CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias.

Published on May 27, 2021in Radiologia Medica2
· DOI :10.1007/S11547-021-01370-8
Nicolò Cardobi10
Estimated H-index: 10
,
Giulio Benetti9
Estimated H-index: 9
+ 4 AuthorsStefania Montemezzi15
Estimated H-index: 15
(University of Verona)
Sources
Abstract
PURPOSE: To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. MATERIAL AND METHODS: CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C-), respectively. C- patients, however, presented with interstitial lung involvement. A subgroup of C-, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. RESULTS: The first model classified C + and C- pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C- (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). CONCLUSION: Whole lung ML models based on radiomics can classify C + and C- interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.
📖 Papers frequently viewed together
9 Citations
3 Citations
References30
Newest
#1Julien GuiotH-Index: 16
#2Akshayaa Vaidyanathan (UM: Maastricht University)H-Index: 3
Last. Stephane MathieuH-Index: 2
view all 21 authors...
The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVI...
4 CitationsSource
#1Hongmei YueH-Index: 6
#2Qian YuH-Index: 5
Last. Xiaolong Qi (Lanzhou University)H-Index: 23
view all 24 authors...
Background The coronavirus disease 2019 (COVID-19) has become a global challenge since the December 2019. The hospital stay is one of the prognostic indicators, and its predicting model based on CT radiomics features is important for assessing the patients' clinical outcome. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with COVID-19 pneumonia. Methods This retrospective, multicenter study enrolled patients with laboratory...
35 CitationsSource
#1Wei Zhao (CSU: Central South University)
#1Wei Zhao (CSU: Central South University)H-Index: 6
Last. Jun Liu (CSU: Central South University)
view all 6 authors...
Purpose: We aimed to investigate the relationship between clinical characteristics, radiographic features, and the viral load of patients with coronavirus disease 2019 (COVID-19). Methods and Materials: We retrospectively collected 56 COVID-19 cases from two institutions in Hunan province, China. The basal clinical characteristics, detail imaging features and follow-up CT changes were evaluated and the relationship with the viral load was analyzed. Results: GGO (48, 85.7%) and vascular enlargeme...
2 CitationsSource
#1Chenglong Liu (USST: University of Shanghai for Science and Technology)H-Index: 1
#2Xiaoyang WangH-Index: 5
Last. Wenxian Peng (University of Medicine and Health Sciences)H-Index: 2
view all 5 authors...
BACKGROUND: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. METHODS: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were co...
4 CitationsSource
#1Xu FangH-Index: 4
#2Xiao Li (NU: Nanjing University)H-Index: 3
Last. Jianping LuH-Index: 16
view all 5 authors...
To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. For this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator meth...
9 CitationsSource
#1Riccardo M. Inciardi (University of Brescia)H-Index: 9
#2Laura Lupi (University of Brescia)H-Index: 8
Last. Marco Metra (University of Brescia)H-Index: 109
view all 16 authors...
Importance Virus infection has been widely described as one of the most common causes of myocarditis. However, less is known about the cardiac involvement as a complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objective To describe the presentation of acute myocardial inflammation in a patient with coronavirus disease 2019 (COVID-19) who recovered from the influenzalike syndrome and developed fatigue and signs and symptoms of heart failure a week after upper...
774 CitationsSource
#1Li-Ping FuH-Index: 1
#2Yongchou Li (WMU: Wenzhou Medical College)H-Index: 1
Last. Zhenyu ShuH-Index: 1
view all 5 authors...
OBJECTIVE: This study aimed to use the radiomics signatures of a machine learning-based tool to evaluate the prognosis of patients with coronavirus disease 2019 (COVID-19) infection. METHODS: The clinical and imaging data of 64 patients with confirmed diagnoses of COVID-19 were retrospectively selected and divided into a stable group and a progressive group according to the data obtained from the ongoing treatment process. Imaging features from whole-lung images from baseline computed tomography...
11 CitationsSource
#1Qingxia Wu (NU: Northeastern University)H-Index: 7
#2Shuo Wang (Beihang University)H-Index: 15
Last. Jie Tian (NU: Northeastern University)H-Index: 95
view all 13 authors...
Rationale: Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a non-invasive and easy-to-use prognostic signature by chest CT to individually predict poor outcome (death, need for mechanical ventilation, or intensive care unit admission) in patients with COVID-19. Methods: From November 29, 2019 to February 19, 2020, a total of 492 patients with COVID-19 from four centers wer...
21 CitationsSource
#1Takeshi Moriguchi (University of Yamanashi)H-Index: 6
#2Norikazu Harii (University of Yamanashi)H-Index: 8
Last. Shinji Shimada (University of Yamanashi)H-Index: 35
view all 29 authors...
Novel coronavirus (SARS-Coronavirus-2:SARS-CoV-2) which emerged in Wuhan, China, has spread to multiple countries rapidly. We report the first case of meningitis associated with SARS-CoV-2 who was brought in by ambulance due to a convulsion accompanied by unconsciousness. He had never been to any foreign countries. He felt generalized fatigue and fever (day 1). He saw doctors nearby twice (day2 and 5) and was prescribed Laninamivir and antipyretic agents, His family visited his home and found th...
818 CitationsSource
#1Julien GuiotH-Index: 16
#2Akshayaa Vaidyanathan (UM: Maastricht University)H-Index: 3
Last. Pierre LovinfosseH-Index: 6
view all 21 authors...
Background : The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits. Objectives : To develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance. Methods : In this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest...
3 CitationsSource
Cited By0
Newest