A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

Selective immune suppression using interleukin-6 receptor inhibitors for management of immune-related adverse events.

BACKGROUND: Management of immune-related adverse events (irAEs) is important as they cause treatment interruption or discontinuation, more often seen with combination immune checkpoint inhibitor (ICI) therapy. Here, we retrospectively evaluated the safety and effectiveness of anti-interleukin-6 receptor (anti-IL-6R) as therapy for irAEs. METHODS: We performed a retrospective multicenter study evaluating patients diagnosed with de novo irAEs or flare of pre-existing autoimmune disease following ICI and were treated with anti-IL-6R. Our objectives were to assess the improvement of irAEs as well as the overall tumor response rate (ORR) before and after anti-IL-6R treatment. RESULTS: We identified a total of 92 patients who received therapeutic anti-IL-6R antibodies (tocilizumab or sarilumab). Median age was 61 years, 63% were men, 69% received anti-programmed cell death protein-1 (PD-1) antibodies alone, and 26% patients were treated with the combination of anti-cytotoxic T lymphocyte antigen-4 and anti-PD-1 antibodies. Cancer types were primarily melanoma (46%), genitourinary cancer (35%), and lung cancer (8%). Indications for using anti-IL-6R antibodies included inflammatory arthritis (73%), hepatitis/cholangitis (7%), myositis/myocarditis/myasthenia gravis (5%), polymyalgia rheumatica (4%), and one patient each with autoimmune scleroderma, nephritis, colitis, pneumonitis and central nervous system vasculitis. Notably, 88% of patients had received corticosteroids, and 36% received other disease-modifying antirheumatic drugs (DMARDs) as first-line therapies, but without adequate improvement. After initiation of anti-IL-6R (as first-line or post-corticosteroids and DMARDs), 73% of patients showed resolution or change to ≤grade 1 of irAEs after a median of 2.0 months from initiation of anti-IL-6R therapy. Six patients (7%) stopped anti-IL-6R due to adverse events. Of 70 evaluable patients by RECIST (Response Evaluation Criteria in Solid Tumors) V.1.1 criteria; the ORR was 66% prior versus 66% after anti-IL-6R (95% CI, 54% to 77%), with 8% higher complete response rate. Of 34 evaluable patients with melanoma, the ORR was 56% prior and increased to 68% after anti-IL-6R (p=0.04). CONCLUSION: Targeting IL-6R could be an effective approach to treat several irAE types without hindering antitumor immunity. This study supports ongoing clinical trials evaluating the safety and efficacy of tocilizumab (anti-IL-6R antibody) in combination with ICIs (NCT04940299, NCT03999749).

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.

Clinical monitoring of metastatic disease to the brain can be a laborious and timeconsuming process, especially in cases involving multiple metastases when the assessment is performed manually. The Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) guideline, which utilizes the unidimensional longest diameter, is commonly used in clinical and research settings to evaluate response to therapy in patients with brain metastases. However, accurate volumetric assessment of the lesion and surrounding peri-lesional edema holds significant importance in clinical decision-making and can greatly enhance outcome prediction. The unique challenge in performing segmentations of brain metastases lies in their common occurrence as small lesions. Detection and segmentation of lesions that are smaller than 10 mm in size has not demonstrated high accuracy in prior publications. The brain metastases challenge sets itself apart from previously conducted MICCAI challenges on glioma segmentation due to the significant variability in lesion size. Unlike gliomas, which tend to be larger on presentation scans, brain metastases exhibit a wide range of sizes and tend to include small lesions. We hope that the BraTS-METS dataset and challenge will advance the field of automated brain metastasis detection and segmentation.

A general skull stripping of multiparametric brain MRIs using 3D convolutional neural network.

Accurate skull stripping facilitates following neuro-image analysis. For computer-aided methods, the presence of brain skull in structural magnetic resonance imaging (MRI) impacts brain tissue identification, which could result in serious misjudgments, specifically for patients with brain tumors. Though there are several existing works on skull stripping in literature, most of them either focus on healthy brain MRIs or only apply for a single image modality. These methods may be not optimal for multiparametric MRI scans. In the paper, we propose an ensemble neural network (EnNet), a 3D convolutional neural network (3DCNN) based method, for brain extraction on multiparametric MRI scans (mpMRIs). We comprehensively investigate the skull stripping performance by using the proposed method on a total of 15 image modality combinations. The comparison shows that utilizing all modalities provides the best performance on skull stripping. We have collected a retrospective dataset of 815 cases with/without glioblastoma multiforme (GBM) at the University of Pittsburgh Medical Center (UPMC) and The Cancer Imaging Archive (TCIA). The ground truths of the skull stripping are verified by at least one qualified radiologist. The quantitative evaluation gives an average dice score coefficient and Hausdorff distance at the 95th percentile, respectively. We also compare the performance to the state-of-the-art methods/tools. The proposed method offers the best performance.The contributions of the work have five folds: first, the proposed method is a fully automatic end-to-end for skull stripping using a 3D deep learning method. Second, it is applicable for mpMRIs and is also easy to customize for any MRI modality combination. Third, the proposed method not only works for healthy brain mpMRIs but also pre-/post-operative brain mpMRIs with GBM. Fourth, the proposed method handles multicenter data. Finally, to the best of our knowledge, we are the first group to quantitatively compare the skull stripping performance using different modalities. All code and pre-trained model are available at: https://github.com/plmoer/skull_stripping_code_SR .