Yang YH, Li Y. Prognostic value of early changes in artificial intelligence-based computed tomography-measured body composition in pediatric osteosarcoma receiving neoadjuvant chemotherapy. Artif Intell Cancer 2026; 7(1): 116464 [DOI: 10.35713/aic.v7.i1.116464]
Corresponding Author of This Article
Yuan Li, MD, Professor, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, Laboratory of Digestive Surgery, West China Hospital, Sichuan University, No. 17, Section 3, Renmin South Road, Chengdu 6100000, Sichuan Province, China. l13258389785@126.com
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Pediatrics
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Yang YH, Li Y. Prognostic value of early changes in artificial intelligence-based computed tomography-measured body composition in pediatric osteosarcoma receiving neoadjuvant chemotherapy. Artif Intell Cancer 2026; 7(1): 116464 [DOI: 10.35713/aic.v7.i1.116464]
Prognostic value of early changes in artificial intelligence-based computed tomography-measured body composition in pediatric osteosarcoma receiving neoadjuvant chemotherapy
Yu-Han Yang, Yuan Li
Yu-Han Yang, West China School of Medicine, Sichuan University, Chengdu 6100041, Sichuan Province, China
Yuan Li, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, Laboratory of Digestive Surgery, West China Hospital, Sichuan University, Chengdu 6100000, Sichuan Province, China
Author contributions: Yang YH and Li Y contributed to study conception and design; Yang YH contributed to data acquisition, analysis and interpretation, and manuscript draft; Li Y critical revised the manuscript. All authors have read and approved the final manuscript.
Institutional review board statement: This retrospective study involving human participants was reviewed and approved by the Institutional Review Boards of the participating institutions. All procedures were conducted in accordance with the ethical standards of the institutional and/or national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: De-identified individual participant data that underlie the results reported in this article are available from the corresponding author upon reasonable request. Data sharing is subject to approval by the relevant institutional review boards and execution of a data-use agreement to ensure protection of patient privacy and compliance with applicable regulations. Due to institutional policies and patient privacy considerations, raw imaging data or any data containing potentially identifying information will not be publicly released.
Corresponding author: Yuan Li, MD, Professor, State Key Laboratory of Biotherapy and Cancer Center, Department of Pediatric Surgery, Laboratory of Digestive Surgery, West China Hospital, Sichuan University, No. 17, Section 3, Renmin South Road, Chengdu 6100000, Sichuan Province, China. l13258389785@126.com
Received: November 12, 2025 Revised: December 4, 2025 Accepted: February 2, 2026 Published online: September 8, 2026 Processing time: 294 Days and 17.9 Hours
Abstract
BACKGROUND
For pediatric patients with osteosarcoma, nutritional status is associated with treatment response and survival outcome.
AIM
To explore computed tomography (CT)-derived body composition using artificial intelligence (AI)-based tissue segmentation in pediatric patients with osteosarcoma in order to identify possible predictors for response to neoadjuvant chemotherapy and overall survival (OS).
METHODS
In this study, the body composition of 133 patients aged ≤ 18 years with young osteosarcoma at the third lumbar vertebra level was analyzed on a retrospective dataset using an AI-based software tool (Visage Imaging). All patients received neoadjuvant chemotherapy and surgical resection who underwent abdominal CT scanning before and after chemotherapy. The primary outcome was OS, and the secondary outcome was response to neoadjuvant chemotherapy. The CT-derived factors, including skeletal muscle index, subcutaneous and visceral adipose tissue index, and skeletal muscle density (SMD), and other nutrition factor, such as systemic immune-inflammation index and prognostic nutritional index were collected before and after chemotherapy. Changes (Δ) in body composition parameters between pre-chemotherapy and post-chemotherapy CT were assessed. Logistic and Cox regression models were used to identify predictors of therapeutic response and OS, respectively. Statistical significance was defined as a two-sided P < 0.05.
RESULTS
On our results age at diagnosis, post-chemotherapy systemic immune-inflammation index, and ΔSMD had prognostic value for classification of responders and non-responders to neoadjuvant chemotherapy at multivariable logistic analysis. ΔSMD was found as an independent predictor for OS with a hazard ratio of 1.113 (95% confidence interval: 1.035-1.197, P = 0.004). At an optimal cut-off value of 6.27 Hounsfield unit per 30 days, the median OS with low ΔSMD vs high ΔSMD was 94.657 months vs 43.356 months (P = 0.001).
CONCLUSION
AI-based analysis of third lumbar vertebra body composition on CT images is feasible in pediatric patients receiving neoadjuvant chemotherapy. ΔSMD is a prognostic indicator that a great value significantly predicts the poor treatment response and short OS. Using CT-derived and other nutritional predictors may conduct optimized nutrition support and appropriate selection of individualized therapeutic regimen.
Core Tip: Using artificial intelligence based automated computed tomography analysis of the third lumbar vertebra body composition in 133 pediatric osteosarcoma patients, early reductions in skeletal muscle density during neoadjuvant chemotherapy were independently associated with poor histologic response and shorter overall survival. An integrated index combining age, postchemotherapy inflammatory indicator and change of skeletal muscle density improved prediction of treatment response and prognosis, suggesting that early computed tomography-derived muscle metrics can guide timely nutritional and rehabilitative interventions to potentially improve outcomes.
