Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226059105> ?p ?o ?g. }
- W4226059105 endingPage "105467" @default.
- W4226059105 startingPage "105467" @default.
- W4226059105 abstract "We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance.Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients." @default.
- W4226059105 created "2022-05-05" @default.
- W4226059105 creator A5002885963 @default.
- W4226059105 creator A5003433949 @default.
- W4226059105 creator A5004193178 @default.
- W4226059105 creator A5007055141 @default.
- W4226059105 creator A5007891293 @default.
- W4226059105 creator A5009538885 @default.
- W4226059105 creator A5017888054 @default.
- W4226059105 creator A5018450250 @default.
- W4226059105 creator A5021438906 @default.
- W4226059105 creator A5023542749 @default.
- W4226059105 creator A5025683930 @default.
- W4226059105 creator A5029665643 @default.
- W4226059105 creator A5036586704 @default.
- W4226059105 creator A5036836472 @default.
- W4226059105 creator A5038329042 @default.
- W4226059105 creator A5039181443 @default.
- W4226059105 creator A5039467855 @default.
- W4226059105 creator A5040861172 @default.
- W4226059105 creator A5041147908 @default.
- W4226059105 creator A5041981029 @default.
- W4226059105 creator A5043140418 @default.
- W4226059105 creator A5043174153 @default.
- W4226059105 creator A5043250262 @default.
- W4226059105 creator A5044859383 @default.
- W4226059105 creator A5045836855 @default.
- W4226059105 creator A5047145471 @default.
- W4226059105 creator A5048817483 @default.
- W4226059105 creator A5049491814 @default.
- W4226059105 creator A5049895715 @default.
- W4226059105 creator A5051197496 @default.
- W4226059105 creator A5052594901 @default.
- W4226059105 creator A5052997773 @default.
- W4226059105 creator A5054247879 @default.
- W4226059105 creator A5054918541 @default.
- W4226059105 creator A5057433079 @default.
- W4226059105 creator A5059624748 @default.
- W4226059105 creator A5062382836 @default.
- W4226059105 creator A5062949279 @default.
- W4226059105 creator A5064324055 @default.
- W4226059105 creator A5066149951 @default.
- W4226059105 creator A5067475611 @default.
- W4226059105 creator A5067594915 @default.
- W4226059105 creator A5067903849 @default.
- W4226059105 creator A5069570780 @default.
- W4226059105 creator A5070346261 @default.
- W4226059105 creator A5081306836 @default.
- W4226059105 creator A5084020872 @default.
- W4226059105 creator A5089587974 @default.
- W4226059105 creator A5091064624 @default.
- W4226059105 date "2022-06-01" @default.
- W4226059105 modified "2023-10-06" @default.
- W4226059105 title "COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients" @default.
- W4226059105 cites W2006617902 @default.
- W4226059105 cites W2050167599 @default.
- W4226059105 cites W2107665951 @default.
- W4226059105 cites W2763355946 @default.
- W4226059105 cites W2767128594 @default.
- W4226059105 cites W2915618956 @default.
- W4226059105 cites W2954503654 @default.
- W4226059105 cites W2963464382 @default.
- W4226059105 cites W2998789541 @default.
- W4226059105 cites W3006645647 @default.
- W4226059105 cites W3009875419 @default.
- W4226059105 cites W3010061930 @default.
- W4226059105 cites W3013294478 @default.
- W4226059105 cites W3013585706 @default.
- W4226059105 cites W3017272320 @default.
- W4226059105 cites W3019336217 @default.
- W4226059105 cites W3020653337 @default.
- W4226059105 cites W3025948831 @default.
- W4226059105 cites W3026085071 @default.
- W4226059105 cites W3034988614 @default.
- W4226059105 cites W3035941308 @default.
- W4226059105 cites W3040075864 @default.
- W4226059105 cites W3041041945 @default.
- W4226059105 cites W3044185109 @default.
- W4226059105 cites W3044994436 @default.
- W4226059105 cites W3045596310 @default.
- W4226059105 cites W3047349135 @default.
- W4226059105 cites W3048479457 @default.
- W4226059105 cites W3049757379 @default.
- W4226059105 cites W3080709651 @default.
- W4226059105 cites W3083718880 @default.
- W4226059105 cites W3083792018 @default.
- W4226059105 cites W3087434656 @default.
- W4226059105 cites W3090218328 @default.
- W4226059105 cites W3092234733 @default.
- W4226059105 cites W3093272769 @default.
- W4226059105 cites W3093455605 @default.
- W4226059105 cites W3094035514 @default.
- W4226059105 cites W3094239664 @default.
- W4226059105 cites W3095989031 @default.
- W4226059105 cites W3101024103 @default.
- W4226059105 cites W3102547009 @default.
- W4226059105 cites W3104739447 @default.
- W4226059105 cites W3105326651 @default.