scholarly journals Developing a Radiomics Signature for Supratentorial Extra-Ventricular Ependymoma Using Multimodal MR Imaging

2021 ◽  
Vol 12 ◽  
Author(s):  
Apoorva Safai ◽  
Sumeet Shinde ◽  
Manali Jadhav ◽  
Tanay Chougule ◽  
Abhilasha Indoria ◽  
...  
Keyword(s):  
Classification Performance ◽  
Diagnostic Model ◽  
Multiple Tumor ◽  
Feature Sets ◽  
Contrast Enhanced ◽  
Multimodal Features ◽  
Apparent Diffusion ◽  
Fluid Attenuated Inversion Recovery ◽  
Multimodal Mri

Rationale and Objectives: To build a machine learning-based diagnostic model that can accurately distinguish adult supratentorial extraventricular ependymoma (STEE) from similarly appearing high-grade gliomas (HGG) using quantitative radiomic signatures from a multi-parametric MRI framework.Materials and Methods: We computed radiomic features on the preprocessed and segmented tumor masks from a pre-operative multimodal MRI dataset [contrast-enhanced T1 (T1ce), T2, fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC)] from STEE (n = 15), HGG-Grade IV (HGG-G4) (n = 24), and HGG-Grade III (HGG-G3) (n = 36) patients, followed by an optimum two-stage feature selection and multiclass classification. Performance of multiple classifiers were evaluated on both unimodal and multimodal feature sets and most discriminative radiomic features involved in classification of STEE from HGG subtypes were obtained.Results: Multimodal features demonstrated higher classification performance over unimodal feature set in discriminating STEE and HGG subtypes with an accuracy of 68% on test data and above 80% on cross validation, along with an overall above 90% specificity. Among unimodal feature sets, those extracted from FLAIR demonstrated high classification performance in delineating all three tumor groups. Texture-based radiomic features particularly from FLAIR were most important in discriminating STEE from HGG-G4, whereas first-order features from T2 and ADC consistently ranked higher in differentiating multiple tumor groups.Conclusions: This study illustrates the utility of radiomics-based multimodal MRI framework in accurately discriminating similarly appearing adult STEE from HGG subtypes. Radiomic features from multiple MRI modalities could capture intricate and complementary information for a robust and highly accurate multiclass tumor classification.

scholarly journals Pathophysiological consideration of medullary streaks on FLAIR imaging in pediatric moyamoya disease

2017 ◽  
Vol 19 (5) ◽  
pp. 560-566 ◽  
Author(s):  
Hime Suzuki ◽  
Takeshi Mikami ◽  
Tomoyoshi Kuribara ◽  
Kazuhisa Yoshifuji ◽  
Katsuya Komatsu ◽  
...  
Keyword(s):  
White Matter ◽  
Moyamoya Disease ◽  
Vasogenic Edema ◽  
Weighted Image ◽  
Flair Imaging ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
Fluid Attenuated Inversion Recovery ◽  
Ct Analysis

OBJECTIVEMedullary streaks detected on fluid-attenuated inversion recovery (FLAIR) imaging have been considered to be reflected ischemic regions in pediatric moyamoya disease. The purpose of this study was to evaluate these medullary streaks both clinically and radiologically and to discuss associated pathophysiological concerns.METHODSThe authors retrospectively reviewed data from 14 consecutive pediatric patients with moyamoya disease treated between April 2009 and June 2016. Clinical and radiological features and postoperative imaging changes were analyzed. In 4 patients, hyperintense medullary streaks on FLAIR imaging (HMSF) at the level of the centrum semiovale were detected.RESULTSThe HMSF were coincident with hyperintense medullary streaks on a T2-weighted image, though they were not completely coincident with the vasculature on either a T2*-weighted image or contrast-enhanced CT. Analysis revealed significantly higher values in terms of MR angiography scores, number of flow voids of the basal ganglia, and the presence of the medullary artery in the group with HMSF than in those without. In contrast, the presence of white matter damage was significantly less frequent in the HMSF group. All HMSF disappeared after surgery, and the mean apparent diffusion coefficient at the same level was significantly reduced postoperatively.CONCLUSIONSAlthough HMSF should be associated with collateral circulation in moyamoya disease, other factors may be involved, including stagnated cerebrospinal fluid or vasogenic edema that is relevant to the impaired state of the white matter. Findings in this study provide insight into the pathophysiological basis of the perivascular space in moyamoya disease.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Jing Yan ◽  
Bin Zhang ◽  
Shuaitong Zhang ◽  
Jingliang Cheng ◽  
Xianzhi Liu ◽  
...  
Keyword(s):  
Image Fusion ◽  
Molecular Subtypes ◽  
Progression Free Survival ◽  
Quantitative Mri ◽  
Promoter Mutation ◽  
Fusion Model ◽  
Free Survival ◽  
Contrast Enhanced ◽  
Apparent Diffusion ◽  
The Status

AbstractGliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive molecular groups and clinicopathologic data to predict progression-free survival (PFS) and overall survival (OS). The results showed that the image fusion model incorporating radiomic signatures from contrast-enhanced T1-weighted imaging (cT1WI) and apparent diffusion coefficient (ADC) achieved an AUC of 0.884 and 0.669 for predicting IDH and TERT status, respectively. cT1WI-based radiomic signature alone yielded favorable performance in predicting 1p/19q status (AUC = 0.815). The predictive molecular groups were comparable to actual ones in predicting PFS (C-index: 0.709 vs. 0.722, P = 0.241) and OS (C-index: 0.703 vs. 0.751, P = 0.359). Subgroup analyses by grades showed similar findings. The prognostic nomograms based on grades and the predictive molecular groups yielded a C-index of 0.736 and 0.735 in predicting PFS and OS, respectively. Accordingly, MRI-based radiomics may be useful for noninvasively detecting molecular groups and predicting survival in gliomas regardless of grades.

scholarly journals Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
Kun Zeng ◽  
Yibin Xu ◽  
Ge Lin ◽  
Likeng Liang ◽  
Tianyong Hao
Keyword(s):  
Clinical Trial ◽  
Ensemble Learning ◽  
Metric Learning ◽  
Classification Performance ◽  
Ensemble Model ◽  
Automated Classification ◽  
Eligibility Criteria ◽  
Data Imbalance ◽  
The Impact

Abstract Background Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clinical trial eligibility criteria text by using machine learning methods improves recruitment efficiency to reduce the cost of clinical research. However, existing methods suffer from poor classification performance due to the complexity and imbalance of eligibility criteria text data. Methods An ensemble learning-based model with metric learning is proposed for eligibility criteria classification. The model integrates a set of pre-trained models including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), XLNet, Pre-training Text Encoders as Discriminators Rather Than Generators (ELECTRA), and Enhanced Representation through Knowledge Integration (ERNIE). Focal Loss is used as a loss function to address the data imbalance problem. Metric learning is employed to train the embedding of each base model for feature distinguish. Soft Voting is applied to achieve final classification of the ensemble model. The dataset is from the standard evaluation task 3 of 5th China Health Information Processing Conference containing 38,341 eligibility criteria text in 44 categories. Results Our ensemble method had an accuracy of 0.8497, a precision of 0.8229, and a recall of 0.8216 on the dataset. The macro F1-score was 0.8169, outperforming state-of-the-art baseline methods by 0.84% improvement on average. In addition, the performance improvement had a p-value of 2.152e-07 with a standard t-test, indicating that our model achieved a significant improvement. Conclusions A model for classifying eligibility criteria text of clinical trials based on multi-model ensemble learning and metric learning was proposed. The experiments demonstrated that the classification performance was improved by our ensemble model significantly. In addition, metric learning was able to improve word embedding representation and the focal loss reduced the impact of data imbalance to model performance.

scholarly journals MultiKOC: Multi-One-Class Classifier Based K-Means Clustering

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 134
Author(s):  
Loai Abdallah ◽  
Murad Badarna ◽  
Waleed Khalifa ◽  
Malik Yousef
Keyword(s):  
Clustering Algorithm ◽  
Main Idea ◽  
Molecular Classification ◽  
Positive Sample ◽  
Classification Problems ◽  
Multiple Cancer ◽  
Multiple Tumor ◽  
One Class Classifier ◽  
The Given

In the computational biology community there are many biological cases that are considered as multi-one-class classification problems. Examples include the classification of multiple tumor types, protein fold recognition and the molecular classification of multiple cancer types. In all of these cases the real world appropriately characterized negative cases or outliers are impractical to achieve and the positive cases might consist of different clusters, which in turn might lead to accuracy degradation. In this paper we present a novel algorithm named MultiKOC multi-one-class classifiers based K-means to deal with this problem. The main idea is to execute a clustering algorithm over the positive samples to capture the hidden subdata of the given positive data, and then building up a one-class classifier for every cluster member’s examples separately: in other word, train the OC classifier on each piece of subdata. For a given new sample, the generated classifiers are applied. If it is rejected by all of those classifiers, the given sample is considered as a negative sample, otherwise it is a positive sample. The results of MultiKOC are compared with the traditional one-class, multi-one-class, ensemble one-classes and two-class methods, yielding a significant improvement over the one-class and like the two-class performance.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

Clinical value of classification of venous malformations with contrast-enhanced MR Angiography

2016 ◽  
Vol 32 (9) ◽  
pp. 628-633 ◽  
Author(s):  
Ahmed Abdel Khalek Abdel Razek ◽  
Germeen Ashmalla Albair ◽  
Sieza Samir
Keyword(s):  
Mr Angiography ◽  
Treatment Plan ◽  
Venous Malformation ◽  
Observer Agreement ◽  
Type I ◽  
Venous Malformations ◽  
Single Measure ◽  
Intra Class Correlation ◽  
Contrast Enhanced

Aim To classify venous malformations based on contrast-enhanced MR angiography that may serve as a basis for treatment plan. Patients and methods A retrospective analysis was performed in 58 patients with venous malformations who underwent contrast-enhanced MR angiography. Venous malformations were classified according to their venous drainage into: type I, isolated malformation without peripheral drainage; type II, malformation that drains into normal veins; type III, malformation that drains into dilated veins; and type IV, malformation that represents dysplastic venous ectasia. Image analysis was done by two reviewers. Intra and inter-observer agreement of both reviewers and intra-class correlation was done. Results The intra-observer agreement of contrast-enhanced MR angiography classification of venous malformations was excellent for the first reviewer ( k = 0.83, 95% CI = 0.724–0.951, P = 0.001) and substantial for the second reviewer ( K = 0.79, 95% CI = 0.656-0.931, P = 0.001). The inter-observer agreement of contrast-enhanced MR angiography classification of venous malformations was excellent for both reviewers at the first time ( K = 0.96, 95% CI = 0.933–1.000, P = 0.001) and second time ( k = 0.81, 95% CI = 0.678–0.942, P = 0.001). There was high intra-class correlation of both reviewers for single measure ( ICC = 0.85, 95% CI = 0.776–0.918, P = 0.001) and for average measures ( ICC = 0.96, 95% CI = 0.933–0.978, P = 0.001). Conclusion Contrast-enhanced MR angiography classification of venous malformations may be a useful, simple and reliable tool to accurately classify venous malformation and this topographic classification helps for better management strategy.

scholarly journals Correlation of radiomic features on dynamic contrast-enhanced magnetic resonance with microvessel density in hepatocellular carcinoma based on different models

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199758
Author(s):  
Hongwei Liang ◽  
Chunhong Hu ◽  
Jian Lu ◽  
Tao Zhang ◽  
Jifeng Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Magnetic Resonance ◽  
Microvessel Density ◽  
Roc Analysis ◽  
Diagnostic Model ◽  
Model Parameters ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Quantitative Parameters ◽  
Contrast Enhanced

Objective To explore the correlations of radiomic features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with microvessel density (MVD) in patients with hepatocellular carcinoma (HCC), based on single-input and dual-input two-compartment extended Tofts (SITET and DITET) models. Methods We compared the quantitative parameters of SITET and DITET models for DCE-MRI in 30 patients with HCC using paired sample t-tests. The correlations of SITET and DITET model parameters with CD31-MVD and CD34-MVD were analyzed using Pearson’s correlation analysis. A diagnostic model of CD34-MVD was established and the diagnostic abilities of models for MVD were analyzed using receiver operating characteristic curve (ROC) analysis. Results There were significant differences between the quantitative parameters in the two kinds of models. Compared with SITET, DITET parameters showed better correlations with CD31-MVD and CD34-MVD. The Ktrans and Ve radiomics features of the DITET model showed high efficiency for predicting the level of CD34-MVD according to ROC analysis, with areas under curves of 0.83 and 0.94, respectively. Conclusion Compared with SITET, the DITET model provides a better indication of the microcirculation of HCC and is thus more suitable for examining patients with HCC.

The role of contrast-enhanced ultrasound in the classification of CT-indeterminate renal lesions

2016 ◽  
Vol 50 (6) ◽  
pp. 445-451 ◽  
Author(s):  
Jan Edenberg ◽  
Kaja Gløersen ◽  
Herzi Abdi Osman ◽  
Magne Dimmen ◽  
Geir V. Berg
Keyword(s):  
Contrast Enhanced Ultrasound ◽  
Renal Lesions ◽  
Contrast Enhanced

Preoperative Gross Classification of Gastric Adenocarcinoma: Comparison of Double Contrast-Enhanced Ultrasound and Multi-Detector Row CT

2016 ◽  
Vol 42 (7) ◽  
pp. 1431-1440 ◽  
Author(s):  
Caoxin Yan ◽  
Xiaofeng Bao ◽  
Weihui Shentu ◽  
Jian Chen ◽  
Chunmei Liu ◽  
...  
Keyword(s):  
Gastric Adenocarcinoma ◽  
Contrast Enhanced Ultrasound ◽  
Double Contrast ◽  
Contrast Enhanced
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