Scalable Clinical Annotation with Location Evidence (SCALE).
Authors
Affiliations (14)
Affiliations (14)
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Health & Information Technology, Ziekenhuisgroep Twente, Almelo, The Netherlands; Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands. Electronic address: [email protected].
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
- Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Minimally Invasive Image-Guided Intervention Center, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
- Minimally Invasive Image-Guided Intervention Center, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
- Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Crete, Greece; Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), Heraklion, Crete, Greece.
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy; Department of Surgical Sciences, University of Turin, Turin, Italy.
- Computational Clinical Imaging Group, Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal; Department of Radiology, Royal Marsden Hospital, Sutton, United Kingdom.
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.
- Department of Radiology, Ziekenhuisgroep Twente, Hengelo, Netherlands; Department of Multi-Modality Medical Imaging, Technical Medical Centre, University of Twente, Enschede, Netherlands.
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway.
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands; Department of Radiology, University Medical Center Groningen, Groningen, The Netherlands.
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, United States.
- Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany.
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
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.