Training With Local Data Remains Important for Deep Learning MRI Prostate Cancer Detection

Clinical Snapshot

Key Findings

• P Value: P < 0.002 for LOCAL-TRAIN and CENTRAL-TRAIN vs PICAI-TRAIN

• Effect Size: LOCAL-TRAIN: 66 [60-72] vs PICAI-TRAIN: 58 [51-64] vs CENTRAL-TRAIN: 65 [58-71]

• Primary Outcome: PICAI Score for prostate cancer segmentation accuracy

• Nnt Or Sensitivity: Only 22% (309/1400) of local training data needed to match external dataset performance

• Confidence Interval: 95% confidence intervals provided for all comparisons

Clinical Application

Requires substantial local dataset collection (300+ cases) and technical expertise for model training, potentially challenging for smaller centres Highly relevant for Australian radiology practices considering AI implementation. Supports local training initiatives and may inform TGA approval pathways for AI medical devices. Aligns with RANZCR AI strategy recommendations Radiology departments implementing AI for prostate cancer MRI interpretation, particularly relevant for Australian public and private imaging centres

Abstract

OBJECTIVES: Domain shift has been shown to have a major detrimental effect on AI model performance however prior studies on domain shift for MRI prostate cancer segmentation have been limited to small, or heterogenous cohorts. Our objective was to assess whether prostate cancer segmentation models trained on local MRI data continue to outperform those trained on external data with cohorts exceeding 1000. METHODS: We simulated a multi-institutional consortium using the public PICAI dataset (PICAI-TRAIN: 1241 exams, PICAI-TEST: 259) and a local dataset (LOCAL-TRAIN: 1400 exams, LOCAL-TEST: 308). IRB approval was obtained and consent waived. We compared nnUNet-v2 models trained on the combined data (CENTRAL-TRAIN) and separately on PICAI-TRAIN and LOCAL-TRAIN. Accuracy was evaluated using the open source PICAI Score on LOCAL-TEST. Significance was tested using bootstrapping. RESULTS: Just 22% (309/1400) of LOCAL-TRAIN exams would be sufficient to match the performance of a model trained on PICAI-TRAIN. The CENTRAL-TRAIN performance was similar to LOCAL-TRAIN performance, with PICAI Scores [95% CI] of 65 [58-71] and 66 [60-72], respectively. Both of these models exceeded the model trained on PICAI-TRAIN alone which had a score of 58 [51-64] (P < .002). Reducing training set size did not alter these relative trends. CONCLUSION: Domain shift limits MRI prostate cancer segmentation performance even when training with over 1000 exams from 3 external institutions. Use of local data is paramount at these scales.

References

1. 1.Carere, S. G., Jewell, J., Nasute Fauerbach, P. V., Emerson, D. B., Finelli, A., Ghai, S., & Haider, M. A. (2026). Training with local data remains important for deep learning MRI prostate cancer detection. Canadian Association of Radiologists Journal, Article 08465371251367620. https://doi.org/10.1177/08465371251367620