ArticleThe clinical respiratory journal2023
Potential predictive value of CT radiomics features for treatment response in patients with COVID-19.
Article in The clinical respiratory journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A Joint Classification Method for COVID-19 Lesions Based on Deep Learning and Radiomics.Tomography (Ann Arbor, Mich.) · 2024Article
- Predicting oxygen needs in COVID-19 patients using chest radiography multi-region radiomics.Radiological physics and technology · 2024Article
- Potential predictive value of CT radiomics features for treatment response in patients with COVID-19.The clinical respiratory journal · 2023Article
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Authors and funding
13 authors.
Funding
Abstract
introductionThis study aims to explore the predictive value of CT radiomics and clinical characteristics for treatment response in COVID-19 patients.
methodsData were collected from clinical/auxiliary examinations and follow-ups of COVID-19 patients. Whole lung radiomics feature extraction was performed at baseline chest CT. Radiomics, clinical, and combined features (nomogram) were evaluated for predicting treatment response.
resultsAmong 36 COVID-19 patients, mild, common, severe, and critical disease symptoms were found in 1, 21, 13, and 1 of them, respectively. Twenty-five (1 mild, 18 common, and 6 severe) patients showed a good response to treatment and 11 poor/fair responses. The clinical classification (p = 0.025) and serum creatinine (p = 0.010) on admission and small area emphasis (p = 0.036) from radiomics analysis significantly differed between the two groups. Predictive models were constructed based on the radiomics, clinical features, and nomogram showing an area under the curve of 0.651, 0.836, and 0.869, respectively. The nomogram achieved good calibration.
conclusionThis new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.
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