Enabling earlier detection of spinal lesions in CT imaging with artificial intelligence-a case study.

This study investigates whether an artificial intelligence-based second reader can detect malignant spinal lesions on computed tomography earlier than radiologists, thereby supporting precision oncology through more timely diagnosis and treatment planning. The spine is one of the most common locations for metastases in advanced cancer, significantly influencing symptoms, staging and therapeutic decisions. Delayed or missed detection can, however, impair outcomes. A three-dimensional nnU-Net segmentation model trained on 653 scans was applied to a retrospective cohort of 200 patients who later received confirmed diagnoses of malignant spinal lesions; earlier examinations without documented disease were re-evaluated by one board-certified radiologist both unaided and with AI support. The primary measure was the proportion of malignant spinal lesions detected by the model before their baseline reporting, with AI-derived lead time as a secondary endpoint. The system identified 12 malignant spinal lesions that remained invisible to radiologists on unaided retrospective review, achieving a mean lead time of 228 days, and highlighted 25 additional malignant spinal lesions that were retrospectively visible but initially unreported. Across the cohort, AI flagged earlier findings in 37 patients. These preliminary results suggest that AI-assisted CT interpretation may have the potential to identify sub-visual or otherwise overlooked malignant spinal lesions at an earlier scan date than standard radiologist reporting. Further prospective studies are needed to determine whether these findings translate into clinical benefits such as improved diagnostic completeness, earlier treatment initiation, and better patient outcomes.