Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.
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SUBMITTER: Schiaffino S
PROVIDER: S-EPMC8230339 | biostudies-literature | 2021 Jun
REPOSITORIES: biostudies-literature
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Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.
Schiaffino Simone S Codari Marina M Cozzi Andrea A Albano Domenico D Alì Marco M Arioli Roberto R Avola Emanuele E Bnà Claudio C Cariati Maurizio M Carriero Serena S Cressoni Massimo M Danna Pietro S C PSC Della Pepa Gianmarco G Di Leo Giovanni G Dolci Francesco F Falaschi Zeno Z Flor Nicola N Foà Riccardo A RA Gitto Salvatore S Leati Giovanni G Magni Veronica V Malavazos Alexis E AE Mauri Giovanni G Messina Carmelo C Monfardini Lorenzo L Paschè Alessio A Pesapane Filippo F Sconfienza Luca M LM Secchi Francesco F Segalini Edoardo E Spinazzola Angelo A Tombini Valeria V Tresoldi Silvia S Vanzulli Angelo A Vicentin Ilaria I Zagaria Domenico D Fleischmann Dominik D Sardanelli Francesco F
Journal of personalized medicine 20210603 6
Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney <i>U</i> test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-c ...[more]
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Project description:BackgroundIt is clinically important to accurately assess the pulmonary function of patients with lung cancer, especially before surgery. This knowledge can help clinicians to monitor patients pre- and post-surgery, predict the impact of surgery on pulmonary function, and help to optimize postsurgical recovery. We used a deep learning approach for assessing pulmonary function on computed tomography (CT) scans in patients with lung cancer before they underwent surgery.MethodsA total of 188 patients with lung cancer whose diagnoses had been pathologically confirmed were enrolled in this study. We used a software to automatically delineate regions of interest (ROIs) throughout the airways, lobes, and the whole lungs. We then used AK software to extract radiomics features of the 3 types of ROIs. We randomly separated these cases into a training cohort and a test cohort at a ratio of 7:3. We next constructed a logistic regression model to assess pulmonary function from the radiomics features. The machine learning outcomes were compared with established clinical criteria for pulmonary function. including forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), FVC, and maximum vital capacity (VCmax) to evaluate the accuracy of the machine learning model.ResultsIn the ROIs of the lobes, our results showed that the machine learning model had good performance in predicting FVC and VCmax, attaining a Spearman correlation r value of 0.714 with P<0.001 for FVC and a r value of 0.687 with P<0.001 for VCmax. Using the airway ROIs, our model achieved a r of 0.603 with P=0.001 for VCmax. Using the whole lung ROIs, our model achieved a r of 0.704 with P<0.001 for FVC and a r of 0.693 with P<0.001 for VCmax.ConclusionsPreoperative CT may provide a means for evaluating pulmonary function in patients with lung cancer. With radiomics features extracted from the airway, lobes, and the whole lung region, and a properly trained machine learning model, it is possible to obtain accurate estimation for metrics used in clinical criteria and to offer clinicians imaging-based indicators for the status of pulmonary functions.
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