ArticleLa Radiologia medica2021
CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias.
Article in La Radiologia medica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Development and validation of a clinical-radiomics nomogram for differentiatingFrontiers in pediatrics · 2026Article
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- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
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- Tips and Tricks in Thoracic Radiology for Beginners: A Findings-Based Approach.Tomography (Ann Arbor, Mich.) · 2023Review
- Qualitative and semi-quantitative ultrasound assessment in delta and Omicron Covid-19 patients: data from high volume reference center.Infectious agents and cancer · 2023Article
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review.International journal of environmental research and public health · 2023Review
- Radiation Recall Pneumonitis: The Open Challenge in Differential Diagnosis of Pneumonia Induced by Oncological Treatments.Journal of clinical medicine · 2023Review
- Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches.European radiology experimental · 2023Article
- Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.Tomography (Ann Arbor, Mich.) · 2022Article
- Radiomics in medical imaging: pitfalls and challenges in clinical management.Japanese journal of radiology · 2022Review
- Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center.Journal of personalized medicine · 2022Article
- Pulmonary Lymphangitis Poses a Major Challenge for Radiologists in an Oncological Setting during the COVID-19 Pandemic.Journal of personalized medicine · 2022Article
- A review on advances inInsights into imaging · 2022Review
- MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net.Clinical imaging · 2022Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. MATERIAL AND
methodsCT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C-), respectively. C- patients, however, presented with interstitial lung involvement. A subgroup of C-, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set.
resultsThe first model classified C + and C- pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C- (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81).
conclusionWhole lung ML models based on radiomics can classify C + and C- interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.
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