COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients
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SciScore for 10.1101/2021.12.07.21267364: (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
Experimental Models: Organisms/Strains Sentences Resources Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost (AB), Naïve Bayes (NB) and Multilayer Perceptron (MLP). ABsuggested: RRID:BDSC_203)Software and Algorithms Sentences Resources Radiomics features, including morphological (n=16), intensity (n=17), and texture features including second-order features, such as Gray Level Co-occurrence Matrix (GLCM, n=24), higher-order features namely Gray Level Size Zone Matrix (GLSZM, n=16), Neighboring Gray Tone Difference Matrix (NGTDM, n=5), Gray Level Run Length Matrix (GLRLM, n=16), and Gray Level Dependence Matrix (GLDM, … SciScore for 10.1101/2021.12.07.21267364: (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
Experimental Models: Organisms/Strains Sentences Resources Linear Discriminant Analysis (LDA), Random Forest (RF), AdaBoost (AB), Naïve Bayes (NB) and Multilayer Perceptron (MLP). ABsuggested: RRID:BDSC_203)Software and Algorithms Sentences Resources Radiomics features, including morphological (n=16), intensity (n=17), and texture features including second-order features, such as Gray Level Co-occurrence Matrix (GLCM, n=24), higher-order features namely Gray Level Size Zone Matrix (GLSZM, n=16), Neighboring Gray Tone Difference Matrix (NGTDM, n=5), Gray Level Run Length Matrix (GLRLM, n=16), and Gray Level Dependence Matrix (GLDM, n=14) were extracted in compliance with the Image Biomarker Standardization Initiative (IBSI) guidelines 53 Image Biomarker Standardization Initiativesuggested: NoneAfter applying ComBat harmonization, we divided the datasets of each center to 70/30% ComBatsuggested: (ComBat, RRID:SCR_010974)All multivariate analysis steps were performed using Python Scikit-Learn open-source library. Pythonsuggested: (IPython, RRID:SCR_001658)Scikit-Learnsuggested: (scikit-learn, RRID:SCR_002577)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Most previous studies suffered from a common limitation of COVID-19 RT-PCR not being available for the entire dataset when using multicentric data. In our study, COVID-19 positivity was confirmed by either RT-PCR or CT images, and different strategies were adopted to evaluate the models, including random splits and leave-one-center-out. We randomly split the data to train and test sets containing both CT positive and RT-PCR positive patients. Furthermore, to ensure the reproducibility of our results on RT-PCR positive patients, we split the dataset in a way that the test set consisted only of RT-PCR positive patients. To maximize the generalizability of the model and avoid overfitting on training sets, owing to variability in acquisition and reconstruction protocols, our model was developed on multicentric datasets with a wide variety of acquisition and reconstruction parameters. To test the generalizability of our model, we repeated the evaluation of our model using leave-one-center-out cross-validation. The results were reported for 10 different strategies of splitting and cross-validation scenarios. Several studies reported on the use of CT radiomics or DL algorithms for diagnostic and prognostic purposes in patients with COVID-19 58, 59, 68. However, most studies were performed using a small sample size. Overall, establishing evidence that radiomics features can help prioritize patients based on the severity of their disease and/or predicting their survival requires asses...
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: Please consider improving the rainbow (“jet”) colormap(s) used on page 25. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
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.
Results from scite Reference Check: We found no unreliable references.
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