Krishnan A. Radiotherapy dosage: A neural network approach for uninvolved liver dose in stereotactic body radiation therapy for liver cancer. World J Gastrointest Oncol 2025; 17(2): 101888 [PMID: PMC11756014 DOI: 10.4251/wjgo.v17.i2.101888]
Corresponding Author of This Article
Arunkumar Krishnan, MD, MS, Assistant Professor, Research Scientist, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Suite 70100, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
Research Domain of This Article
Oncology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Oncol. Feb 15, 2025; 17(2): 101888 Published online Feb 15, 2025. doi: 10.4251/wjgo.v17.i2.101888
Radiotherapy dosage: A neural network approach for uninvolved liver dose in stereotactic body radiation therapy for liver cancer
Arunkumar Krishnan
Arunkumar Krishnan, Department of Supportive Oncology, Atrium Health Levine Cancer, Charlotte, NC 28204, United States
Author contributions: Krishnan A contributed to the concept of the study, drafted the manuscript, and performed the critical revision for important intellectual content.
Conflict-of-interest statement: No relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Arunkumar Krishnan, MD, MS, Assistant Professor, Research Scientist, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Suite 70100, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
Received: September 29, 2024 Revised: November 7, 2024 Accepted: December 2, 2024 Published online: February 15, 2025 Processing time: 110 Days and 14.1 Hours
Core Tip
Core Tip: A study by Zhang et al developed a neural network-based predictive model for estimating doses to uninvolved liver tissue during stereotactic body radiation therapy (RT), representing a significant advancement in personalizing RT for liver cancer patients. The model demonstrated high predictive accuracy, with R-values exceeding 0.8, highlighting its potential to standardize dose estimation and improve patient safety by reducing biases. The study's relatively small patient cohort (114 patients) raises concerns about selection bias and limits the model's generalizability. Future research should involve larger multicenter cohorts and a more comprehensive cohort of patient characteristics to improve the generalizability of models and clinical relevance. Interdisciplinary collaboration among oncologists, data scientists, and radiation technologists is vital for improving predictive models and the efficacy and precision of cancer treatment.
