###### Abstract  
Background: Few studies have focused on predicting the overall survival (OS) of patients affected by SARS-CoV-2 (i.e., COVID-19) using radiomic features (RFs) extracted from computer tomography (CT) images. Reconstruction of CT scans might potentially affect the values of RFs. Methods: Out of 435 patients, 239 had the scans reconstructed with a single modality, and hence, were used for training/testing, and 196 were reconstructed with two modalities were used as validation to evaluate RFs robustness to reconstruction. During training, the dataset was split into train/test using a 70/30 proportion, randomizing the procedure 100 times to obtain 100 different models. In all cases, RFs were normalized using the z-score and then given as input into a Cox proportional-hazards model regularized with the Least Absolute Shrinkage and Selection Operator (LASSO-Cox), used for feature selection and developing a robust model. The RFs retained multiple times in the models were also included in a final LASSO-Cox for developing the predictive model. Thus, we conducted sensitivity analysis increasing the number of retained RFs with an occurrence cut-off from 11% to 60%. The Bayesian information criterion (BIC) was used to identify the cut-off to build the optimal model. Results: The best BIC value indicated 45% as the optimal occurrence cut-off, resulting in five RFs used for generating the final LASSO-Cox. All the Kaplan-Meier curves of training and validation datasets were statistically significant in identifying patients with good and poor prognoses, irrespective of CT reconstruction. Conclusions: The final LASSO-Cox model maintained its predictive ability for predicting the OS in COVID-19 patients irrespective of CT reconstruction algorithms. CT images; overall survival; radiomic features; LASSO-Cox +
Footnote †: journal: Medical Imaging
through the use of artificial intelligence (AI) as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care [2]. AI-based solutions have been implemented in several medical sectors, including image analysis [3; 4]. In this landscape, widespread interest has developed towards using radiomics, which is the extraction of quantitative measurements from radiological images [5]. Indeed, radiomic feature (RF) analysis has proved that its application can provide an objective insight into the pathophysiology of tissues, such as inhomogeneities in the gray level distribution within lesions, otherwise not quantifiable when relying exclusively on human perception [6]. In patients affected by SARS-CoV-2, radiomic-based tools have been developed to address different clinical objectives and predict various endpoints of interest, including the identification of patients in severe and critical conditions [7], the prediction of mortality disregarding the time to event [8] and, more rarely, the prediction of high-/low-risk subgroups obtained from multivariate survival analysis methods [9; 10]. In this context, a recent meta-analysis showed that CT-based RF models could successfully differentiate COVID-19 from other viral pneumonia, with a pooled sensitivity of 0.885 (95% CI: 0.818-0.929) and a pooled specificity of 0.811 (95% CI: 0.667-0.902) [11]. These analyses have been carried out relying primarily on clinical variables (i.e., comorbidities and laboratory findings) alone or in conjunction with radiomic variables extracted from CT scans or X-ray projections. In most studies, the investigated cohort comprises a small number of patients, often of a specific ethnic group, and with a relatively short follow-up. A critical point is that the use of RFs in the analysis pipeline works best when focusing on the texture of the specific VOIs. However, manual delineation represents one of the most limiting factors in a busy clinical department, being a complex, time-consuming, and labor-intensive task [12].