Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images

Cai, Shengping; Yang, Chen; Zhao, Shixuan; He, De-Huai; Li, Yongjie; Xiong, Nian; Li, Zhidan; Hu, Shaoping

doi:10.1007/s00330-021-08533-1

Cited by 3 publications

(1 citation statement)

References 24 publications

(21 reference statements)

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“…In addition, several studies were conducted at the intersection era of COVID-19 to develop CT-based radiomic signatures for diagnostic and predictive purposes with the aim to improve clinical decision-making. 17,[23][24][25][26][27][28][29][30][31][32] Most aforementioned studies utilized datasets consisting of the dichotomy of COVID-19 pneumonia versus other non-COVID-19 pneumonia or COVID-19 vs. normal patients. However, some CXR or CT studies included other lung diseases as well.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Shiri,

Salimi,

Saberi

et al. 2024

Int J Imaging Syst Tech

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To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests.

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Section: Introductionmentioning

confidence: 99%

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Shiri,

Salimi,

Saberi

et al. 2024

Int J Imaging Syst Tech

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A Comparative Study of Artificial Intelligence and eXplainable AI Techniques for Pulmonary Disease Detection and Its Severity Classification

Pawar,

Patil,

Dhotre

et al. 2024

2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)

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Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

Azizi,

Sakly,

Wajdi

et al. 2024

HIS

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In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.

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Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images

Cited by 3 publications

References 24 publications

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

A Comparative Study of Artificial Intelligence and eXplainable AI Techniques for Pulmonary Disease Detection and Its Severity Classification

Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

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