ArticleQuantitative imaging in medicine and surgery2022
Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- The study of nomogram model based on CT radiomics and clinical features for histological classification of parotid gland tumors.Scientific reports · 2026Article
- Decoding the Rotation Effect: A Retrospective Analysis of Lesion Orientation and Its Impact on Wavelet-Based Radiomics Feature Extraction and Lung Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Classification of Chest X-Ray Images Using Modern Machine Learning Techniques by Orang3 Data Mining Tool.International journal of biomedical imaging · 2026Article
- CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.Biomedical engineering online · 2025Article
- Radiomics application using non-contrast computed tomography for predicting uric acid kidney stones.BMC medical imaging · 2025Article
- Arterial phase CT radiomics for non-invasive prediction of Ki-67 proliferation index in pancreatic solid pseudopapillary neoplasms.Abdominal radiology (New York) · 2025Article
- A radiomics-based machine learning model and SHAP for predicting spread through air spaces and its prognostic implications in stage I lung adenocarcinoma: a multicenter cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A prognostic model integrating radiomics and deep learning based on CT for survival prediction in laryngeal squamous cell carcinoma.Scientific reports · 2025Article
- Machine-learning model based on ultrasomics for non-invasive evaluation of fibrosis in IgA nephropathy.European radiology · 2025Article
- Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.European journal of nuclear medicine and molecular imaging · 2025Article
- An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Integrating intratumoral, peritumoral, and clinical features in an ultrasound-based radiomics model: contributions and synergies for predicting microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics.Frontiers in oncology · 2025Article
- Intratumoral and peritumoral radiomics based on automated breast volume scanner for predicting human epidermal growth factor receptor 2 status.Frontiers in oncology · 2025Article
- A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients.European journal of radiology open · 2024Article
- Computed tomography-based radiomics nomogram for prediction of lympho-vascular and perineural invasion in esophageal squamous cell cancer patients: a retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Development and validation of an interpretable delta radiomics-based model for predicting invasive ground-glass nodules in lung adenocarcinoma: a retrospective cohort study.Quantitative imaging in medicine and surgery · 2024Article
- Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer.Frontiers in oncology · 2024Article
- Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics.Frontiers in oncology · 2024Article
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Abstract
Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data. Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models. Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability. Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, which may facilitate individualized management of patients with this disease.
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