Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study.

Background: Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS). Methods: We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers of 53 (internal cohort) and 16 (external cohort) DMG patients. We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features. Results: For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS. Conclusions: Machine learning analysis of MRI radiomics has potential to accurately and non-invasively predict which pediatric patients with DMG will survive less than one year from the time of diagnosis to provide patient stratification and guide therapy.

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.

Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging.

Children with optic pathway gliomas (OPGs), a low-grade brain tumor associated with neurofibromatosis type 1 (NF1-OPG), are at risk for permanent vision loss. While OPG size has been associated with vision loss, it is unclear how changes in size, shape, and imaging features of OPGs are associated with the likelihood of vision loss. This paper presents a fully automatic framework for accurate prediction of visual acuity loss using multi-sequence magnetic resonance images (MRIs). Our proposed framework includes a transformer-based segmentation network using transfer learning, statistical analysis of radiomic features, and a machine learning method for predicting vision loss. Our segmentation network was evaluated on multi-sequence MRIs acquired from 75 pediatric subjects with NF1-OPG and obtained an average Dice similarity coefficient of 0.791. The ability to predict vision loss was evaluated on a subset of 25 subjects with ground truth using cross-validation and achieved an average accuracy of 0.8. Analyzing multiple MRI features appear to be good indicators of vision loss, potentially permitting early treatment decisions.Clinical relevance- Accurately determining which children with NF1-OPGs are at risk and hence require preventive treatment before vision loss remains challenging, towards this we present a fully automatic deep learning-based framework for vision outcome prediction, potentially permitting early treatment decisions.

The Diagnostic Accuracy of N-Terminal Pro-B-Type Natriuretic Peptide and Soluble ST2 for Heart Failure in Chronic Kidney Disease Patients: A Comparative Analysis.

BACKGROUND N-terminal proatrial natriuretic peptide (NT-proBNP) levels are often markedly elevated in patients with chronic kidney disease (CKD). Identifying novel biomarkers is an important step toward effective diagnosis. Interleukin-1 receptor-like 1 (IL1RL1) protein and human/Soluble suppression of tumorigenesis-2 (sST2) are promising biomarkers for heart failure (HF). This study aimed to assess the trend of NT-proBNP and sST2 in chronic kidney disease and their diagnostic value for HF. MATERIAL AND METHODS This study was carried out on 420 patients who were divided into a no heart failure group (N=182) and a heart failure group (N=238). Spearman correlation analysis was used to test the association of sST2 and NT-proBNP with renal function. The diagnostic value of each biomarker was assessed using receiver operating characteristic (ROC) curves according to 3 different forms: Total group (n=420), non-CKD group (n=217), and CKD group (n=203). RESULTS A striking correlation between eGFR and NT-proBNP (r=-0.525; P<0.001) seemed to be far stronger than that with sST2 (r=-0.147; P<0.05). The optimum cutoff points for sST2 and NT-proBNP to detect HF were 28.960 ng/mL and 1280 pg/mL, respectively, in total, 28.71 ng/mL and 481 pg/mL, respectively, in non-CKD patients, and 30.55 ng/mL and 3314 pg/mL, respectively, in CKD patients. The combined model of sST2 and NT-proBNP was superior to the model of sST2 or NT-proBNP alone, and the difference was statistically significant (P<0.05). CONCLUSIONS The diagnostic value of sST2 is less affected by decreased renal function. sST2 combined with NT-proBNP may improve the diagnostic accuracy of HF.

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.

An MRTF-A-ZEB1-IRF9 axis contributes to fibroblast-myofibroblast transition and renal fibrosis.

Myofibroblasts, characterized by the expression of the matricellular protein periostin (Postn), mediate the profibrogenic response during tissue repair and remodeling. Previous studies have demonstrated that systemic deficiency in myocardin-related transcription factor A (MRTF-A) attenuates renal fibrosis in mice. In the present study, we investigated the myofibroblast-specific role of MRTF-A in renal fibrosis and the underlying mechanism. We report that myofibroblast-specific deletion of MRTF-A, achieved through crossbreeding Mrtfa-flox mice with Postn-CreERT2 mice, led to amelioration of renal fibrosis. RNA-seq identified zinc finger E-Box binding homeobox 1 (Zeb1) as a downstream target of MRTF-A in renal fibroblasts. MRTF-A interacts with TEA domain transcription factor 1 (TEAD1) to bind to the Zeb1 promoter and activate Zeb1 transcription. Zeb1 knockdown retarded the fibroblast-myofibroblast transition (FMyT) in vitro and dampened renal fibrosis in mice. Transcriptomic assays showed that Zeb1 might contribute to FMyT by repressing the transcription of interferon regulatory factor 9 (IRF9). IRF9 knockdown overcame the effect of Zeb1 depletion and promoted FMyT, whereas IRF9 overexpression antagonized TGF-ß-induced FMyT. In conclusion, our data unveil a novel MRTF-A-Zeb1-IRF9 axis that can potentially contribute to fibroblast-myofibroblast transition and renal fibrosis. Screening for small-molecule compounds that target this axis may yield therapeutic options for the mollification of renal fibrosis.

Bioorthogonal Lanthanide Molecular Probes for Near-Infrared Fluorescence and Mass Spectrometry Imaging.

We report here the development of clickable and highly near-infrared (NIR) fluorescent lanthanide (Ln) complexes for bioorthogonal labeling of biomolecules. These azide- or alkyne-functionalized Ln complexes are hydrophilic and fluorogenic, exhibiting a strong increase of NIR fluorescence upon conjugation with biomolecules. Metabolic labeling of biomolecules with azide or alkyne, followed by click labeling with the Ln complexes, enables NIR fluorescence (NIRF) imaging of DNA, RNA, proteins, and glycans in cells. Furthermore, multicolor imaging is performed by combining click-labeling with the Ln complexes and immunostaining. In addition, the Ln complexes is compatible with click-expansion microscopy (click-ExM), which enables high-resolution NIRF imaging of cellular glycoproteins. Finally, the Ln complexes can be used for time-of-flight secondary-ion mass spectrometry (ToF-SIMS) imaging, thus achieving the first example of dual-modal imaging combining NIRF and SIMS microscopies.

Bioinspired Design of seco-Chlorin Photosensitizers to Overcome Phototoxic Effects in Photodynamic Therapy.

Photodynamic therapy (PDT) is a non-invasive treatment modality against a range of cancers and nonmalignant diseases, however one must be aware of the risk of causing phototoxic reactions after treatment. We herein report a bioinspired design of next-generation photosensitizers (PSs) that not only effectively produce ROS but undergo fast metabolism after treatment to overcome undesirable side effects. We constructed a series of ß-pyrrolic ring-opening seco-chlorins, termed beidaphyrin (BP), beidapholactone (BPL), and their zinc(II) derivatives (ZnBP and ZnBPL), featuring intense near-infrared absorption and effective O2 photosensitization. Irradiation of ZnBPL led to a non-cytotoxic, metabolizable beidaphodiacetamide (ZnBPD) via in situ generated O2.- but not 1 O2 , as revealed by mechanistic studies including time-resolved absorption, kinetics, and isotope labeling. Furthermore, water-soluble ZnBPL showed an effective therapeutic outcome, fast metabolism, and negligible phototoxic reactions.