Radiation induced pneumonitis during COVID-19: artificial intelligence for differential diagnosis

Purpose or Objective: In 2019, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was identified in Wuhan, China and in March 2020 the World Health Organization (WHO) declared the global public health emergency describing the situation as a pandemic. The most serious clinical entity of the respiratory syndrome associated with SARS-CoV-2 is a severe interstitial pneumonia. Radiation pneumonitis (RP) is a typical toxicity related to chemoradiation for locally advanced lung cancer patients. RP and SARS-CoV-2 interstitial pneumonia show overlapping clinical features and differential diagnosis maybe be challenging. The aim of this study is to test the performance of a deep learning algorithm in discriminating radiation pneumonitis (RP) from COVID-19 pneumonia. Materials and Methods: Seventy patients were analysed, thirty-four affected by COVID-19 pneumonia and thirty-six by radiation therapy-related pneumonitis (RP group). The CT images were quantitatively analyzed by InferReadTM CT Lung (COVID-19) (Infervision, Europe GmbH, Wiesbaden, Germany), an Artificial Intelligence solution specifically developed for diagnosis and management support of COVID-19 pneumonia, based on an AI algorithm built on a novel deep convolutional neural network structure. Based on a preliminary analysis of the deep-learning algorithm, the cut-off value of the estimated risk probability of COVID-19 was set at levels higher than 30% (“COVID19 High Risk”), as the percentage of COVID-19 confirmed patients above this cut-off value was higher than 95%. Values of estimated risk probability below 30% were classified as “COVID19 Low Risk. Results: Most patients presenting RP were classified by the algorithm as “COVID19 Low Risk” (66.7%). All RP classified as “COVID19 High Risk” were ≥G3 (CTC AE vers. 4.0). The algorithm showed good accuracy in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, AUC = 0.72). This accuracy increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). The total lung volume involvement was higher in COVID 19 patients compared with RP group (mean= 105.54 cc, IQ range= 44.68-257.07 vs mean=29.14 cc, IQ range= 5.59-69.20, p <0.001). In patients pretreated with radiation therapy and actually presenting diffuse pneumonitis classified by AI as “COVID19 High Risk” a combination of dosimetric factors may help to identify RP (PPV increased from 60% to 99.8%). Conclusion: Deep-learning algorithm can help to discriminate RP from COVID-19 pneumonia, classifying most RP as “Lowrisk COVID19” (below the cut off value of COVID-19 risk probability of 30%). In patients classified as high risk , treated with radiation therapy also dosimetric factors should be taken into account.

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

(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.