Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.

ABSTRACT:

Objectives

To assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes and predict response to induction chemotherapy using histopathology as the reference standard.

Materials and methods

This retrospective, multicentre study included magnetic resonance imaging (MRI) scans from osteosarcoma patients acquired between January 2006 and July 2024. A 3D U-Net CNN segmented tumours and calculated volumes at baseline and post-chemotherapy. A second CNN predicted treatment response based on MRI-derived tumour volume changes using histopathologic necrosis (≥ 90%) as the reference standard. Both models were trained on 162 scans from 81 patients (Centre A) and validated on 40 scans from 20 patients (10 per c

SUBMITTER: von Kruchten R 

PROVIDER: S-EPMC12694974 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature