| Autor: |
Giordano FM; Departmental Faculty of Medicine and Surgery, Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Ippolito E; Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Quattrocchi CC; Departmental Faculty of Medicine and Surgery, Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Greco C; Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Mallio CA; Departmental Faculty of Medicine and Surgery, Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Santo B; Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., D'Alessio P; Departmental Faculty of Medicine and Surgery, Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Crucitti P; Departmental Faculty of Medicine and Surgery, Thoracic Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Fiore M; Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., Zobel BB; Departmental Faculty of Medicine and Surgery, Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy., D'Angelillo RM; Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università degli Studi Tor Vergata, 00133 Rome, Italy., Ramella S; Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy. |
| Abstrakt: |
(1) Aim: To test the performance of a deep learning algorithm in discriminating radiation therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) Methods: In this retrospective study, we enrolled three groups of subjects: pneumonia-free (control group), COVID-19 pneumonia and RP patients. CT images were analyzed by mean of an artificial intelligence (AI) algorithm based on a novel deep convolutional neural network structure. The cut-off value of risk probability of COVID-19 was 30%; values higher than 30% were classified as COVID-19 High Risk, and values below 30% as COVID-19 Low Risk. The statistical analysis included the Mann-Whitney U test (significance threshold at p < 0.05) and receiver operating characteristic (ROC) curve, with fitting performed using the maximum likelihood fit of a binormal model. (3) Results: Most patients presenting RP (66.7%) were classified by the algorithm as COVID-19 Low Risk. The algorithm showed high sensitivity but low specificity in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, area under the curve (AUC = 0.72). The specificity increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). (4) Conclusions: The deep learning algorithm was able to discriminate RP from COVID-19 pneumonia, classifying most RP cases as COVID-19 Low Risk. |
