MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
Washington University in St. Louis（圣路易斯华盛顿大学）；Mallinckrodt Institute of Radiology（马林克罗特放射研究所）；Washington University School of Medicine（华盛顿大学医学院）；Institute for Informatics（信息学研究所）
Satrajit Chakrabarty, Pamela LaMontagne, Joshua Shimony, Daniel S. Marcus, Aristeidis Sotiras
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774). A 2.5D hybrid convolutional neural network was proposed to simultaneously localize the tumor and classify its molecular status by leveraging imaging features from MR scans and prior knowledge features from clinical records and tumor location. The models were tested on one internal (TCGA) and two external (WUSM and EGD) test sets. For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform a 'virtual biopsy' for tailoring treatment planning and overall clinical management of gliomas.
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine（佩雷尔曼医学院）；COMSATS University Islamabad（COMSATS伊斯兰堡大学）
Cancer histology reveals disease progression and associated molecular processes, and contains rich phenotypic information that is predictive of outcome. In this paper, we developed a computational approach based on deep learning to predict the overall survival and molecular subtypes of glioma patients from microscopic images of tissue biopsies, reflecting measures of microvascular proliferation, mitotic activity, nuclear atypia, and the presence of necrosis. Whole-slide images from 663 unique patients [IDH: 333 IDH-wildtype, 330 IDH-mutants, 1p/19q: 201 1p/19q non-codeleted, 129 1p/19q codelet…
University of California San Francisco（加州大学旧金山分校）；University of Pennsylvania（宾夕法尼亚大学）
Here we present the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 500 subjects with histopathologically-proven diffuse gliomas who were imaged with a standardized 3 Tesla preoperative brain tumor MRI protocol featuring predominantly 3D imaging, as well as advanced diffusion and perfusion imaging techniques. The dataset also includes isocitrate dehydrogenase (IDH) mutation status for all cases and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status for World Health Organization (WHO) grade III and…
Indian Statistical Institute（印度统计研究所）；University of Calcutta（加尔各答大学）；University of Genova（热那亚大学）
Glioma constitutes 80% of malignant primary brain tumors and is usually classified as HGG and LGG. The LGG tumors are less aggressive, with slower growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy being challenging for brain tumor patients, noninvasive imaging techniques like Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing brain tumors. Therefore automated systems for the detection and prediction of the grade of tumors based on MRI data becomes necessary for assisting doctors in the framework of augmented intelligence. In this paper, we t…
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning
Cancer Research Institute, Central South University（中南大学肿瘤研究所）；School of Physics & Electronic Science, Changsha University of Science & Technology（长沙理工大学物理与电子科学学院）；IFLYTEK Research（iFlytek研究院）；School of Life Sciences, Central South University（中南大学生命科学学院）；Department of Radiology, Xiangya Hospital, Central South University（中南大学湘雅医院放射科）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Department of Neurosurgery, Xiangya Hospital, Central South University（中南大学湘雅医院神经外科）
In the WHO glioma classification guidelines grade, IDH mutation and 1p19q co-deletion play a central role as they are important markers for prognosis and optimal therapy planning. Therefore, we propose a fully automatic, MRI based, 3D pipeline for glioma segmentation and classification. The designed segmentation network was a 3D U-Net achieving an average whole tumor dice score of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the multi-task classification network. The network was trained and evaluated on a large heterogeneous dataset of 628 patients, collected from The Ca…
Brain Tumor Classification on MRI in Light of Molecular Markers
Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA Department of Electrical \& Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA University of Maryland, Baltimore County, MD, USA；Princeton University, Princeton NJ 08544, USA, , WWW home page: http://users/ iekeland/web/welcome.html Universit\' e de Paris-Sud, Laboratoire d'Analyse Num\' e rique, B\ a timent 425, F-91405 Orsay Cedex, France
University of Cambridge（剑桥大学）；Nanyang Technological University（南洋理工大学）；University of Bath（巴斯大学）
Glioma is a common malignant brain tumor with distinct survival among patients. The isocitrate dehydrogenase (IDH) gene mutation provides critical diagnostic and prognostic value for glioma. It is of crucial significance to non-invasively predict IDH mutation based on pre-treatment MRI. Machine learning/deep learning models show reasonable performance in predicting IDH mutation using MRI. However, most models neglect the systematic brain alterations caused by tumor invasion, where widespread infiltration along white matter tracts is a hallmark of glioma. Structural brain network provides an ef…
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Erasmus MC, University Medical Center Rotterdam（鹿特丹大学医学中心伊拉斯谟MC）；Amsterdam UMC, University of Amsterdam（阿姆斯特丹大学医学中心阿姆斯特丹UMC）；GE HealthCare（GE医疗）；Erasmus MC Cancer Institute（伊拉斯谟MC癌症研究所）；Medical Delta（医疗三角洲）
A Benchmark of (MRI-) Foundation Models to Predict IDH Mutational Status in Glioma
Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern（伯尔尼大学医院因塞尔斯皮塔尔放射肿瘤科）；University of Bern（伯尔尼大学）；Department of Digital Medicine, University of Bern（伯尔尼大学数字医学系）；School of Life Sciences, FHNW University of Applied Sciences and Arts, Muttenz（FHNW应用科学与艺术大学生命科学学院）；School of Business, FHNW University of Applied Sciences and Arts, Olten（FHNW应用科学与艺术学院商学院）；University Children’s Hospital Basel UKBB（巴塞尔儿童医院UKBB）
Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam（伊拉斯谟医学中心鹿特丹大学医学中心放射科和核医学科生物医学成像组鹿特丹分部）；Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam（伊拉斯谟医学中心鹿特丹大学医学中心放射科和核医学科）；Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam（伊拉斯谟医学中心鹿特丹大学医学中心脑肿瘤中心神经外科）；Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute（伊拉斯谟癌症研究所脑肿瘤中心神经科）；Department of Neurosurgery, Haaglanden Medical Center（海牙 Haaglanden 医疗中心神经外科）；Department of Radiology, Haaglanden Medical Center（海牙 Haaglanden 医疗中心放射科）；Department of Neurosurgery, Cancer Center Amsterdam, Brain Tumor Center, Amsterdam UMC（阿姆斯特丹大学医学中心阿姆斯特丹癌症中心脑肿瘤中心神经外科）
Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is a time-consuming task. Leveraging the latest GPU capabilities, we developed a single multi-task convolutional neural network that uses the full 3D, structural, pre-operative MRI scans to can predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultaneously segmenting the tumor. We trained our method using the largest, most diverse patient cohort to date containing 1508 glioma patients from 16…
Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis
Background: Distinction of IDH mutant and wildtype GBMs is challenging on MRI, since conventional imaging shows considerable overlap. While few studies employed deep-learning in a mixed low/high grade glioma population, a GBM-specific model is still lacking in the literature. Our objective was to develop a deep-learning model for IDH prediction in GBM by using Convoluted Neural Networks (CNN) on multiparametric MRI. Methods: We included 100 adult patients with pathologically proven GBM and IDH testing. MRI data included: morphologic sequences, rCBV and ADC maps. Tumor area was obtained by a bo…
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes. We used preoperative imaging and genomic data of 110 patients from 5 institutions with lower-grade gliomas from The Cancer Genome Atlas. Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional …
Indiana University School of Medicine（印第安纳大学医学院）；Purdue University（普渡大学）；Dayton Children’s Hospital（代顿儿童医院）
Background and Purpose: Biopsy is the main determinants of glioma clinical management, but require invasive sampling that fail to detect relevant features because of tumor heterogeneity. The purpose of this study was to evaluate the accuracy of a voxel-wise, multiparametric MRI radiomic method to predict features and develop a minimally invasive method to objectively assess neoplasms. Methods: Multiparametric MRI were registered to T1-weighted gadolinium contrast-enhanced data using a 12 degree-of-freedom affine model. The retrospectively collected MRI data included T1-weighted, T1-weighted ga…
Towards Machine Learning-based Quantitative Hyperspectral Image Guidance for Brain Tumor Resection
University of British Columbia（不列颠哥伦比亚大学）；University Hospital of Münster（明斯特大学医院）；Macquarie University（麦考瑞大学）；Hokkaido Medical Center, National Hospital Organization（国立医院机构北海道医疗中心）
