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.

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 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.

Brain tumor segmentation on Multimodal MRI scans using EMAP Algorithm.

The utilization of digital images is becoming popular in multiple areas such as clinical applications. There are multiple diagnostic and machine vision-based applications, where image processing plays a vital role in analyzing, interpreting, and solving the problem. Digital image processing techniques are used to increase the quality of images for human interpretation and machine perception. Tumor segmentation in brain magnetic resonance (MRI) volumes is considered as a complex task because of tumor shape, location, and texture. Manual segmentation is a time-consuming task that can be inaccurate due to an increasing volume of MR scanning performed. The goal of this research is to propose an automated method that can identify the whole tumor in each slice in volumetric MRI brain images, and find out the sub-tumor (core tumor, enhancing and non-enhancing) regions. The proposed algorithm is fully automated to segment out both high-grade glioma (HGG) and low-grade glioma (LGG), using the information provided by a sequence of MRI volumes. The designed algorithm does not require any training database and estimates the tumor regions independently using image processing techniques based on expectation maximization and K-mean clustering. The method is evaluated on BRATS 2015 dataset of LGG and HGG MR volumes. The average DICE score achieved by using the proposed technique is 0.92 and is comparable to state-of-the-art techniques which rely on computationally expensive algorithms.

Electronic cigarettes use and perception amongst medical students: a cross sectional survey from Sindh, Pakistan.

OBJECTIVE: The manufacturers of electronic cigarettes (e-cigarettes) are actively marketing their product through electronic and social media. Undergraduate medical students are expected to have better knowledge and awareness as they directly interact with patients in their training, The purpose of this study is therefore, to determine knowledge, use and perception regarding e-cigarettes among medical students from Sindh, Pakistan. RESULTS: A cross-sectional study was conducted between 1st July and 30th September 2016 at five different medical colleges situated in the second largest province of Sindh, Pakistan. The data was collected through a structured, self-administered questionnaire. Of the 500 students, the mean age was 21.5 ± 1.7 years and 58% were females. Over (65.6%) students were aware of e-cigarettes, 31 (6.2%) reported having used e-cigarettes, of whom 6 (1.2%) self-reported daily use. Users of conventional tobacco products were significantly more likely to have heard of e-cigarettes (87.6% vs 51.6%, p < 0.001) and having used them (13.9% vs 1.3%, p < 0.001). On multivariable logistic regression analysis we found a strong association of e-cigarette use with consumption of conventional cigarettes [OR: 10.6, 95% CI 3.6-30.8, p < 0.001], use of smokeless tobacco products [OR: 7.9, 95% CI 2.7-23.4, p < 0.001] however a weak association was observed for Shisha use [OR: 3.05, 95% CI 0.9-9.6, p = 0.05].

Brain tumor segmentation using cascaded deep convolutional neural network.

Gliomas are the most common and threatening brain tumors with little to no survival rate. Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have irregular shape and can be spatially located anywhere in the brain, which makes it a challenging task to segment them accurately enough for clinical purposes. In this paper, an automated segmentation algorithm for brain tumor using deep convolutional neural networks (DCNN) is proposed. Deep networks tend to have a lot of parameters thus over-fitting is almost always an issue especially when data are sparse. Max-out and drop-out layers are used to reduce the chances of over-fitting since data are scant. Patch based training method is used for the model where two types of patches sized 37×37 and 19×19 with same center pixel are selected. The proposed algorithm includes preprocessing in which images are normalized and bias field corrected, and post processing where small false positives are removed using morphological operators. BRATS 2013 dataset is used for evaluation of the proposed method, where it outperforms state-of-the-art methods with similar settings in key performance indicators.