Dataset Information

Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

ABSTRACT: This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19.

SUBMITTER: Selvam M

PROVIDER: S-EPMC10963772 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

Selvam Minmini M Chandrasekharan Anupama A Sadanandan Abjasree A Anand Vikas K VK Ramesh Sidharth S Murali Arunan A Krishnamurthi Ganapathy G

Scientific reports 20240325 1

This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were ide ...[more]

PMID: 38528100

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Project description:The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.

Project description:BackgroundConventional methods for detecting lung cancer early are often qualitative and subject to interpretation. Radiomics provides quantitative characteristics of pulmonary nodules (PNs) in medical images, but variability in medical image acquisition is an obstacle to consistent clinical application of these quantitative features. Correcting radiomic features' dependency on acquisition parameters is problematic when combining data from benign and malignant PNs, as is necessary when the goal is to diagnose lung cancer, because acquisition effects may differ between them due to their biological differences.PurposeWe evaluated whether we must account for biological differences between benign and malignant PNs when correcting the dependency of radiomic features on acquisition parameters, and we compared methods of doing this using ComBat harmonization.MethodsThis study used a dataset of 567 clinical chest CT scans containing both malignant and benign PNs. Scans were grouped as benign, malignant, or lung cancer screening (mixed benign and malignant). Preprocessing and feature extraction from ROIs were performed using PyRadiomics. Optimized Permutation Nested ComBat harmonization was performed on extracted features to account for variability in four imaging protocols: contrast enhancement, scanner manufacturer, acquisition voltage, focal spot size. Three methods were compared: harmonizing all data collectively in the standard manner, harmonizing all data with a covariate to preserve distinctions between subgroups, and harmonizing subgroups separately. A significant (p ≤ 0.05) Kruskal-Wallis test determined whether harmonization removed a feature's dependency on an acquisition parameter. A LASSO-SVM pipeline was trained using acquisition-independent radiomic features to predict whether PNs were malignant or benign. To evaluate the predictive information made available by each harmonization method, the trained harmonization estimators and predictive model were applied to a corresponding unseen test set. Harmonization and predictive performance metrics were assessed over 10 trials of 5-fold cross validation.ResultsKruskal-Wallis defined an average 2.1% of features (95% CI: 1.9-2.4%) as acquisition-independent when data were harmonized collectively, 27.3% of features (95% CI: 25.7-28.9%) as acquisition-independent when harmonized with a covariate, and 90.9% of features (95% CI: 90.4-91.5%) as acquisition-independent when harmonized separately. LASSO-SVM models trained on data harmonized separately or with a covariate had higher ROC-AUC for lung cancer screening scans than models trained on data harmonized without distinction between benign and malignant tissues (Delong test, Holm-Bonferroni adjusted p ≤ 0.05). There was not a conclusive difference in ROC-AUC between models trained on data harmonized separately and models trained on data harmonized with a covariate.ConclusionsRadiomic features of benign and malignant PNs require different corrective transformations to recover acquisition-independent distributions. This can be done using separate harmonization or harmonization with a covariate. Separate harmonization enabled the greatest number of predictive features to be used in a machine learning model to retrospectively detect lung cancer. Features harmonized separately and features harmonized with a covariate enabled predictive models to achieve similar performance on lung cancer screening scans.

Dataset Information

Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

Publications

Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

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