TY  - JOUR
T1  - Multimodal AI for Automated Staging of Neuroblastoma and Paediatric Sarcomas Using MRI, CT, and Reports
A1  - Viktoria Igorevna Zaytseva
A1  - Diana Vyacheslavovna Krivun
A1  - Roman Vladimirovich Ermolov
A1  - Anastasiia Romanovna Pronina
A1  - Evelina Ruslanovna Skripko
A1  - Vasilina Leonidovna Myshak
A1  - Zalina Adlanovna Gichchieva
A1  - Elena Nikolaevna Pavlenko
A1  - Alfira Menigulovna Kumratova
A1  - Lyana Ruslanovna Khastsaeva
JF  - Journal of Biochemical Technology
JO  - J Biochem Technol
SN  - 0974-2328
Y1  - 2026
VL  - 17
IS  - 2
DO  - 10.51847/yYe6E6ZE9Z
SP  - 156
EP  - 166
N2  - Neuroblastoma and paediatric sarcomas frequently present with distant metastases at diagnosis, making accurate staging critical for treatment planning. Manual staging based on MRI and CT is time‑consuming and subject to interobserver variability. A multimodal artificial intelligence system was developed for automated staging of neuroblastoma (n=50) and sarcomas (n=34) in children. The system comprises three deep learning modules: a U‑Net for tumour segmentation on MRI, a YOLOv8‑based detector for metastatic lesions on CT, and a RuBERT natural language processing module for extracting staging information from Russian‑language radiology reports. The reference standard was multidisciplinary tumour board decisions. Performance was evaluated on a single‑centre retrospective cohort of 84 patients aged 6 months to 17 years. The segmentation module achieved a mean Dice coefficient of 0.79 for neuroblastoma and 0.81 for sarcomas. The metastasis detector showed a sensitivity of 0.95 and a specificity of 0.85 for lung nodules. The integrated classifier correctly assigned the stage in 78 of 84 cases (overall accuracy 92.8%). The full analysis pipeline took 8 minutes per patient on average, compared with approximately 45 minutes for manual reading. Staging errors (6 cases) were mainly due to occult bone marrow micrometastases not visible on imaging or postoperative findings. Surgeons agreed with the AI‑generated 3D tumour models in 92% of cases. The proposed multimodal AI system achieved expert‑comparable accuracy for automated staging of neuroblastoma and paediatric sarcomas while significantly reducing analysis time. Limitations include the modest sample size, single‑centre design and imperfect specificity (0.85).
UR  - https://jbiochemtech.com/article/multimodal-ai-for-automated-staging-of-neuroblastoma-and-paediatric-sarcomas-using-mri-ct-and-repo-ffy8ecaxcdq5ky6
ER  -