Rizvi, Tanvir

Introduction: Optimizing patient selection and time-to-intervention will be crucial to the success of future clinical trials seeking to implement therapies and improve intracerebral hemorrhage (ICH) patient outcomes. Automated and accurate ICH and perihematomal edema (PHE) volumetry carries significant potential to strengthen trials targeting ICH and PHE expansion. Aims: The Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage (QUANTUM) trial was designed with aims of (1) validating a fully automated artificial intelligence (AI) method for supratentorial, spontaneous ICH and PHE volumetry against the manual and semiautomated quantification methods and (2) establishing a model framework for the translation of AI technology through conduct of a clinical trial under the equivalence hypothesis paradigm. Methods: This trial was a prospective, randomized, clinical study with blinded outcome assessment. Six independent raters were randomized in a 1:1 ratio to ICH and PHE volumetry from non-contrast CT scans in the Virtual International Stroke Trials Archive with either the semiautomated or manual quantification method. The primary outcomes were equivalence between (1)ICH volume measurements on admission (<24h from symptom onset) for the manual versus the fully automated or the sem-automated quantification methods, and (2)PHE volume measurements on day 3 (72±12h from symptom onset) for the manual versus the fully automated or the semiautomated quantification methods. The required sample size was 126 ICH and 126 PHE measurements, based upon a significance level of 0.025 to account for the two equivalence tests. Results: The geometric mean relative discrepancies between the fully automated and the manual ICH and PHE volume measurements did not cross the pre-specified 10% equivalence margin. The geometric mean relative discrepancies between the semiautomated and the manual ICH and PHE volume measurements did cross the pre-specified 10% equivalence margin. The fully automated method (mean 10.0±2.7s/scan) was significantly faster than the manual (mean 189±199 s/scan; P <0.001) and semiautomated (mean 79±68s/scan; P <0.001) methods. Conclusions: ICH and PHE volumes quantified by AI could replace clinician measurements within a 10% error margin and with substantially greater efficiency. The semiautomated method could not replace manual measurements. This trial demonstrated feasibility of the equivalence hypothesis framework for AI validation studies.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.