COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14, 339 patients. (June 2022)

• Record Type: Journal Article
• Authors: Shiri, Isaac; Salimi, Yazdan; Pakbin, Masoumeh; Hajianfar, Ghasem; Avval, Atlas Haddadi; Sanaat, Amirhossein; Mostafaei, Shayan; Akhavanallaf, Azadeh; Saberi, Abdollah; Mansouri, Zahra; Askari, Dariush; Ghasemian, Mohammadreza; Sharifipour, Ehsan; Sandoughdaran, Saleh; Sohrabi, Ahmad; Sadati, Elham; Livani, Somayeh; Iranpour, Pooya; Kolahi, Shahriar; Khateri, Maziar; Bijari, Salar; Atashzar, Mohammad Reza; Shayesteh, Sajad P.; Khosravi, Bardia; Babaei, Mohammad Reza; Jenabi, Elnaz; Hasanian, Mohammad; Shahhamzeh, Alireza; Foroghi Ghomi, Seyaed Yaser; Mozafari, Abolfazl; Teimouri, Arash; Movaseghi, Fatemeh; Ahmari, Azin; Goharpey, Neda; Bozorgmehr, Rama; Shirzad-Aski, Hesamaddin; Mortazavi, Roozbeh; Karimi, Jalal; Mortazavi, Nazanin; Besharat, Sima; Afsharpad, Mandana; Abdollahi, Hamid; Geramifar, Parham; Radmard, Amir Reza; Arabi, Hossein; Rezaei-Kalantari, Kiara; Oveisi, Mehrdad; Rahmim, Arman; Zaidi, Habib;
• Is Part Of: Computers in biology and medicine. Volume 145(2022)
• Publication Date: 2022-06
• Subjects: X-ray CT -- COVID-19 -- Radiomics -- Prognosis -- Machine learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105467
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Graphical abstract: Image 1 Highlights: CT images of 14′339 COVID-19 patients with known outcome from 19 centers were enrolled. 28 combinations of feature selection and classification approaches were implemented. The models were evaluated using 10 different splitting and cross-validation strategies. Lung CT radiomics features are promising for generalizable prognostic modeling.