Glioblastomas are the most common malignant brain tumors in adults. Approximately 200000 people die each year from Glioblastoma in the world. Glioblastoma patients have a median survival of 12 months with optimal therapy and about 4 months without treatment. Glioblastomas appear as heterogeneous necrotic masses with irregular peripheral enhancement, surrounded by vasogenic edema. The current standard of care includes surgical resection, radiotherapy and chemotherapy, which require accurate segmentation of brain tumor subregions. For effective treatment planning, it is vital to identify the methylation status of the promoter of Methylguanine Methyltransferase (MGMT), a positive prognostic factor for chemotherapy. However, current methods for brain tumor segmentation are tedious, subjective and not scalable, and current techniques to determine the methylation status of MGMT promoter involve surgically invasive procedures, which are expensive and time consuming. Hence there is a pressing need to develop automated tools to segment brain tumors and non-invasive methods to predict methylation status of MGMT promoter, to facilitate better treatment planning and improve survival rate. I created an integrated diagnostics solution powered by Artificial Intelligence to automatically segment brain tumor subregions and predict MGMT promoter methylation status, using brain MRI scans. My AI solution is proven on large datasets with performance exceeding current standards and field tested with data from teaching files of local neuroradiologists. With my solution, physicians can submit brain MRI images, and get segmentation and methylation predictions in minutes, and guide brain tumor patients with effective treatment planning and ultimately improve survival time.
The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
This paper proposes to use deep radiomic features (DRFs) from a convolutional neural network (CNN) to model fine-grained texture signatures in the radiomic analysis of recurrent glioblastoma (rGBM). We use DRFs to predict survival of rGBM patients with preoperative T1-weighted post-contrast MR images (n=100). DRFs are extracted from regions of interest labelled by a radiation oncologist and used to compare between short-term and long-term survival patient groups. Random forest (RF) classification is employed to predict survival outcome (i.e., short or long survival), as well as to identify hig…
Glioblastoma, an aggressive brain cancer, is amongst the most lethal of all cancers. Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) gene in glioblastoma tumor tissue is of clinical importance as it has a significant effect on the efficacy of Temozolomide, the primary chemotherapy treatment administered to glioblastoma patients. Currently, MGMT methylation is determined through an invasive brain biopsy and subsequent genetic analysis of the extracted tumor tissue. In this work, we present novel Bayesian classifiers that make probabilistic predictions of MGMT methylation status …
Future Processing Healthcare（未来医疗处理公司）；Silesian University of Technology（西里西亚工业大学）；Medical University of Silesia in Katowice（卡托维兹西里西亚医科大学）；ZSM Chorzów（霍茹夫ZSM医院）；University Clinical Centre（大学临床中心）；Roche Pharmaceutical Research & Early Development（罗氏制药研究与早期开发部）
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimens…
Glioblastoma Multiforme is a very aggressive type of brain tumor. Due to spatial and temporal intra-tissue inhomogeneity, location and the extent of the cancer tissue, it is difficult to detect and dissect the tumor regions. In this paper, we propose survival prognosis models using four regressors operating on handcrafted image-based and radiomics features. We hypothesize that the radiomics shape features have the highest correlation with survival prediction. The proposed approaches were assessed on the Brain Tumor Segmentation (BraTS-2020) challenge dataset. The highest accuracy of image feat…
Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks
Mathematics Area, mathLab, SISSA（SISSA数学区mathLab）；Dipartimento di Matematica e Fisica “N. Tartaglia”, Università Cattolica del Sacro Cuore（圣心大学N. Tartaglia数学与物理系）；Department of Neurosurgery – Fondazione I.R.C.C.S. Istituto Neurologico Carlo Besta（卡尔·贝斯塔神经科学研究所神经外科部）
Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes. Also, the weak interpretability of the model poses challenges to clinical application. Here we proposed a machine learning framework to semi-automatically fine-tune the clustering algorithms and quantitatively identify stable sub-re…
We proposed a fully automatic workflow for glioblastoma (GBM) survival prediction using deep learning (DL) methods. 285 glioma (210 GBM, 75 low-grade glioma) patients were included. 163 of the GBM patients had overall survival (OS) data. Every patient had four pre-operative MR scans and manually drawn tumor contours. For automatic tumor segmentation, a 3D convolutional neural network (CNN) was trained and validated using 122 glioma patients. The trained model was applied to the remaining 163 GBM patients to generate tumor contours. The handcrafted and DL-based radiomic features were extracted …
Case Western Reserve University（凯斯西储大学）；Cleveland Clinic（克利夫兰诊所）
A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging following conventional treatment. Previous studies have linked tumor lobar presence and laterality to GBM outcomes, suggesting that disease etiology and progression in GBM may be impacted by tumor location. Hence, in this feasibility study, we seek to investigate the following question: Can tumor location on treatment-naïve MRI provide early cues regarding likelihood of a patient developing pseudo-progression versus tumor…
Center for Biomedical Image Computing and Analytics（生物医学图像计算与分析中心）；University of Pennsylvania（宾夕法尼亚大学）；Comsats University Islamabad（COMSATS伊斯兰堡大学）；Wake Forest School of Medicine（维克森林医学院）；Bowman Gray Center for Medical Education（鲍曼·格雷医学教育中心）
Both radiographic (Rad) imaging, such as multi-parametric magnetic resonance imaging, and digital pathology (Path) images captured from tissue samples are currently acquired as standard clinical practice for glioblastoma tumors. Both these data streams have been separately used for diagnosis and treatment planning, despite the fact that they provide complementary information. In this research work, we aimed to assess the potential of both Rad and Path images in combination and comparison. An extensive set of engineered features was extracted from delineated tumor regions in Rad images, compris…
Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks
SINTEF Digital（SINTEF数字研究所）；Norwegian University of Science and Technology（挪威科技大学）；Amsterdam University Medical Centers（阿姆斯特丹大学医学中心）；Twee Steden Hospital（双城医院）；University College London（伦敦大学学院）
Department of Health Technology, Technical University of Denmark（丹麦技术大学健康技术系）；Department of Oncology, The Finsen Center, Rigshospitalet（哥本哈根大学医院芬森中心肿瘤学系）；Department of Clinical Physiology, Nuclear Medicine and PET, Center of Diagnostic Investigation, Rigshospitalet（哥本哈根大学医院诊断研究中心临床生理学、核医学与正电子发射断层扫描科）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院Athinoula A. Martinos生物医学成像中心）
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct biological interpretation: They measure the…
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively captur…
Decoding MGMT Methylation: A Step Towards Precision Medicine in Glioblastoma
National University of Computer and Emerging Sciences（国立计算机与新兴科学大学）；Oryx Universal College with Liverpool John Moores University（羚羊环球学院与利物浦约翰摩尔斯大学）；Al-Balqa Applied University（巴尔卡应用大学）
Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel 3D multi-path DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multi-modal pre-operative MR images. We hypothesized that the multi-path architecture could achieve more accurate segmentation than a single-path architecture. 258 GBM patients were included in this study. Each patient had four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multi…
College of Electronic Science and Engineering, Jilin University（吉林大学电子科学与工程学院）；Wake Forest School of Medicine（维克森林医学院）
Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. In the course of post-treatment magnetic resonance imaging (MRI), PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM. So, these similarities pose challenges on the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. In this paper, we introduce DC-AL GAN, a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet…
A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma
University Hospital Erlangen（埃尔兰根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔兰根-欧洲肿瘤研究所综合癌症中心）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Institute of Neuroradiology, University Hospital Erlangen（埃尔兰根大学医院神经放射学研究所）；Department of Neurosurgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希·亚历山大大学埃尔兰根大学医院神经外科）；Universität Würzburg（维尔茨堡大学）
Reutlingen University（罗伊特林根大学）；Menoufia University（梅努菲亚大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation. The Brain Tumor Segmentation Challenge (BraTS) has been a popular benchmark for automatic brain glioblastomas segmentation algorithms since its initiation. In this year, BraTS 2021 challenge provides the largest multi-parametric (mpMRI) dataset of 2,000 pre-operative patients. In this paper, we propose a new aggregation of two …
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
SINTEF Digital（挪威科技工业研究院数字分院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学（NTNU））；St. Olavs hospital, Trondheim University Hospital（特隆赫姆大学医院圣奥拉夫斯医院）
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. Th…
ARTORG Center for Biomedical Engineering Research, University of Bern（伯尔尼大学生物医学工程研究ARTORG中心）；University Hospital of Diabetology, Endocrinology, Nutritional Medicine and Metabolism (UDEM) Inselspital, University Hospital（糖尿病、内分泌、营养医学与代谢大学医院（UDEM）因塞尔医院）
Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve data accuracy and reduce both the burden on participants and health costs. In this paper, we propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake, by simply processing RGB Depth (RGB-D) image pairs captured before and after meal consumpt…
University of Wisconsin（威斯康星大学）；Henry Ford Health（亨利福特医疗集团）
Purpose: To answer the long-standing question of whether a model trained from a single clinical site can be generalized to external sites. Materials and Methods: 17,537 chest x-ray radiographs (CXRs) from 3,264 COVID-19-positive patients and 4,802 COVID-19-negative patients were collected from a single site for AI model development. The generalizability of the trained model was retrospectively evaluated using four different real-world clinical datasets with a total of 26,633 CXRs from 15,097 patients (3,277 COVID-19-positive patients). The area under the receiver operating characteristic curve…
Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
Department of Radiology, Fondazione Policlinico Campus Bio-Medico（放射学系，政策临床医学院）；Department of Radiology, Università Campus Bio-Medico di Roma（放射学系，罗马生物医学大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入系，辐射物理，生物医学工程，乌梅拉大学）
University of Bern（伯尔尼大学）；Bern University Hospital（伯尔尼大学医院）
Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve the data accuracy and reduce both the participant burden and the health costs. In this paper, we propose a novel system based on artificial intelligence to accurately estimate nutrient intake, by simply processing RGB depth image pairs captured before and after a meal consumption.…
Purpose: Artificial intelligence (AI) solutions for medical diagnosis require thorough evaluation to demonstrate that performance is maintained for all patient sub-groups and to ensure that proposed improvements in care will be delivered equitably. This study evaluates the robustness of an AI solution for the diagnosis of normal chest X-rays (CXRs) by comparing performance across multiple patient and environmental subgroups, as well as comparing AI errors with those made by human experts. Methods: A total of 4,060 CXRs were sampled to represent a diverse dataset of NHS patients and care settin…
Thomas Jefferson University（托马斯·杰斐逊大学）；University of Pennsylvania（宾夕法尼亚大学）
PURPOSE OF REVIEW: Despite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. RECENT FINDINGS: The most significant advances involve the automated detection of retinopathy of prematurity (ROP), y…
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine（佩雷尔曼医学院）
In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Previous work on characterizing the infiltrative heterogeneity in the peritumoral region used various imaging modalities, but information of extracellular free water movement restriction has been limitedly explored. Here, we derive a unique set of Artificial Intelligence (AI)-based mar…
We present a novel formulation for the calibration of a biophysical tumor growth model from a single-time snapshot, MRI scan of a glioblastoma patient. Tumor growth models are typically nonlinear parabolic partial differential equations (PDEs). Thus, we have to generate a second snapshot to be able to extract significant information from a single patient snapshot. We create this two-snapshot scenario as follows. We use an atlas (an average of several scans of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of the patient. Then, using the patient scan and the atlas, we c…
Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the orig…
Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina
Tabriz University of Medical Sciences（大不里士医科大学）；Nikookari Eye Center（尼库卡里眼科中心）；Tabriz Islamic Art University（大不里士伊斯兰艺术大学）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；National Heart Centre Singapore（新加坡国家心脏中心）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；The George Institute for Global Health（乔治全球健康研究所）；Sydney Medical School, University of Sydney（悉尼大学悉尼医学院）；School of Mathematics, Physics, and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Centre for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
University of Oulu（奥卢大学）；Oulu University Hospital（奥卢大学医院）；Oxipit UAB
Objectives: To assess the use of artificial intelligence-based software in ruling out chest X-ray cases, with no significant findings in a primary health care setting. Methods: In this retrospective study, a commercially available artificial intelligence (AI) software was used to analyse 10 000 chest X-rays of Finnish primary health care patients. In studies with a mismatch between an AI normal report and the original radiologist report, a consensus read by two board-certified radiologists was conducted to make the final diagnosis. Results: After the exclusion of cases not meeting the study cr…
Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognostic biomarkers for ccRCC. Methods: We proposed a weakly-supervised deep learning strategy using conventional histology of 1752 whole slide images from multiple centers. Our study was demonstrated through internal cross-validation and external validations for the deep learning-based models. Results: Automatic diagnosis for ccRCC through intelligent subtyping of renal cell carcinoma was proved in this study. Our graderis…
