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World J Radiol. Apr 28, 2026; 18(4): 118196
Published online Apr 28, 2026. doi: 10.4329/wjr.v18.i4.118196
Published online Apr 28, 2026. doi: 10.4329/wjr.v18.i4.118196
Letter to the Editor: Magnetic resonance imaging-based deep learning radiomics for preoperative risk stratification in pediatric hepatoblastoma
Ujjayita Chowdhury, Atharva A Mahajan, Cancer Research Institute, Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai 410210, Maharashtra, India
Muthu Subash Kavitha, School of Information and Data Sciences, Nagasaki University, Nagasaki 852-8521, Japan
Ramya Lakshmi Rajendran, Byeong-Cheol Ahn, BK21 FOUR KNU Convergence Educational Program of Biomedical Sciences for Creative Future Talents, Department of Biomedical Sciences, School of Medicine, Kyungpook National University, Daegu 41944, South Korea
Ramya Lakshmi Rajendran, Prakash Gangadaran, Byeong-Cheol Ahn, Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu 41944, South Korea
Ramya Lakshmi Rajendran, Prakash Gangadaran, Byeong-Cheol Ahn, Cardiovascular Research Institute, Kyungpook National University, Daegu 41944, South Korea
Byeong-Cheol Ahn, Department of Nuclear Medicine, Kyungpook National University Hospital, Daegu 41944, South Korea
Co-first authors: Ujjayita Chowdhury and Atharva A Mahajan.
Co-corresponding authors: Prakash Gangadaran and Byeong-Cheol Ahn.
Author contributions: Chowdhury U, Mahajan AA, Kavitha MS, Rajendran RL, Gangadaran P, and Ahn BC designed the overall concept and outline of the manuscript, contributed to the discussion and design of the manuscript, and contributed to the writing and editing of the manuscript and review of the literature. Chowdhury U and Mahajan AA contributed equally to this work and considered as co-first authors. Gangadaran P and Ahn BC are designated as co-corresponding authors and were equally involved in the conceptualization and design of the study, critical writing and intellectual revision of the manuscript, overall supervision of the research, and final approval of the version to be published.
Conflict-of-interest statement: All authors report no relevant conflicts of interest for this article.
Corresponding author: Byeong-Cheol Ahn, MD, PhD, Professor, Department of Nuclear Medicine, School of Medicine, Kyungpook National University, 680, Gukchaebosang ro, Jung gu, Daegu 41944, South Korea. abc2000@knu.ac.kr
Received: December 28, 2025
Revised: January 21, 2026
Accepted: February 4, 2026
Published online: April 28, 2026
Processing time: 119 Days and 0.9 Hours
Revised: January 21, 2026
Accepted: February 4, 2026
Published online: April 28, 2026
Processing time: 119 Days and 0.9 Hours
Core Tip
Core Tip: Accurate preoperative risk stratification remains challenging in pediatric hepatoblastoma. This study demonstrates that a magnetic resonance imaging-based deep learning radiomics score predicts event-free survival and refines risk stratification beyond conventional clinical factors. Integration with pretreatment extension of disease stage and alpha-fetoprotein improves prognostic accuracy, supporting noninvasive, imaging-driven decision-making for individualized.