Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning

Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning Publisher Pubmed

Shiri I¹ ; Salimi Y¹ ; Pakbin M² ; Hajianfar G³ ; Avval AH⁴ ; Sanaat A¹ ; Mostafaei S⁵ ; Akhavanallaf A¹ ; Saberi A¹ ; Mansouri Z¹ ; Askari D⁶ ; Ghasemian M⁷ ; Sharifipour E⁸ ; Sandoughdaran S⁹ Show All Authors

Shiri I¹
Salimi Y¹
Pakbin M²
Hajianfar G³
Avval AH⁴
Sanaat A¹
Mostafaei S⁵
Akhavanallaf A¹
Saberi A¹
Mansouri Z¹
Askari D⁶
Ghasemian M⁷
Sharifipour E⁸
Sandoughdaran S⁹
Sohrabi A¹⁰
Sadati E¹¹
Livani S¹²
Iranpour P¹³
Kolahi S¹⁴
Khateri M¹⁵
Bijari S¹¹
Atashzar MR¹⁶
Shayesteh SP¹⁷
Khosravi B¹⁸
Babaei MR¹⁹
Jenabi E²⁰
Hasanian M²¹
Shahhamzeh A²²
Foroghi Ghomi SY²³
Mozafari A²⁴
Teimouri A¹³
Movaseghi F²⁴
Ahmari A²⁵
Goharpey N²⁶
Bozorgmehr R²⁷
Shirzadaski H²⁸
Mortazavi R²⁹
Karimi J³⁰
Mortazavi N³¹
Besharat S³²
Afsharpad M¹⁰
Abdollahi H³³
Geramifar P²⁰
Radmard AR¹⁸
Arabi H¹
Rezaeikalantari K³
Oveisi M³⁴
Rahmim A^{35, 36}
Zaidi H^{1, 37, 38, 39}

Source: Computers in Biology and Medicine Published:2022

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. © 2022 The Authors

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Style	Citing Format
MLA	Shiri I, et al.. "Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning." Computers in Biology and Medicine, vol. 145, no. , 2022, pp. -.
APA	Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, ... Zaidi H (2022). Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning. Computers in Biology and Medicine, 145(), -.
Chicago	Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, et al.. "Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning." Computers in Biology and Medicine 145, no. (2022): -.
Harvard	Shiri I et al. (2022) 'Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning', Computers in Biology and Medicine, 145(), pp. -.
Vancouver	Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, et al.. Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning. Computers in Biology and Medicine. 2022;145():-.
BibTex	@article{ author = {Shiri I and Salimi Y and Pakbin M and Hajianfar G and Avval AH and Sanaat A and Mostafaei S and Akhavanallaf A and Saberi A and Mansouri Z and Askari D and Ghasemian M and Sharifipour E and Sandoughdaran S and Sohrabi A and Sadati E and Livani S and Iranpour P and Kolahi S and Khateri M and Bijari S and Atashzar MR and Shayesteh SP and Khosravi B and Babaei MR and Jenabi E and Hasanian M and Shahhamzeh A and Foroghi Ghomi SY and Mozafari A and Teimouri A and Movaseghi F and Ahmari A and Goharpey N and Bozorgmehr R and Shirzadaski H and Mortazavi R and Karimi J and Mortazavi N and Besharat S and Afsharpad M and Abdollahi H and Geramifar P and Radmard AR and Arabi H and Rezaeikalantari K and Oveisi M and Rahmim A and Zaidi H}, title = {Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning}, journal = {Computers in Biology and Medicine}, volume = {145}, number = {}, pages = {-}, year = {2022} }
RIS	TY - JOUR AU - Shiri I AU - Salimi Y AU - Pakbin M AU - Hajianfar G AU - Avval AH AU - Sanaat A AU - Mostafaei S AU - Akhavanallaf A AU - Saberi A AU - Mansouri Z AU - Askari D AU - Ghasemian M AU - Sharifipour E AU - Sandoughdaran S AU - Sohrabi A AU - Sadati E AU - Livani S AU - Iranpour P AU - Kolahi S AU - Khateri M AU - Bijari S AU - Atashzar MR AU - Shayesteh SP AU - Khosravi B AU - Babaei MR AU - Jenabi E AU - Hasanian M AU - Shahhamzeh A AU - Foroghi Ghomi SY AU - Mozafari A AU - Teimouri A AU - Movaseghi F AU - Ahmari A AU - Goharpey N AU - Bozorgmehr R AU - Shirzadaski H AU - Mortazavi R AU - Karimi J AU - Mortazavi N AU - Besharat S AU - Afsharpad M AU - Abdollahi H AU - Geramifar P AU - Radmard AR AU - Arabi H AU - Rezaeikalantari K AU - Oveisi M AU - Rahmim A AU - Zaidi H TI - Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning JO - Computers in Biology and Medicine VL - 145 IS - SP - EP - PY - 2022 ER -

Style

Citing Format

MLA

Shiri I, et al.. "Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning." Computers in Biology and Medicine, vol. 145, no. , 2022, pp. -.

APA

Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, ... Zaidi H (2022). Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning. Computers in Biology and Medicine, 145(), -.

Chicago

Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, Mostafaei S, et al.. "Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning." Computers in Biology and Medicine 145, no. (2022): -.

Harvard

Shiri I et al. (2022) 'Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning', Computers in Biology and Medicine, 145(), pp. -.

Vancouver

Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval AH, Sanaat A, et al.. Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning. Computers in Biology and Medicine. 2022;145():-.

BibTex

@article{ author = {Shiri I and Salimi Y and Pakbin M and Hajianfar G and Avval AH and Sanaat A and Mostafaei S and Akhavanallaf A and Saberi A and Mansouri Z and Askari D and Ghasemian M and Sharifipour E and Sandoughdaran S and Sohrabi A and Sadati E and Livani S and Iranpour P and Kolahi S and Khateri M and Bijari S and Atashzar MR and Shayesteh SP and Khosravi B and Babaei MR and Jenabi E and Hasanian M and Shahhamzeh A and Foroghi Ghomi SY and Mozafari A and Teimouri A and Movaseghi F and Ahmari A and Goharpey N and Bozorgmehr R and Shirzadaski H and Mortazavi R and Karimi J and Mortazavi N and Besharat S and Afsharpad M and Abdollahi H and Geramifar P and Radmard AR and Arabi H and Rezaeikalantari K and Oveisi M and Rahmim A and Zaidi H}, title = {Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning}, journal = {Computers in Biology and Medicine}, volume = {145}, number = {}, pages = {-}, year = {2022} }

RIS

TY - JOUR
AU - Shiri I
AU - Salimi Y
AU - Pakbin M
AU - Hajianfar G
AU - Avval AH
AU - Sanaat A
AU - Mostafaei S
AU - Akhavanallaf A
AU - Saberi A
AU - Mansouri Z
AU - Askari D
AU - Ghasemian M
AU - Sharifipour E
AU - Sandoughdaran S
AU - Sohrabi A
AU - Sadati E
AU - Livani S
AU - Iranpour P
AU - Kolahi S
AU - Khateri M
AU - Bijari S
AU - Atashzar MR
AU - Shayesteh SP
AU - Khosravi B
AU - Babaei MR
AU - Jenabi E
AU - Hasanian M
AU - Shahhamzeh A
AU - Foroghi Ghomi SY
AU - Mozafari A
AU - Teimouri A
AU - Movaseghi F
AU - Ahmari A
AU - Goharpey N
AU - Bozorgmehr R
AU - Shirzadaski H
AU - Mortazavi R
AU - Karimi J
AU - Mortazavi N
AU - Besharat S
AU - Afsharpad M
AU - Abdollahi H
AU - Geramifar P
AU - Radmard AR
AU - Arabi H
AU - Rezaeikalantari K
AU - Oveisi M
AU - Rahmim A
AU - Zaidi H
TI - Covid-19 Prognostic Modeling Using Ct Radiomic Features and Machine Learning Algorithms: Analysis of a Multi-Institutional Dataset of 14,339 Patients: Covid-19 Prognostic Modeling Using Ct Radiomics and Machine Learning
JO - Computers in Biology and Medicine
VL - 145
IS -
SP -
EP -
PY - 2022
ER -

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