{"id":26898,"date":"2021-03-24T11:00:00","date_gmt":"2021-03-24T11:00:00","guid":{"rendered":"http:\/\/1033c5b0dc00012adbcb0d12ae8d0f31"},"modified":"2024-11-03T15:15:00","modified_gmt":"2024-11-03T15:15:00","slug":"artificial-intelligence-may-aid-in-the-discrimination-of-radiation-pneumonitis-from-covid-19-associated-interstitial-pneumonia","status":"publish","type":"post","link":"https:\/\/ogkologos.com\/en\/artificial-intelligence-may-aid-in-the-discrimination-of-radiation-pneumonitis-from-covid-19-associated-interstitial-pneumonia\/","title":{"rendered":"Artificial Intelligence May Aid in the Discrimination of Radiation Pneumonitis from COVID-19-Associated Interstitial Pneumonia"},"content":{"rendered":"

An artificial intelligence (AI) algorithm was able to classify most patients with radiation induced pneumonitis into a category of low risk for Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) to assist in differentiating between the syndromes, according to findings presented at the European Lung Cancer Virtual Congress 2021<\/a> (25-27 March).<\/p>\n

Sara Ramella of the Radiation Oncology, Campus Bio-Medico University in Rome, Italy underscored the importance of distinguishing between SARS-CoV-2-associated severe interstitial pneumonia and radiation pneumonitis, which demonstrate similar and overlapping clinical features. Prof. Ramella and colleagues conducted this study to determine whether a deep learning algorithm may aid in the determination of radiation pneumonitis from COVID-19 pneumonia.<\/p>\n

The study analysed the data and computed tomography (CT) images of 34 patients with COVID-19 pneumonia and 36 with radiation pneumonitis. The InferReadTM CT Lung (COVID-19) \u00ae, an Artificial Intelligence algorithm based on a novel deep convolutional neural network structure, was used to analyse the patients\u2019 CT images. Cut-off for the estimated risk probability of COVID-19 was set at levels higher than 30% and categorised as COVID-19 high risk, according to a recent publication which determined the percentage of COVID-19 confirmed patients above this cut-off value was higher than 95%. Using the cut-off set in the same publication, values of estimated risk probability below 30% were classified as COVID-19 low risk.<\/p>\n

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\"Artificial-Intelligence-May-Aid-in-the-Discrimination-of-Radiation-Pneumonitis-from-COVID-19-Associated-Interstitial-Pneumonia-Figure-1\"<\/a>
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InferReadTM<\/sup> CT Lung (COVID-19) system interface example; comparison between a COVID-19 pneumonia positive patient (A) and a radiation pneumonitis-affected patient (B).<\/p>\n

\u00a9 Sara Ramella.<\/p>\n<\/div>\n<\/figcaption><\/figure>\n<\/div>\n<\/div>\n

Statistical analyses included the Mann Whitney U test using a significance threshold of p < 0.05, and receiver operating characteristic (ROC) curve with fitting performed by using the maximum likelihood fit of a binormal model.<\/p>\n

The algorithm aided the differential diagnosis of radiation- and COVID-19-associated pneumonia<\/h4>\n

According to the algorithm, 66.7% of patients with radiation pneumonitis were classified as COVID-19 low risk and all patients with radiation pneumonitis that were classified as COVID-19 high risk had \u2265grade 3 disease.<\/p>\n

The algorithm showed good accuracy in the detection of radiation pneumonia apart from COVID-19 pneumonia with 97% sensitivity and 2% specificity (area under the curve [AUC] 0.72). Accuracy was increased with the application of a 30% cut-off to 76% sensitivity and 63% specificity (AUC 0.84). The investigators also found that total lung volume (p = 0.001), the left lower lobe (p < 0.001), and the right lower lobe (p < 0.001) involvement were increased in the COVID-19 group compared to patients with radiation pneumonitis.<\/p>\n

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\"Artificial-Intelligence-May-Aid-in-the-Discrimination-of-Radiation-Pneumonitis-from-COVID-19-Associated-Interstitial-Pneumonia-Figure-2\"<\/a>
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Receiving operating curves (ROC) of the diagnostic performance of the artificial intelligence prediction risk of COVID-19 pneumonia. Each plot shows the ROC obtained on the testing after including the pairs: COVID-19 and pneumonia-free patients (A), RP and COVID-19 (B), COVID-19 and non-COVID-19 patients (C), RP and COVID-19 with 30% threshold (D), COVID-19 and RP total lung volume involvement (E), COVID-19 and RP RUL involvement (F), COVID-19 and RP RLL involvement (G), and COVID-19 and RP LLL involvement (H). Gray lines plot 95% confidence intervals.<\/p>\n

RP – Radiation Pneumonitis, RUL – Right Upper Lobe, ML – Middle Lobe, RLL – Right Lower Lobe, LUL – Left Upper Lobe, LLL – Left Lower Lobe, AUC = area under ROC, Std. Error = Standard Error.<\/p>\n

\u00a9 Sara Ramella.<\/p>\n<\/div>\n<\/figcaption><\/figure>\n<\/div>\n<\/div>\n

Conclusions<\/h4>\n

According to the authors, a deep-learning algorithm can help to discriminate radiation pneumonitis from COVID-19 pneumonia, and was able to classifying most radiation pneumonitis as low-risk COVID19. They advise that, in cases where patients treated with radiation therapy are classified as high risk, dosimetric factors should be taken into account.<\/p>\n

They further recommend that, in cases where patients pretreated with radiotherapy and presenting with diffuse pneumonitis are classified by AI as COVID-19 high risk, a combination of dosimetric factors may help to identify radiation pneumonitis, such as an increase in positive predictive value from 60% to 99.8%.<\/p>\n

No external funding was disclosed.<\/p>\n

Reference<\/h4>\n

42P \u2013 Ramella S, Quattrocchi CC, Ippolito E, et al.<\/em> Radiation induced pneumonitis in the era of COVID-19 pandemic: Artificial intelligence for differential diagnosis. European Lung Cancer Virtual Congress 2021<\/a> (25-27 March).<\/p>\n

Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

An artificial intelligence (AI) algorithm was able to classify most patients with radiation induced pneumonitis into a category of low risk for Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) to assist in differentiating between the syndromes, according to findings presented at the European Lung Cancer Virtual Congress 2021 (25-27 March). Sara Ramella of the Radiation Oncology, Campus […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[5137],"tags":[],"class_list":["post-26898","post","type-post","status-publish","format-standard","hentry","category-world-news"],"_links":{"self":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts\/26898","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/comments?post=26898"}],"version-history":[{"count":1,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts\/26898\/revisions"}],"predecessor-version":[{"id":45369,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/posts\/26898\/revisions\/45369"}],"wp:attachment":[{"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/media?parent=26898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/categories?post=26898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ogkologos.com\/en\/wp-json\/wp\/v2\/tags?post=26898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}