Scalable Clinical Annotation with Location Evidence (SCALE)

Abstract

Deep learning can mitigate the global radiologist shortage but its development requires large-scale annotated datasets. This study introduces SCALE (Scalable Clinical Annotation with Location Evidence), a fully automated method for generating voxel-level annotations. It uses location priors that are automatically extracted from medical reports, tracked biopsy coordinates, or provided anatomical sectors. We annotated a large-scale dataset comprising 17 896 cases from 16 562 patients across 24 hospitals in 10 countries and 2 continents, using both SCALE and a count-based weakly semisupervised learning (CWSSL) method. An optimized algorithm was developed and trained on these datasets. Evaluation with 1561 cases from 1561 patients across 19 hospitals in 3 countries and 1 continent showed the superiority of the model trained on the dataset with SCALE annotations, achieving a case-level area under the receiver operating characteristic curve of 0.856. This is ＋0.012 compared to supervised learning ( p = 0 . 02 ), ＋0.007 compared to training with CWSSL ( p = 0 . 12 ), and ＋0.006 compared to the Prostate Imaging: Cancer AI (PI-CAI) Ensemble AI System. These results demonstrate that automated, location-guided annotation enables scalable development of AI for clinically significant prostate cancer detection on MRI, surpassing previous methods, and paving the way for broader clinical deployment. • SCALE generates voxel cancer labels from reports, sectors, or biopsy location priors. • 17,896 MRI exams from 24 hospitals in 10 countries were used for development. • 1,561 MRI exams from 19 European sites in 3 countries were used for testing. • Training with SCALE yielded AUROC of 0.856, ＋0.012 versus supervised (p=0.02). • Large gain of ＋0.022 AUROC on PROMIS indicates strong cross-site generalization.

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