ArticleScientific reports2021
Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- CT-based radiomics nomogram for distinguishingTranslational pediatrics · 2026Article
- Unlocking the potential of radiomics in identifying fibrosing and inflammatory patterns in interstitial lung disease.La Radiologia medica · 2025Article
- Development and multicentric external validation of a prognostic COVID-19 severity model based on thoracic CT.BMC medical informatics and decision making · 2025Article
- Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.Narra J · 2025Review
- Molecular characterization of cell decay in inflammation and topological assignment of released cfDNA for integrative laboratory and radiological outcome assessment.Frontiers in cellular and infection microbiology · 2025Article
- Texture analysis improves lung-tissue segmentation on high-resolution computed tomography in COVID-19.Frontiers in radiology · 2025Article
- Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study.La Radiologia medica · 2024Article
- Article
- 3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.Journal of digital imaging · 2023Article
- Sansoninto-induced Lung Injury.Internal medicine (Tokyo, Japan) · 2022Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
- Advances in diagnostic tools for respiratory tract infections: from tuberculosis to COVID-19 - changing paradigms?ERJ open research · 2022Review
- Telemedicine and virtual respiratory care in the era of COVID-19.ERJ open research · 2022Review
- Atypical Sites of Lymphadenopathy after Anti-COVID-19 Vaccine: Ultrasound Features.Medicina (Kaunas, Lithuania) · 2022Article
- Using Lung Base Covid-19 Findings to Predict Future Disease Trends and New Variant Outbreaks: Study of First New York City (NYC) Outbreak.Academic radiology · 2021Article
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Authors and funding
20 authors at 3 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Ground-glass opacities (GGOs) are a non-specific high-resolution computed tomography (HRCT) finding tipically observed in early Coronavirus disesase 19 (COVID-19) pneumonia. However, GGOs are also seen in other acute lung diseases, thus making challenging the differential diagnosis. To this aim, we investigated the performance of a radiomics-based machine learning method to discriminate GGOs due to COVID-19 from those due to other acute lung diseases. Two sets of patients were included: a first set of 28 patients (COVID) diagnosed with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) between March and April 2020 having (a) baseline HRCT at hospital admission and (b) predominant GGOs pattern on HRCT; a second set of 30 patients (nCOVID) showing (a) predominant GGOs pattern on HRCT performed between August 2019 and April 2020 and (b) availability of final diagnosis. Two readers independently segmented GGOs on HRCTs using a semi-automated approach, and radiomics features were extracted using a standard open source software (PyRadiomics). Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. A leave-one-out nested cross-validation was implemented. PLS β-weights of radiomics features, including the 5% features with the largest β-weights in magnitude (top 5%), were obtained. The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. The Youden's test assessed sensitivity and specificity of the classification. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.868 (Youden's index = 0.68, sensitivity = 93%, specificity 75%, p = 4.2 × 10
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