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Deep learning-based diagnostic performance increases with more annotateddata, but large-scale manual annotations are expensive and labour-intensive.Experts evaluate diagnostic images during clinical routine, and write theirfindings in reports. Leveraging unlabelled exams paired with clinical reportscould overcome the manual labelling bottleneck. We hypothesise that detectionmodels can be trained semi-supervised with automatic annotations generatedusing model predictions, guided by sparse information from clinical reports. Todemonstrate efficacy, we train clinically significant prostate cancer (csPCa)segmentation models, where automatic annotations are guided by the number ofclinically significant findings in the radiology reports. We included 7,756prostate MRI examinations, of which 3,050 were manually annotated. We evaluatedprostate cancer detection performance on 300 exams from an external centre withhistopathology-confirmed ground truth. Semi-supervised training improvedpatient-based diagnostic area under the receiver operating characteristic curvefrom $87.2 \pm 0.8\%$ to $89.4 \pm 1.0\%$ ($P<10^{-4}$) and improvedlesion-based sensitivity at one false positive per case from $76.4 \pm 3.8\%$to $83.6 \pm 2.3\%$ ($P<10^{-4}$). Semi-supervised training was 14$\times$ moreannotation-efficient for case-based performance and 6$\times$ moreannotation-efficient for lesion-based performance. This improved performancedemonstrates the feasibility of our training procedure. Source code is publiclyavailable at github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCadetection algorithm is available atgrand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/.

Annotation-efficient cancer detection with report-guided lesion annotation for deep learning-based prostate cancer detection in bpMRI