Citations

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

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.

Purpose Light chain (AL) amyloidosis is caused by the accumulation of misfolded proteins, which induces the dysfunction of vital organs. NEOD001 is a monoclonal antibody targeting these misfolded proteins. We report interim data from a phase I/II dose-escalation/expansion study of NEOD001 in patients with AL amyloidosis and persistent organ dysfunction (NCT01707264). Patients and Methods Patients who had completed at least one previous anti–plasma cell-directed therapy, had partial hematologic response or better, and had persistent organ dysfunction received NEOD001 intravenously every 28 days. Dose levels of 0.5, 1, 2, 4, 8, 16, and 24 mg/kg were evaluated (3 + 3 study design). Primary objectives were to determine the maximum tolerated dose and the recommended dose for future studies and to evaluate safety/tolerability. Secondary and exploratory objectives included pharmacokinetics, immunogenicity, and organ responses on the basis of published consensus criteria. Results Twenty-seven patients were enrolled in seven cohorts (dose-escalation component). No drug-related serious adverse events (AEs), discontinuations because of drug-related AEs, dose-limiting toxicities, or antidrug antibodies were reported. The most frequent AEs were fatigue, upper respiratory tract infection, cough, and dyspnea. Recommended dosing was 24 mg/kg. Pharmacokinetics support intravenous dosing every 28 days. Of 14 cardiac-evaluable patients, eight (57%) met the criteria for cardiac response and six (43%) had stable disease. Of 15 renal-evaluable patients, nine (60%) met the criteria for renal response and six (40%) had stable disease. Conclusion Monthly infusions of NEOD001 were safe and well tolerated. Recommended future dosing was 24 mg/kg. Organ response rates compared favorably with those reported previously for chemotherapy. A phase II expansion is ongoing. A global phase III study (NCT02312206) has been initiated. Antibody therapy targeting misfolded proteins is a potential new therapy for the management of AL amyloidosis.