Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into ...imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985–June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging MRI (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.
•We reviewed Alzheimer’s disease neuroimaging-based classification studies.•We covered structural MRI, fMRI, DTI, amyloid-PET, FDG-PET, and multimodalities.•The reported studies were validated through appropriate cross-validation.•We categorized the studies based on feature extraction methods.•We discussed challenges, disparities in experimental conditions and future directions.
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The epidermal growth factor receptor variant III (
) mutation has been considered a driver mutation and therapeutic target in glioblastoma, the most common and aggressive brain cancer. Currently, ...detecting
requires postoperative tissue analyses, which are
and unable to capture the tumor's spatial heterogeneity. Considering the increasing evidence of
imaging signatures capturing molecular characteristics of cancer, this study aims to detect
in primary glioblastoma noninvasively, using routine clinically acquired imaging.
We found peritumoral infiltration and vascularization patterns being related to
status. We therefore constructed a quantitative within-patient peritumoral heterogeneity index (PHI/φ-index), by contrasting perfusion patterns of immediate and distant peritumoral edema. Application of φ-index in preoperative perfusion scans of independent discovery (
= 64) and validation (
= 78) cohorts, revealed the generalizability of this
imaging signature.
Analysis in both cohorts demonstrated that the obtained signature is highly accurate (89.92%), specific (92.35%), and sensitive (83.77%), with significantly distinctive ability (
= 4.0033 × 10
, AUC = 0.8869). Findings indicated a highly infiltrative-migratory phenotype for
tumors, which displayed similar perfusion patterns throughout peritumoral edema. Contrarily,
tumors displayed perfusion dynamics consistent with peritumorally confined vascularization, suggesting potential benefit from extensive peritumoral resection/radiation.
This
signature is potentially suitable for clinical translation, since obtained from analysis of clinically acquired images. Use of within-patient heterogeneity measures, rather than population-based associations, renders φ-index potentially resistant to inter-scanner variations. Overall, our findings enable noninvasive evaluation of
for patient selection for targeted therapy, stratification into clinical trials, personalized treatment planning, and potentially treatment-response evaluation.
.
Abstract Automatic classification of colon into normal and malignant classes is complex due to numerous factors including similar colors in different biological constituents of histopathological ...imagery. Therefore, such techniques, which exploit the textural and geometric properties of constituents of colon tissues, are desired. In this paper, a novel feature extraction strategy that mathematically models the geometric characteristics of constituents of colon tissues is proposed. In this study, we also show that the hybrid feature space encompassing diverse knowledge about the tissues׳ characteristics is quite promising for classification of colon biopsy images. This paper thus presents a hybrid feature space based colon classification (HFS-CC) technique, which utilizes hybrid features for differentiating normal and malignant colon samples. The hybrid feature space is formed to provide the classifier different types of discriminative features such as features having rich information about geometric structure and image texture. Along with the proposed geometric features, a few conventional features such as morphological, texture, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) are also used to develop a hybrid feature set. The SIFT features are reduced using minimum redundancy and maximum relevancy (mRMR). Various kernels of support vector machines (SVM) are employed as classifiers, and their performance is analyzed on 174 colon biopsy images. The proposed geometric features have achieved an accuracy of 92.62%, thereby showing their effectiveness. Moreover, the proposed HFS-CC technique achieves 98.07% testing and 99.18% training accuracy. The better performance of HFS-CC is largely due to the discerning ability of the proposed geometric features and the developed hybrid feature space.
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Cancer pathology reflects disease progression (or regression) and associated molecular characteristics, and provides rich phenotypic information that is predictive of cancer grade and has potential ...implications in treatment planning and prognosis. According to the remarkable performance of computational approaches in the digital pathology domain, we hypothesized that machine learning can help to distinguish low-grade gliomas (LGG) from high-grade gliomas (HGG) by exploiting the rich phenotypic information that reflects the microvascular proliferation level, mitotic activity, presence of necrosis, and nuclear atypia present in digital pathology images. A set of 735 whole-slide digital pathology images of glioma patients (median age: 49.65 years, male: 427, female: 308, median survival: 761.26 days) were obtained from TCGA. Sub-images that contained a viable tumor area, showing sufficient histologic characteristics, and that did not have any staining artifact were extracted. Several clinical measures and imaging features, including conventional (intensity, morphology) and advanced textures features (gray-level co-occurrence matrix and gray-level run-length matrix), extracted from the sub-images were further used for training the support vector machine model with linear configuration. We sought to evaluate the combined effect of conventional imaging, clinical, and texture features by assessing the predictive value of each feature type and their combinations through a predictive classifier. The texture features were successfully validated on the glioma patients in 10-fold cross-validation (accuracy = 75.12%, AUC = 0.652). The addition of texture features to clinical and conventional imaging features improved grade prediction compared to the models trained on clinical and conventional imaging features alone (
= 0.045 and
= 0.032 for conventional imaging features and texture features, respectively). The integration of imaging, texture, and clinical features yielded a significant improvement in accuracy, supporting the synergistic value of these features in the predictive model. The findings suggest that the texture features, when combined with conventional imaging and clinical markers, may provide an objective, accurate, and integrated prediction of glioma grades. The proposed digital pathology imaging-based marker may help to (i) stratify patients into clinical trials, (ii) select patients for targeted therapies, and (iii) personalize treatment planning on an individual person basis.
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We explored the clinical and pathological impact of epidermal growth factor receptor (EGFR) extracellular domain missense mutations. Retrospective assessment of 260 de novo glioblastoma patients ...revealed a significant reduction in overall survival of patients having tumors with EGFR mutations at alanine 289 (EGFRA289D/T/V). Quantitative multi-parametric magnetic resonance imaging analyses indicated increased tumor invasion for EGFRA289D/T/V mutants, corroborated in mice bearing intracranial tumors expressing EGFRA289V and dependent on ERK-mediated expression of matrix metalloproteinase-1. EGFRA289V tumor growth was attenuated with an antibody against a cryptic epitope, based on in silico simulation. The findings of this study indicate a highly invasive phenotype associated with the EGFRA289V mutation in glioblastoma, postulating EGFRA289V as a molecular marker for responsiveness to therapy with EGFR-targeting antibodies.
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•EGFRA289D/T/V-mutated GBM tumors have distinct in vivo imaging characteristics•EGFRA289D/T/V-associated decrease in OS is driven by tumor proliferation and invasion•EGFRA289V leads to phosphorylation of Erk followed by increased MMP1 secretion•Targeting of EGFRA289V via mAb806 can reduce tumor growth and increase survival
Binder et al. show that glioblastoma (GBM) expressing EGFR A289 mutants exhibit invasive features and are associated with shorter survival in patients and mice. GBM cells expressing EGFRA289V increase ERK-dependent MMP1 expression but are sensitive to an EGFR monoclonal antibody being clinically developed.
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Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key ...requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ‘‘modality-agnostic training’’ technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach11Publicly available source code: https://github.com/CBICA/BrainMaGe obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.
•Accurate brain extraction on MRI scans in presence of diffuse gliomas is critical.•Comprehensive evaluation of prominent deep learning architectures, BET & FreeSurfer.•Multi-institutional data to test generalizability and to facilitate collaborations.•A novel “modality-agnostic” strategy to promote widespread application.
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Distinguishing benign from malignant disease is a primary challenge for colon histopathologists. Current clinical methods rely on qualitative visual analysis of features such as glandular ...architecture and size that exist on a continuum from benign to malignant. Consequently, discordance between histopathologists is common. To provide more reliable analysis of colon specimens, we propose an end-to-end computational pathology pipeline that encompasses gland segmentation, cancer detection, and then further breaking down the malignant samples into different cancer grades. We propose a multi-step gland segmentation method, which models tissue components as ellipsoids. For cancer detection/grading, we encode cellular morphology, spatial architectural patterns of glands, and texture by extracting multi-scale features: (i) Gland-based: extracted from individual glands, (ii) local-patch-based: computed from randomly-selected image patches, and (iii) image-based: extracted from images, and employ a hierarchical ensemble-classification method. Using two datasets (Rawalpindi Medical College (RMC),
= 174 and gland segmentation (GlaS),
= 165) with three cancer grades, our method reliably delineated gland regions (RMC = 87.5%, GlaS = 88.4%), detected the presence of malignancy (RMC = 97.6%, GlaS = 98.3%), and predicted tumor grade (RMC = 98.6%, GlaS = 98.6%). Training the model using one dataset and testing it on the other showed strong concordance in cancer detection (Train RMC - Test GlaS = 94.5%, Train GlaS - Test RMC = 93.7%) and grading (Train RMC - Test GlaS = 95%, Train GlaS - Test RMC = 95%) suggesting that the model will be applicable across institutions. With further prospective validation, the techniques demonstrated here may provide a reproducible and easily accessible method to standardize analysis of colon cancer specimens.
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The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is ...effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.
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Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public ...datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor's grade. This ...review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa's grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.
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