Shiroishi, Mark S.

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.

The Quantitative Imaging Biomarkers Alliance Profile for dynamic susceptibility contrast (DSC) MRI and its longitudinal claims for the precision of a DSC MRI–derived quantitative imaging biomarker to assess the relative cerebral blood volume changes in gliomas are summarized.

Abstract INTRODUCTION DSC-MRI perfusion methods are commonly used to evaluate both primary and metastatic brain cancer with the creation of maps of relative cerebral blood volume (rCBV). Recently, guidelines were established to ensure a standard DSC-MRI acquisition protocol that reduces inter-site variability. The purpose of this study was to initiate the determination of rCBV benchmark values using the DSC-MRI consensus protocol in treatment-naïve brain metastases. METHODS Patients from three sites with treatment-naïve, contrast-enhancing brain metastases on MRI were considered for inclusion in this retrospective study. The MRIs included pre- and post-contrast T1w(T1+C) images obtained after administration of gadolinium-based contrast agent (GBCA) (0.1 mmol/kg), which serves as the recommended preload for the DSC-MRI data collection. A 2nd GBCA dose (0.1 mmol/kg) was administered 40-60sec after the collection of baseline GRE-EPI images using recommended settings (FA=60o, TE/TR=30ms/1100-1250ms) for 120s. Calibrated pre/post T1w difference maps (dT1) were used for delineation of enhancing lesion, and standardized (calibrated) rCBV (sRCBV) created. Mean sRCBV for metastases were compared to normal appearing white matter (NAWM) and treatment-naive glioblastoma (GBM) from a previous study. Pairwise comparisons were performed using the Mann-Whitney nonparametric test. RESULTS N=52 patients with primary histology of lung (n=27); breast (n=6); skin (n=7); gastrointestinal (GI: n=3) and genitourinary (GU: n=9) cancers were included in comparison to GBM (n=31). The mean sRCBV for all metastases (1.77+/-1.05) is significantly lower (p=0.0003) than for GBM (2.67+/-1.34) but with both statistically greater (p< 0.0001) than NAWM (0.706+/-0.163). Individually, lung (1.47+/- 0.61), breast (2.275+/-0.87), skin (2.10+/-1.22), GI (1.91+/- 0.64) and GU (2.01+/-0.63) mean sRCBV are statistically greater than for NAWM. CONCLUSION Using the consensus DSC-MRI acquisition protocol confirms use of sRCBV to identify biologically active, treatment-naive brain metastases setting benchmark values for future applications.