AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation

Background: Brain tumor magnetic resonance imaging (MRI) reporting and tumor segmentation for treatment planning are time-consuming and variable. This retrospective fixed-sequence paired workflow study evaluates whether AI assistance is associated with changes in efficiency, consistency, and reproducibility. Methods: Thirty MRI cases (10 vestibular schwannomas, 10 meningiomas, 10 brain metastases) were assessed. Two neuroradiologists completed diagnostic reporting with and without AI assistance, and two physicians completed tumor delineation with and without AI-generated preliminary contours after a 3-week washout. Results: Reporting time decreased from 42.94 to 27.90 min for Reader A and from 101.04 to 80.47 min for Reader B, corresponding to median paired case-level reductions of 40.39% and 11.51%, respectively; only Reader A reached statistical significance. Sensitivity remained 97.73% and 100.00%, while precision was numerically higher after AI assistance (89.58% to 97.73% and 83.02% to 91.67%). Report-similarity metrics increased across ROUGE-L, BERTScore F1, and Sentence-BERT cosine similarity (all p < 0.001). Contouring time decreased from 54.63 to 4.93 min for Reader 1 and from 184.44 to 44.19 min for Reader 2, with median paired reductions of 100.00% and 87.11%. Dice coefficients were numerically higher after AI assistance (0.81 to 0.87 and 0.83 to 0.87). Conclusions: AI assistance was associated with shorter task-completion times, higher report-similarity metrics, and numerically higher contour-overlap measures. Prospective validation should determine whether these workflow efficiency gains translate into broader clinical benefit.