Clinical Validation of Segmentation-Based Detection of Glioma Progression

Purpose To evaluate whether an AI-based method could be used routinely as part of patient care to assist in detecting non-enhancing glioma progression. Materials and Methods A 3D U-Net trained (n=481) and validated (n=121) to segment post-surgical lower grade gliomas was used to measure tumor volumes over time and assess progression in a clinical test set. Eight prospective and eight retrospective patients (total 72 exams) who were suspected of progression during their routine outpatient imaging were clinically assessed. Gold standards for progression were derived from clinical reports a posteriori using visual read, and radiologists were blinded to the AI decision at time of reporting. Results Progression assessments were presented to radiologists via an easy-to-use, interactive, and interpretable environment in under 10 minutes. Combining prospective and retrospective cases, a final sensitivity of 0.72 and specificity of 0.75 was achieved at progression detection. Conclusions Automated detection of glioma progression would provide valuable decision support for routine use. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This works was supported by the Precision Imaging of Cancer and Therapy program of the Helen Diller Family Cancer Center at UCSF. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All participants gave written informed consent, which included access to their medical record and MRI scans, and ethical approval was granted by the UCSF Institutional Review Board. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data that support the findings of this study are available from the corresponding author upon reasonable request.