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

Deep learning-based diagnostic performance increases with more annotated data, but large-scale manual annotations are expensive and labour-intensive. Experts evaluate diagnostic images during clinical routine, and write their findings in reports. Leveraging unlabelled exams paired with clinical reports could overcome the manual labelling bottleneck. We hypothesise that detection models can be trained semi-supervised with automatic annotations generated using model predictions, guided by sparse information from clinical reports. To demonstrate efficacy, we train clinically significant prostate cancer (csPCa) segmentation models, where automatic annotations are guided by the number of clinically significant findings in the radiology reports. We included 7,756 prostate MRI examinations, of which 3,050 were manually annotated. We evaluated prostate cancer detection performance on 300 exams from an external centre with histopathology-confirmed ground truth. Semi-supervised training improved patient-based diagnostic area under the receiver operating characteristic curve from $87.2 \pm 0.8\%$ to $89.4 \pm 1.0\%$ ($P<10^{-4}$) and improved lesion-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$\times$ more annotation-efficient for case-based performance and 6$\times$ more annotation-efficient for lesion-based performance. This improved performance demonstrates the feasibility of our training procedure. Source code is publicly available at github.com/DIAGNijmegen/Report-Guided-Annotation. Best csPCa detection algorithm is available at grand-challenge.org/algorithms/bpmri-cspca-detection-report-guided-annotations/

Tailored anticoagulant treatment after a first venous thromboembolism: protocol of the Leiden Thrombosis Recurrence Risk Prevention (L-TRRiP) study - cohort-based randomised controlled trial

Introduction Patients with a first venous thromboembolism (VTE) are at risk of recurrence. Recurrent VTE (rVTE) can be prevented by extended anticoagulant therapy, but this comes at the cost of an increased risk of bleeding. It is still uncertain whether patients with an intermediate recurrence risk or with a high recurrence and high bleeding risk will benefit from extended anticoagulant treatment, and whether a strategy where anticoagulant duration is tailored on the predicted risks of rVTE and bleeding can improve outcomes. The aim of the Leiden Thrombosis Recurrence Risk Prevention (L-TRRiP) study is to evaluate the outcomes of tailored duration of long-term anticoagulant treatment based on individualised assessment of rVTE and major bleeding risks.Methods and analysis The L-TRRiP study is a multicentre, open-label, cohort-based, randomised controlled trial, including patients with a first VTE. We classify the risk of rVTE and major bleeding using the L-TRRiP and VTE-BLEED scores, respectively. After 3 months of anticoagulant therapy, patients with a low rVTE risk will discontinue anticoagulant treatment, patients with a high rVTE and low bleeding risk will continue anticoagulant treatment, whereas all other patients will be randomised to continue or discontinue anticoagulant treatment. All patients will be followed up for at least 2 years. Inclusion will continue until the randomised group consists of 608 patients; we estimate to include 1600 patients in total. The primary outcome is the combined incidence of rVTE and major bleeding in the randomised group after 2 years of follow-up. Secondary outcomes include the incidence of rVTE and major bleeding, functional outcomes, quality of life and cost-effectiveness in all patients.Ethics and dissemination The protocol was approved by the Medical Research Ethics Committee Leiden-Den Haag-Delft. Results are expected in 2028 and will be disseminated through peer-reviewed journals and during (inter)national conferences.Trial registration number NCT06087952