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.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa).

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.

Effect of histology on stereotactic body radiotherapy for non-small cell lung cancer oligometastatic pulmonary lesions.

Background: Stereotactic body radiotherapy (SBRT) is commonly used to provide targeted treatment to metastatic lung disease. Investigation is needed to understand the influence of histology on treatment outcomes. We report how tumor histology affects local control (LC) in a cohort of patients with non-small cell lung cancer (NSCLC) receiving SBRT for oligometastatic and recurrent pulmonary lesions. Methods: Patients who received SBRT to recurrent or oligometastatic NSCLC pulmonary lesions from 2015-2019 at our institution were included in this retrospective cohort study. Minimum follow-up was 2 months. Kaplan-Meier (KM) analysis was performed to assess local progression-free survival (LPFS). Local failure cumulative incidence curves using death as a competing risk factor were also generated. Results: A total of 147 treated lesions from 83 patients were included: 95 lesions from 51 patients with lung adenocarcinoma and 52 lesions from 32 patients with lung squamous cell carcinoma (SqCC). Median follow-up was 23 [interquartile range (IQR): 9.5-44.5] months for adenocarcinoma, and 11.5 (6-32.25) months for SqCC. Two-year LC was 89% for adenocarcinoma and 77% for SqCC (P=0.04). Median overall survival (OS) was 24.5 (10-46.25) months for adenocarcinoma and 14.5 (7.75-23.25) months for SqCC. Adenocarcinoma had improved LPFS over SqCC (P=0.014). SqCC was associated with increased local failure risk that approached statistical significance (P=0.061) with death as a competing risk. Overall toxicity incidence was 8.2% with no G3+ toxicities. Conclusions: For SBRT-treated oligometastatic or recurrent NSCLC pulmonary lesions, adenocarcinoma histology is associated with improved 2-year LC and LPFS compared to SqCC and reduced incidence of local recurrence (LR) with death as a competing risk.

The role of adjuvant chemotherapy in the management of acute promyelocytic leukemia differentiation syndrome.

Acute Promyelocytic Leukemia (APL) is characterized by the t(15;17) chromosomal translocation resulting in a PML-RARA fusion protein. The all-trans-retinoic acid (ATRA) and Arsenic Trioxide (ATO) only regimens have demonstrated success in treating low- and intermediate-risk patients. However, induction with ATRA/ATO only regimens have been showing increased incidence of differentiation syndrome (DS), a potentially lethal complication, traditionally treated with dexamethasone. We conducted a three-institution retrospective study, aiming to evaluate the role of short-term adjuvant chemotherapy in managing moderate DS for patients with low- or intermediate-risk APL initially treated with ATRA/ATO only protocols. We evaluated the difference in incidence and duration of moderate DS in APL patients who were treated with ATRA/ATO with or without adjuvant chemotherapy. 57 low- or intermediate-risk APL patients were retrospectively identified and included for this study; 36 patients received ATRA/ATO only induction treatment, and 21 patients received ATRA/ATO/adjuvant chemotherapy combination induction therapy. Similar proportions of patients experienced DS in both groups (66.7% vs. 81.0%, P = 0.246). The median duration of DS resolution in patients receiving ATRA/ATO only was 17 days (n = 23), and in patients receiving combination therapy was 8 days (n = 16) (P = 0.0001). The lengths of hospital stay in patients receiving ATRO/ATO only was 38 days (n = 7), and in patients receiving combination therapy was 14 days (n = 17) (P = 0.0007). In conclusion, adding adjuvant chemotherapy to ATRA/ATO only protocol may reduce the duration of DS and the length of hospital stay during APL induction treatment.

Recurrent Solitary Fibrous Tumor (Intracranial Hemangiopericytoma) Treated With a Novel Combined-Modality Radiosurgery Technique: A Case Report and Review of the Literature.

Solitary Fibrous Tumor (SFT) is a rare and aggressive mesenchymal malignancy of the dura with a predilection for recurrence after treatment. We report a case of a SFT initially treated with subtotal surgical resection followed by a combination of Gamma Knife (GK) and linear accelerator-based radiosurgery. Forty-four days post-resection, the tumor had demonstrated radiographic evidence of recurrent disease within the post-operative bed. GK radiosurgery treatment was delivered in a "four-matrix" fashion targeting the entire surgical cavity as well as three nodular areas within this wide field. This treatment was delivered in one fraction with a stereotactic head frame for immobilization. A consolidation radiosurgery treatment course was then delivered over three additional fractions to the resection bed using a linear accelerator and mesh mask for immobilization. The total biologically effective dose (BED) was calculated as 32.50 Gy to the surgical bed and approximately 76.50 Gy to each nodular area. Almost three years post-operatively, the patient is alive and without radiographic or clinical evidence of disease recurrence. To our knowledge, no prior experiences have documented treatment of SFT using a mixed-modality, multi-fraction radiosurgery technique like the method detailed in this report. Our experience describes a combined modality, multi-fraction radiosurgery approach to treating recurrent SFT that maximizes radiation dose to the targets while minimizing complication risk. We believe this novel radiosurgery method should be considered in cases of grade II SFT post-resection.

Is There a Role for Prone Computed Tomography Before Percutaneous Nephrolithotomy?

Background: The majority of percutaneous nephrolithotomies (PCNLs) are performed prone, whereas most preoperative CT scans are done supine. The purpose of this pilot study is to determine if there is utility of prone CT scans in preoperative planning for prone PCNL by identifying patient populations at risk for organ injury and tract length-related complications. Materials and Methods: To represent typical preoperative planning using CT, two-dimensional (2D)-axial-prone/supine percutaneous tract measurements were performed by minimizing the distance from the target calix to the posterior-lateral skin in a single axial plane. The minimum distance and organ interception rates for the 2D-axial planning scans were recorded. Results: A total of 60 CT colonography and 13 CT urography patients were included in analysis. There were 42 women and 31 men with unspecified pathology reports ranging in age from 27 to 86 years and in body mass index (BMI) from 17.1 to 49. Multiple logistic regression identified female gender and low BMI as predictors of organ interception on the left. On multiple linear regression comparing the difference in axial prone/supine lengths; BMI, gender, and age were not significant independent predictors of changes in tract length in any pole when prone vs supine. However, shorter supine tracts tended to lengthen when prone, and longer supine tracts tended to shorten. Conclusions: This pilot study has demonstrated that patients with long and short estimates of tract length in the supine position may have shorter and longer tracts, respectively, with repositioning to prone. Thus, prone CT may have benefit when anticipating exceptionally long (>15 cm) tract lengths. Prone scans also revealed more potential organ interceptions, particularly for low BMI and women in the left upper pole. In patients for whom prone CT demonstrates an organ interception, the urologist should consider an alternate target calix or ultrasound-guided percutaneous access to identify the most appropriate needle trajectory.

A Case of Primary Signet-Ring Cell Cervical Carcinoma Treated with Chemoradiation, Brachytherapy, and Adjuvant Hysterectomy.

Primary signet-ring cell carcinoma of the uterine cervix is a rare subtype of cervical mucinous adenocarcinoma. Approximately 20 cases of primary signet-ring cell carcinoma of the cervix have been reported. Pathologic examination shows that adenocarcinomas with mucin accumulation in intracytoplasmic vacuoles displacing the nucleus indicate signet-ring cell carcinoma. A thorough metastatic workup is needed both for staging and to rule out gastrointestinal tract origin. Due to the rarity of the disease, both the true incidence and optimal management are unknown. Herein, the authors present a case of stage 1B3 primary signet-ring cell cervical carcinoma treated with combined chemotherapy and radiation (including external beam radiation and brachytherapy), followed by resection for residual disease. This case is consistent with limited reports where all surviving patients received surgery as well as 1 surviving patient with bulky disease required with chemoradiation and adjuvant hysterectomy.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.