Clinical target volumes for glioma – Automated delineation to improve neuroanatomic consistency

作者：Gregory Buti, Marcela Giovenco, Tuğba Yılmaz, Ali Ajdari, Christopher P. Bridge, G Sharp, Fredrik Löfman, Helen A. Shih, Thomas Bortfeld · 发表于：Physics and Imaging in Radiation Oncology · 年份：2025 · DOI：10.1016/j.phro.2025.100865 · 被引用次数：1 · 研究领域：Glioma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

Background and purpose: Delineating clinical target volumes (CTVs) for glioma is challenging as consistency with the neuroanatomy needs to be carefully verified. We developed an automated approach that incorporates tumor infiltration pathways and anatomic barriers to improve the neuroanatomical consistency and efficiency of CTV delineation. Materials and methods: A deep learning model for brain structure segmentation was developed based on manual delineations of hemispheres, brainstem, cerebellum, optic chiasm, optic nerves, ventricles, and midline on CT images of ninety-nine glioma patients. Brain structures predictions are integrated into a constrained distance transform that defines the CTV as a 15-mm expansion of the gross tumor volume. Connecting structures with white matter tracts allow for expansions across different structure boundaries, e.g., cerebellum and brainstem connecting at the cerebellar peduncles. Results: Mean (±std) Dice Similarity Coefficient (DSC) for the hemispheres, brainstem, cerebel-lum, chiasm, optic nerves, midline and ventricles were (98.5 ± 0.8)%, (92.5 ± 2.8)%, (96.7 ± 2.2)% (63.9 ± 12.2)%, (83.8 ± 9.0)%, (81.2 ± 7.0) and (91.5 ± 3.9)%. Mean (±std) 95 % Hausdorff distance (HD95) were, in mm, 1.9 ± 2.5, 7.0 ± 5.4, 1.8 ± 1.2, 7.2 ± 3.2, 2.3 ± 1.0, 9.5 ± 10.5, and 3.8 ± 3.1, respectively. Auto-generated CTVs are compared against reference CTVs (15-mm expansion constrained by manually-contoured brain structures). The automatic CTVs showed excellent ...