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#artificial intelligence Open access

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
Sep 2026 · WArtificial Intelligence in Cancer · 29 references
Inflammatory Biomarkers in Disease Prognosis Nutrition and Health in Aging

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.

DOI
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