Prognostic factors for relapse in patients with clinical stage I testicular non-seminoma: A nationwide, population-based cohort study

BACKGROUND: Approximately 30% of patients with clinical stage I non-seminoma (CSI-NS) relapse. Current risk stratification is based on lymphovascular invasion (LVI) alone. The extent to which additional tumor characteristics can improve risk prediction remains unclear.OBJECTIVE: To determine the most important prognostic factors for relapse in CSI-NS patients.DESIGN, SETTING, AND PARTICIPANTS: Population-based cohort study including all patients with CSI-NS diagnosed in Denmark between 2013 and 2018 with follow-up until 2022. Patients were identified in the prospective Danish Testicular Cancer database. By linkage to the Danish National Pathology Registry, histological slides from the orchiectomy specimens were retrieved.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Histological slides were reviewed blinded to the clinical outcome. Clinical data were obtained from medical records. The association between prespecified potential prognostic factors and relapse was assessed using Cox regression analysis. Model performance was evaluated by discrimination (Harrell's C-index) and calibration.RESULTS: Of 453 patients included, 139 patients (30.6%) relapsed during a median follow-up of 6.3 years. Tumor invasion into the hilar soft tissue of the testicular hilum, tumor size, LVI and embryonal carcinoma were independent predictors of relapse. The estimated 5-year risk of relapse ranged from &lt; 5% to &gt; 85%, depending on the number of risk factors. After internal model validation, the model had an overall concordance statistic of 0.75. Model calibration was excellent.CONCLUSION AND RELEVANCE: The identified prognostic factors provide a much more accurate risk stratification than current clinical practice, potentially aiding clinical decision-making.</p

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy

BackgroundAdaptive MRI-guided radiotherapy (MRIgRT) requires accurate and efficient segmentation of organs and targets on MRI scans. Manual segmentation is time-consuming and variable, while deformable image registration (DIR)-based contour propagation may not account for large anatomical changes. Therefore, we developed and evaluated an automatic segmentation method using the nnU-net framework.MethodsThe network was trained on 38 patients (76 scans) with localized prostate cancer and tested on 30 patients (60 scans) with localized prostate, metastatic prostate, or bladder cancer treated at a 1.5 T MRI-linac at our institution. The performance of the network was compared with the current clinical workflow based on DIR. The segmentation accuracy was evaluated using the Dice similarity coefficient (DSC), mean surface distance (MSD), and Hausdorff distance (HD) metrics.ResultsThe trained network successfully segmented all 600 structures in the test set. High similarity was obtained for most structures, with 90% of the contours having a DSC above 0.9 and 86% having an MSD below 1 mm. The largest discrepancies were found in the sigmoid and colon structures. Stratified analysis on cancer type showed that the best performance was seen in the same type of patients that the model was trained on (localized prostate). Especially in patients with bladder cancer, the performance was lower for the bladder and the surrounding organs. A complete automatic delineation workflow took approximately 1 minute. Compared with contour transfer based on the clinically used DIR algorithm, the nnU-net performed statistically better across all organs, with the most significant gain in using the nnU-net seen for organs subject to more considerable volumetric changes due to variation in the filling of the rectum, bladder, bowel, and sigmoid.ConclusionWe successfully trained and tested a network for automatically segmenting organs and targets for MRIgRT in the male pelvis region. Good test results were seen for the trained nnU-net, with test results outperforming the current clinical practice using DIR-based contour propagation at the 1.5 T MRI-linac. The trained network is sufficiently fast and accurate for clinical use in an online setting for MRIgRT. The model is provided as open-source

An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy

&lt;p&gt;Data related to the article:&lt;/p&gt;&lt;p&gt;Front. Oncol.&lt;/p&gt;&lt;p&gt;Sec. Radiation Oncology&lt;/p&gt;&lt;p&gt;Volume 13 - 2023 | doi: 10.3389/fonc.2023.1285725&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy&lt;/p&gt;&lt;p&gt;Ebbe Laugaard Lorenzen 1,2*, Bahar Celik 1, Nis Sarup1, Lars Dysager3, Rasmus Lübeck Christiansen1, Anders Smedegaard Bertelsen1, Uffe Bernchou1,2, Søren Nielsen Agergaard1, Maximilian Lukas Konrad1, Carsten Brink1,2*, Faisal Mahmood1,2, Tine Schytte2,3,&nbsp;Christina Junker Nyborg3&lt;/p&gt;&lt;p&gt;1 Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark&nbsp;&lt;/p&gt;&lt;p&gt;2 Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark&lt;/p&gt;&lt;p&gt;3 Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark&lt;/p&gt;&lt;p&gt;* Correspondence:&nbsp;&lt;/p&gt;&lt;p&gt;Ebbe Laugaard Lorenzen&lt;/p&gt;&lt;p&gt;[email protected]&lt;/p&gt;&lt;p&gt;Carsten Brink&nbsp;&lt;/p&gt;&lt;p&gt;[email protected]&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt