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 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.Comment: Technical report of BraSy

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 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

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

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. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls

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