aceInvestigation on Automatic Classification of Bone, Lymph Node, and Local Lesions in CT Images of Prostate Cancer Patients
Accurate assessment of different types of prostate cancer (PCa) lesions is a crucial part of treatment planning. Following a computed tomography (CT) or a positron emission tomography (PET)/CT imaging scan, a significant amount of time is required from the clinicians to detect lesions and characterize them by their anatomical locations. To facilitate the development of new image analysis applications, we investigated the potential of automated classification of PCa lesions between bone metastases, lymph node met…
A gradient-boosted ML model using lesion coordinates, CT and automated anatomical segmentation features classified bone, lymph node and local prostate cancer lesions with 96.0% overall accuracy, which could cut the clinician time spent characterizing lesions.
The model handles bone, lymph node and local lesions well, but the authors say more research is needed to distinguish prostate cancer lesions outside these categories, and local lesion sensitivity dropped to 80.0% on the external test set.
Evidence
- Peer-reviewedJournal of Imaging Informatics in Medicine2026-10-16
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Truvace Impact Record TRV-2026-1931, v1: “Investigation on Automatic Classification of Bone, Lymph Node, and Local Lesions in CT Images of Prostate Cancer Patients.” Truvace, 2026-10-19. /record/TRV-2026-1931 (accessed at citation time). sha256 d206d334fe6f7f45…
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