Voxel-level forecast system for lesion development in patients with COVID-19
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Abstract
The global spread of COVID-19 seriously endangers human health and even lives. By predicting patients’ individualized disease development and further performing intervention in time, we may rationalize scarce medical resources and reduce mortality. Based on 1337 multi-stage (≥3) high-resolution chest computed tomography (CT) images of 417 infected patients from three centers in the epidemic area, we proposed a random forest + cellular automata (RF+CA) model to forecast voxel-level lesion development of patients with COVID-19. The model showed a promising prediction performance (Dice similarity coefficient [DSC] = 71.1%, Kappa coefficient = 0.612, Figure of Merit [FoM] = 0.257, positional accuracy [PA] = 3.63) on the multicenter dataset. Using this model, multiple driving factors for the development of lesions were determined, such as distance to various interstitials in the lung, distance to the pleura, etc. The driving processes of these driving factors were further dissected and explained in depth from the perspective of pathophysiology, to explore the mechanism of individualized development of COVID-19 disease. The complete codes of the forecast system are available at https://github.com/keyunj/VVForecast_covid19 .
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SciScore for 10.1101/2020.12.17.20248377: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources All these parameter estimation and transformation procedure were achieved using SimpleElastix in Python39. Python39suggested: NoneTo extract the distance map of TAI, the centerlines were extracted first using the morphological thinning algorithm, and followed by the Euclidean distance transformation algorithm from Scipy in Python40. Scipysuggested: (SciPy, RRID:SCR_008058)Python40suggested: NoneResults from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We …SciScore for 10.1101/2020.12.17.20248377: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources All these parameter estimation and transformation procedure were achieved using SimpleElastix in Python39. Python39suggested: NoneTo extract the distance map of TAI, the centerlines were extracted first using the morphological thinning algorithm, and followed by the Euclidean distance transformation algorithm from Scipy in Python40. Scipysuggested: (SciPy, RRID:SCR_008058)Python40suggested: NoneResults from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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