Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease
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...
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2021
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oai:doaj.org-article:ca0ccf9204d9467b9413f9ffef0d77892021-12-02T15:09:23ZRadiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease10.1038/s41598-021-96755-02045-2322https://doaj.org/article/ca0ccf9204d9467b9413f9ffef0d77892021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96755-0https://doaj.org/toc/2045-2322Abstract 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 × 10–7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients.Andrea Delli PizziAntonio Maria ChiarelliPiero ChiacchiarettaCristina ValdesiPierpaolo CroceDomenico MastrodicasaMichela VillaniStefano TrebeschiFrancesco Lorenzo SerafiniConsuelo RosaGiulio CoccoRiccardo LubertiSabrina ConteLucia MazzamurroManuela MereuRosa Lucia PateaValentina PanaraStefano MarinariJacopo VecchietMassimo CauloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Andrea Delli Pizzi Antonio Maria Chiarelli Piero Chiacchiaretta Cristina Valdesi Pierpaolo Croce Domenico Mastrodicasa Michela Villani Stefano Trebeschi Francesco Lorenzo Serafini Consuelo Rosa Giulio Cocco Riccardo Luberti Sabrina Conte Lucia Mazzamurro Manuela Mereu Rosa Lucia Patea Valentina Panara Stefano Marinari Jacopo Vecchiet Massimo Caulo Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease |
| description |
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 × 10–7). Of the seven features included in the top 5% features, five were texture-related. A radiomics-based machine learning signature showed the potential to accurately differentiate GGOs due to COVID-19 pneumonia from those due to other acute lung diseases. Most of the discriminant radiomics features were texture-related. This approach may assist clinician to adopt the appropriate management early, while improving the triage of patients. |
| format |
article |
| author |
Andrea Delli Pizzi Antonio Maria Chiarelli Piero Chiacchiaretta Cristina Valdesi Pierpaolo Croce Domenico Mastrodicasa Michela Villani Stefano Trebeschi Francesco Lorenzo Serafini Consuelo Rosa Giulio Cocco Riccardo Luberti Sabrina Conte Lucia Mazzamurro Manuela Mereu Rosa Lucia Patea Valentina Panara Stefano Marinari Jacopo Vecchiet Massimo Caulo |
| author_facet |
Andrea Delli Pizzi Antonio Maria Chiarelli Piero Chiacchiaretta Cristina Valdesi Pierpaolo Croce Domenico Mastrodicasa Michela Villani Stefano Trebeschi Francesco Lorenzo Serafini Consuelo Rosa Giulio Cocco Riccardo Luberti Sabrina Conte Lucia Mazzamurro Manuela Mereu Rosa Lucia Patea Valentina Panara Stefano Marinari Jacopo Vecchiet Massimo Caulo |
| author_sort |
Andrea Delli Pizzi |
| title |
Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease |
| title_short |
Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease |
| title_full |
Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease |
| title_fullStr |
Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease |
| title_full_unstemmed |
Radiomics-based machine learning differentiates “ground-glass” opacities due to COVID-19 from acute non-COVID-19 lung disease |
| title_sort |
radiomics-based machine learning differentiates “ground-glass” opacities due to covid-19 from acute non-covid-19 lung disease |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/ca0ccf9204d9467b9413f9ffef0d7789 |
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