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Radiation-Induced Pneumonitis in the Era of the COVID-19 Pandemic: Artificial Intelligence for Differential Diagnosis

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Radiation-Induced Pneumonitis in the Era of the COVID-19 Pandemic: Artificial Intelligence for Differential Diagnosis cancers Article Radiation-Induced Pneumonitis in the Era of the COVID-19 Pandemic: Artificial Intelligence for Differential Diagnosis Francesco Maria Giordano 1,† , Edy Ippolito 2,†, Carlo Cosimo Quattrocchi 1,*, Carlo Greco 2, Carlo Augusto Mallio 1 , Bianca Santo 2, Pasquale D’Alessio 1 , Pierfilippo Crucitti 3 , Michele Fiore 2 , Bruno Beomonte Zobel 1 , Rolando Maria D’Angelillo 4 and Sara Ramella 2 1 Departmental Faculty of Medicine and Surgery, Diagnostic Imaging and Interventional Radiology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; [email protected] (F.M.G.); [email protected] (C.A.M.); [email protected] (P.D.); [email protected] (B.B.Z.) 2 Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; [email protected] (E.I.); [email protected] (C.G.); [email protected] (B.S.); m.fi[email protected] (M.F.); [email protected] (S.R.) 3 Departmental Faculty of Medicine and Surgery, Thoracic Surgery, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; [email protected] 4 Departmental Faculty of Medicine and Surgery, Radiation Oncology, Università degli Studi Tor Vergata, 00133 Rome, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-06225411708 † These authors contributed equally to this manuscript. Simple Summary: Radiation-induced pneumonitis and severe acute respiratory syndrome coron- Citation: Giordano, F.M.; Ippolito, E.; avirus 2 (SARS-CoV-2) interstitial pneumonia show overlapping clinical features. As we are facing Quattrocchi, C.C.; Greco, C.; Mallio, the COVID-19 pandemic, the discrimination between these two entities is of paramount importance. C.A.; Santo, B.; D’Alessio, P.; Crucitti, In fact, lung cancer patients are at higher risk of complications from SARS-CoV-2. In this study, P.; Fiore, M.; Zobel, B.B.; et al. Radiation-Induced Pneumonitis in we aimed to investigate if a deep learning algorithm was able to discriminate between COVID-19 the Era of the COVID-19 Pandemic: and radiation therapy-related pneumonitis (RP). The algorithm showed high sensitivity but low Artificial Intelligence for Differential specificity in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, Diagnosis. Cancers 2021, 13, 1960. area under the curve (AUC = 0.72). The specificity increased when an estimated COVID-19 risk https://doi.org/10.3390/ probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). cancers13081960 Abstract: (1) Aim: To test the performance of a deep learning algorithm in discriminating radiation Academic Editors: Antonella Petrillo, therapy-related pneumonitis (RP) from COVID-19 pneumonia. (2) Methods: In this retrospective Vincenza Granata and Roberta Fusco 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 Received: 2 March 2021 on a novel deep convolutional neural network structure. The cut-off value of risk probability of Accepted: 12 April 2021 COVID-19 was 30%; values higher than 30% were classified as COVID-19 High Risk, and values Published: 19 April 2021 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 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in performed using the maximum likelihood fit of a binormal model. (3) Results: Most patients published maps and institutional affil- presenting RP (66.7%) were classified by the algorithm as COVID-19 Low Risk. The algorithm iations. 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 Copyright: © 2021 by the authors. COVID-19 pneumonia, classifying most RP cases as COVID-19 Low Risk. Licensee MDPI, Basel, Switzerland. This article is an open access article Keywords: COVID-19; radiation pneumonitis; artificial intelligence; deep learning; chest CT distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Cancers 2021, 13, 1960. https://doi.org/10.3390/cancers13081960 https://www.mdpi.com/journal/cancers Cancers 2021, 13, 1960 2 of 9 1. Introduction Radiotherapy plays a key role in the treatment of lung cancer, both as a radical treatment in inoperable patients as well as induction therapy or adjuvant treatment for resectable disease [1–3]. The mechanism of radiation-induced lung injury (RILI) depends on direct DNA damage and production of reactive oxygen species. The latter causes cell loss, edema of the alveolar walls and enhanced vascular permeability, leading to the apoptosis of alveolar type-1 pneumocytes. After apoptosis, cells start recruiting immune effector cells that activate tissue remodeling [4]. The clinical picture of RILI is radiation pneumonitis (RP). Symptomatic RP occurs in about 30% of patients receiving concurrent chemoradiation (CCRT) for non-small cell lung cancer (NSCLC) [5]. The incidence of RP is multifactorial, including treatment-related and patient-related conditions, particularly older age and larger volume of lungs receiving higher doses; some chemotherapy regimens are also associated with increased risk of RP [5]. In 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China. As the epidemic rapidly spread around the world, in March 2020, the World Health Organization (WHO) declared a global public health emergency, describing the situation as a pandemic [6]. The respiratory syndrome associated with SARS-CoV-2 includes variable degrees of severity. The most serious clinical entity is a severe interstitial pneumonia that can lead to acute respiratory distress [7]. RP and SARS-CoV-2 interstitial pneumonia show overlapping clinical features. In fact, the most common RP symptoms are dyspnea, dry non-productive cough and fever. In addition, most patients show a high erythrocyte sedimentation rate or C-reactive protein with normal serum procalcitonin and high serum ferritin and D-Dimer as expressions of cancer disease. Moreover, lymphopenia is frequently observed, as lymphocytes are more radiosensitive than other white blood cells. Finally, chest CT findings are also very similar, as the radiological characteristics of RP are ground-glass opacity (GGO) in the initial phase, patchy areas of consolidation in the peak phase and fibrotic changes in the dissipative phase [8]. As we are moving from the peak of the pandemic to the longer mitigation phase, the discrimination between these two entities is of paramount importance [9]. In fact, lung cancer patients are more fragile and are at higher risk of complications from SARS-CoV-2 due to their being immunocompromised and reduced lung function [10]. Various radiological techniques may be used for the detection and quantification of lung involvement in COVID-19 [11,12], but CT proved to be valuable for both purposes [13–16]. Deep learning (DL), a form of artificial intelligence (AI), is becoming a promising support for medical imaging due to its capability of feature extraction and analysis [17–20]. It has been successfully applied to chest CT imaging to distinguish COVID-19 pneumo- nia cases from community-acquired infections [21] as well as to provide qualitative and quantitative analyses for disease burden estimation, facilitating and expediting imaging interpretation [22,23]. Based on the above considerations, the aim of the present study was to test the performance of a deep learning algorithm in discriminating RP from COVID-19 pneumonia. 2. Materials and Methods We designed a retrospective observational study, performed in accordance with the Declaration of Helsinki. The local Ethical Committee approved the study (the ethic code is Prot.: 88/20 OSS.NOT ComEt CBM, 06 October 2020) and waived the need for written informed consent from participants. 2.1. Participants All of the subjects underwent chest CT scan and were consecutively sampled from our electronic database. In this study, three groups of patients were included and classified Cancers 2021, 13, 1960 3 of 9 according to both medical history and CT imaging findings (i.e., radiological reports). The patients were grouped as follows. Group 1 (control group): A group of consecutive symptomatic (fever > 37.5 ◦C, dyspnea and/or cough and/or fatigue) patients with negative chest CT scan acquired between 15 March 2020 and 30 April 2020. Group 2: A group of consecutive symptomatic (fever > 37.5 ◦C, dyspnea and/or cough and/or fatigue) patients with confirmed COVID-19 pneumonia by positive RT- PCR (RealTiMe SARS-CoV-2 Assay, Abbott Laboratories. Abbott Park, IL, USA) on a nasopharyngeal (or oropharyngeal) specimen using the swab technique and positive chest CT scan acquired between 15 March 2020 and 30 April 2020. Group 3: A group of consecutive patients with lung cancer treated with chemora- diation or radiotherapy alone and a positive history of RP confirmed by chest CT scan acquired from 2015 up to September 2019, before the occurrence of any known case of COVID-19 in Italy. Exclusion criteria were patients with history of previous radiation therapy on the thoracic region for other pathologies (such as breast cancer) and poor quality of CT images as well as insufficient chest expansion or movement artefacts. In Group 1 and Group 2, lung CT scans were indicated to explore indeterminate findings on chest X-ray and/or to quantify lung involvement in symptomatic patients. In Group 3, CT images were taken from oncological follow-up whole-body CT scans and retrospectively analyzed. 2.2. Chest CT Imaging Protocol All chest CT acquisitions were obtained by maintaining the patients in a supine posi- tion during end-inspiration, with or without contrast medium injection.
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