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Perspective

Voxel-Based Dose–Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective

Division of Cancer Sciences, The University of Manchester, Paterson Building, Wilmslow Road, Manchester M20 4BX, UK
J. Clin. Med. 2025, 14(20), 7248; https://doi.org/10.3390/jcm14207248 (registering DOI)
Submission received: 27 August 2025 / Revised: 3 October 2025 / Accepted: 9 October 2025 / Published: 14 October 2025
(This article belongs to the Special Issue Recent Developments of Radiotherapy in Oncology)

Abstract

This perspective paper critically examines the emerging role of voxel-based analysis (VBA), also referred to as image-based data mining (IBDM), in dose–toxicity modeling for post-radiotherapy toxicity assessment. These techniques offer promising insights into localized organ subregions associated with toxicity, yet their current application faces substantial methodological and validation challenges. Based on prior studies and practical experience, we highlight seven key limitations: (i) lack of clinical validation for dose–toxicity models, (ii) strong dependence of results on statistical method selection (parametric vs. nonparametric), (iii) insensitivity of commonly used tests to uniform dose scaling, (iv) influence of tail selection (one- vs. two-tailed tests) on statistical power, (v) frequent misapplication of permutation testing, (vi) reliance on dose as the sole predictor while neglecting patient-, treatment-, and genomic-level covariates, and (vii) misinterpretation of voxel-wise associations as causal in the absence of appropriate causal inference frameworks. Collectively, these limitations can obscure clinically relevant dose differences, inflate false-positive or false-negative findings, obscure effect direction, introduce confounded associations, and ultimately yield inconsistent identification of high-risk subregions in organs at risk and poor reproducibility across studies. Notably, current univariable VBA/IBDM approaches should be regarded as hypothesis-generating rather than clinical decision-making tools, as unvalidated findings risk premature translation into clinical practice. Advancing personalized radiotherapy requires rigorous outcome validation, integration of multivariable and causal modeling strategies, and incorporation of clinical and genomic data. By moving beyond dose-only predictor models, VBA/IBDM can achieve greater biological relevance, reliability, and clinical utility, supporting more precise and individualized radiotherapy strategies.
Keywords: radiotherapy; dose–toxicity modeling; predictive modeling; dose–surface maps; dose–toxicity maps; statistics; voxel-based analysis; image-based data mining radiotherapy; dose–toxicity modeling; predictive modeling; dose–surface maps; dose–toxicity maps; statistics; voxel-based analysis; image-based data mining

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MDPI and ACS Style

Puri, T. Voxel-Based Dose–Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective. J. Clin. Med. 2025, 14, 7248. https://doi.org/10.3390/jcm14207248

AMA Style

Puri T. Voxel-Based Dose–Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective. Journal of Clinical Medicine. 2025; 14(20):7248. https://doi.org/10.3390/jcm14207248

Chicago/Turabian Style

Puri, Tanuj. 2025. "Voxel-Based Dose–Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective" Journal of Clinical Medicine 14, no. 20: 7248. https://doi.org/10.3390/jcm14207248

APA Style

Puri, T. (2025). Voxel-Based Dose–Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective. Journal of Clinical Medicine, 14(20), 7248. https://doi.org/10.3390/jcm14207248

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