Abstract
Simple SummaryRadiation-induced pneumonitis and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interstitial pneumonia show overlapping clinical features. As we are facing the COVID-19 pandemic, the discrimination between these two entities is of paramount importance. In fact, lung cancer patients are at higher risk of complications from SARS-CoV-2. In this study, we aimed to investigate if a deep learning algorithm was able to discriminate between COVID-19 and radiation therapy-related pneumonitis (RP). 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).(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.
Highlights
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 [1,2,3].The mechanism of radiation-induced lung injury (RILI) depends on direct DNA damage and production of reactive oxygen species
Patients with COVID-19 and radiation pneumonitis (RP) were older than pneumonia-free patients (p = 0.001)
The assessment of CT scans based on the low-risk/high-risk classification that used the 30% cut-off value showed a net increase in specificity up to 63% (AUC 0.84); the median values of estimated risk probability were significantly lower in patients with RP than in those with COVID-19 (p = 0.001)
Summary
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. 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, older age and larger volume of lungs receiving higher doses; some chemotherapy regimens are associated with increased risk of RP [5]
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