Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study

Feng, Qianjin; Chen, Yuanjun; Liao, Zuwei; Jiang, Hongyang; Mao, Dewang; Wang, Mei; Yu, Enyan; Ding, Zhongxiang

doi:10.3389/fneur.2018.00618

Cited by 42 publications

(42 citation statements)

References 33 publications

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“…According to previous studies [19][20][21], whose sample size is comparable with ours, the ratio between primary and validation cohort is 7:3. In this study, a total of 136 patients were divided into primary (n = 98) and validation (n = 38) cohorts, close to 7:3.…”

Section: Patientssupporting

confidence: 63%

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

et al. 2020

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Objectives Rapid and accurate diagnosis of coronavirus disease 2019 is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. Methods A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. Results Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. Conclusions Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19.Xiaofeng Chen and Yanyan Tang contributed equally to this work.Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-06829-2) contains supplementary material, which is available to authorized users. Key Points• Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

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Section: Patientssupporting

confidence: 63%

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

et al. 2020

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“…Radiomics has been successfully applied to neurodegenerative disease studies. In a recent radiomics study by Feng et al (23), T1W images from 78 patients with AD and 44 healthy controls were used for radiomics analysis. The corpus callosum was segmented manually, and texture features were obtained after extraction from each subject.…”

Section: Discussionmentioning

confidence: 99%

“…However, given its power in capturing the microstructural changes in tissues and its correlation with clinical endpoints (17) and age progression (18,19), the use of radiomics is expected to increase in neurodegenerative disorders (20). Presently, radiomics has been applied to the diagnosis of neurodegenerative diseases, including Alzheimer's disease (AD), amyotrophic lateral sclerosis, and Machado-Joseph disease with conventional MRI (21)(22)(23)(24)(25), which have similar pathological changes with PD, such as atrophy, abnormal proteins, or iron deposition in many brain regions. In a longitudinal study, radiomics successfully detected microstructural changes in invisible normal-appearing white matter on conventional T2 fluid-attenuated inversionrecovery (FLAIR) images (18).…”

Section: Introductionmentioning

confidence: 99%

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

Liu¹,

Wang

Zheng³

et al. 2020

Front. Neurol.

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Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD. Methods: T2-weighted images from 69 PD patients and 69 age-and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group. Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986-0.9833) and 0.7732 (95% CI: 0.6292-0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066-0.9469) and 0.7143 (95% CI: 0.5540-0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05). Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.

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“…One hundred and sixty-two GLSZM-based features represented the joint probability of certain sets of pixels having certain grey-level values, and the co-occurring pairs of pixels could be spatially related in various orientations relative to distance (1,4,7 displacement) and angular (0 ,45 , 90 , 135 ) spatial relationships to represent the intensity value of a neighbourhood. 30 One hundred and eighty RLM-based features were defined as the number of runs with pixels of different grey levels and run lengths for a given direction such as those defined by GLCM. 31 The extracted texture features were standardised by Z-score (z ¼ xÀm s , where m was the mean value of the images, and s was the standard deviation), which could remove the unit limits on the data of each feature.…”

Section: Texture Feature Extractionmentioning

confidence: 99%

A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules

et al. 2019

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to predict the histological subtypes of lung invasive adenocarcinomas (IAs) and minimally invasive adenocarcinomas (MIAs) that manifest as partsolid ground-glass nodules (GGNs). MATERIALS AND METHODS: This retrospective study enrolled 119 patients with histopathologically confirmed part-solid GGNs assigned to the training (n¼83) or testing cohorts (n¼36). Radiomic features were extracted based on the unenhanced computed tomography (CT) images. R software was applied to process the qualitative and quantitative data. The CT features model, radiomic signature model, and combined prediction model were constructed and compared. RESULTS: A total of 396 radiomic features were extracted from the preoperative CT images, four features including MaxIntensity, RMS, ZonePercentage, and Long-RunEmphasis_angle0_offset7 were indicated to be the best discriminators to establish the radiomic signature model. The performance of the model was satisfactory in both the training and testing set with areas under the curve (AUCs) of 0.854 (95% confidence interval [CI]: 0.774 to 0.934) and 0.813 (95% CI: 0.670 to 0.955), respectively. The CT morphology of the lesion shape and diameter of the solid component were confirmed to be a significant feature for building the CT features model, which had an AUC of 0.755 (95% CI: 0.648 to 0.843). A nomogram that integrated lesion shape and radiomic signature was constructed, which contributed an AUC of 0.888 (95% CI: 0.82 to 0.955). CONCLUSIONS: The radiomic signature could provide an important reference for differentiating IAs from MIAs, and could be significantly enhanced by the addition of CT morphology. The nomogram may be highly informative for making clinical decisions.

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Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study

Cited by 42 publications

References 33 publications

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules

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