Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19

Abstract

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 were retrospectively collected. Since different durations from symptom onsets to the first CT scanning might affect the prognostic model, we designated the 492 patients into two groups: 1) the early-phase group: CT scans were performed within one week after symptom onset (0-6 days, n = 317); and 2) the late-phase group: CT scans were performed one week later after symptom onset (≥7 days, n = 175). In each group, we divided patients into the primary cohort (n = 212 in the early-phase group, n = 139 in the late-phase group) and the external independent validation cohort (n = 105 in the early-phase group, n = 36 in the late-phase group) according to the centers. We built two separate radiomics models in the two patient groups. Firstly, we proposed an automatic segmentation method to extract lung volume for radiomics feature extraction. Secondly, we applied several image preprocessing procedures to increase the reproducibility of the radiomics features: 1) applied a low-pass Gaussian filter before voxel resampling to prevent aliasing; 2) conducted ComBat to harmonize radiomics features per scanner; 3) tested the stability of the features in the radiomics signature by several image transformations, such as rotating, translating, and growing/shrinking. Thirdly, we used least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RadScore). Afterward, we conducted a Fine-Gray competing risk regression to build the clinical model and the clinic-radiomics signature (CrrScore). Finally, performances of the three prognostic signatures (clinical model, RadScore, and CrrScore) were estimated from the two aspects: 1) cumulative poor outcome probability prediction; 2) 28-day poor outcome prediction. We also did stratified analyses to explore the potential association between the CrrScore and the poor outcomes regarding different age, type, and comorbidity subgroups. Ivyspring International PublisherResults: In the early-phase group, the CrrScore showed the best performance in estimating poor outcome (C-index = 0.850), and predicting the probability of 28-day poor outcome (AUC = 0.862). In the late-phase group, the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (C-index = 0.885), and 28-day poor outcome probability (AUC = 0.976). Moreover, the RadScore in both groups successfully stratified patients with COVID-19 into low-or high-RadScore groups with significantly different survival time in the training and validation cohorts (all P < 0.05). The CrrScore in both groups can ...

A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

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Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features

“…Our results support the application of radiomics to assist with diagnosis of COVID-19 pneumonia. The recent development of radiomics has provided a new research paradigm in clinical studies [ 35 , 36 ], and there have already been radiomics studies published for COVID-19 survival prognosis and illness severity identification [ [12] , [13] , [14] , [15] ]. However, current radiomics studies on the differentiation of COVID-19 from other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19, and the evaluation of radiomic feature among different classifiers on COVID-19, are scarce.…”

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“…And Chen et al found that radiomics model based on CT images is a feasible and promising method for monitoring poor prognostic outcome in patients with COVID-19 [17][18] . Besides, radiomics has been also used to identify focal organizing pneumonia and peripheral lung adenocarcinoma [19] .…”