Abstract
Background
We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.
Methods
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.
Results
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.
Conclusion
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.
【저자키워드】 COVID-19, Prognosis, machine learning, Radiomics, X-ray CT, 【초록키워드】 lung, RT-PCR, sensitivity, specificity, Features, Sensitivity and specificity, Algorithm, dataset, prognostic, characteristic, COVID-19 patients, Combination, AUC, intensity, Classifier, relief, Positive test, test dataset, heterogeneous, ANOVA, clinical setting, datasets, predictive power, positive, forest, classifiers, feature, splitting, robust, Seven, Result, highest, performed, reported, evaluated, can be used, statistically significant, automatically, 【제목키워드】 Patient, dataset, prognostic, feature,