20, 2026 Accepted: May 25, 2026 Published online: October 21, 2026 Processing time: 187 Days and 2 Hours
Abstract
Magnetic resonance imaging (MRI)-based habitat imaging is a promising non-invasive approach for assessing intratumoral heterogeneity and predicting responses to transarterial chemoembolization (TACE) in hepatocellular carcinoma. A recent multicenter study by Lv et al published in the World Journal of Gastroenterology combined MRI-derived habitat features with clinical data to develop a predictive model that achieved area under the curve values of 0.97, 0.91, and 0.93 across different cohorts, outperforming single-modality models. The ecological diversity index from Gaussian mixture model clustering offers a new measure of tumor ecosystem complexity. However, there are methodological concerns, including potential information leakage, confounding factors from mixed TACE methods, misclassification of modified Response Evaluation Criteria in Solid Tumors outcomes due to imaging variability, and limited inter-site radiomics harmonization. The low transportability of the standalone clinical model (area under the curve values = 0.54, accuracy = 0.41) underscores significant variability across centers. Future efforts should focus on prospective validation with standardized imaging, integration of multiparametric MRI, multi-lesion modeling, radiogenomic correlation studies, and decision-impact trials to assess whether model-guided patient selection can improve survival outcomes. Addressing these challenges is essential to establishing habitat imaging as a reliable tool for personalized TACE in hepatocellular carcinoma.
Core Tip: The study by Lv et al integrated contrast-enhanced magnetic resonance imaging-derived habitat features, conventional radiomics, and clinical variables to predict early response to transarterial chemoembolization in hepatocellular carcinoma, achieving area under the curve values of 0.97 and 0.93 in the training and validation cohorts, respectively. While the ecological diversity index and multicenter design are meaningful advancements, several key methodological issues remain unresolved: Feature selection outside nested cross-validation, which risks data leakage and overly optimistic performance estimates; confounding from mixed protocols; outcome misclassification due to the use of both computed tomography and magnetic resonance imaging follow-up; and insufficient inter-site harmonization. Prospective validation, integration of multi-sequence imaging, and studies analyzing the impact on decision-making are necessary before implementing this in clinical practice.
