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COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients.
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.
  • Shiri I; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland.
  • Salimi Y; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland.
  • Pakbin M; Imaging Department, Qom University of Medical Sciences, Qum, Iran.
  • Hajianfar G; Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran.
  • Avval AH; School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Sanaat A; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland.
  • Mostafaei S; Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
  • Akhavanallaf A; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland.
  • Saberi A; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland.
  • Mansouri Z; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland.
  • Askari D; Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Ghasemian M; Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qum, Iran.
  • Sharifipour E; Neuroscience Research Center, Qom University of Medical Sciences, Qum, Iran.
  • Sandoughdaran S; Men's Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Sohrabi A; Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran.
  • Sadati E; Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
  • Livani S; Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran.
  • Iranpour P; Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Kolahi S; Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Khateri M; Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Tehran, Iran.
  • Bijari S; Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
  • Atashzar MR; Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran.
  • Shayesteh SP; Department of Physiology, Pharmacology and Medical Physics, Alborz University of Medical Sciences, Karaj, Iran.
  • Khosravi B; Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Babaei MR; Department of Interventional Radiology, Firouzgar Hospital, Iran University of Medical Sciences, Tehran, Iran.
  • Jenabi E; Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Hasanian M; Department of Radiology, Arak University of Medical Sciences, Arak, Iran.
  • Shahhamzeh A; Clinical Research Development Center, Qom University of Medical Sciences, Qum, Iran.
  • Foroghi Ghomi SY; Clinical Research Development Center, Shahid Beheshti Hospital, Qom University Of Medical Sciences, Qom, Iran.
  • Mozafari A; Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran.
  • Teimouri A; Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Movaseghi F; Department of Medical Sciences, Qom Branch, Islamic Azad University, Qum, Iran.
  • Ahmari A; Ayatolah Khansary Hospital, Arak University of Medical Sciences, Arak, Iran.
  • Goharpey N; Department of Radiation Oncology, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Bozorgmehr R; Clinical Research Development Unit, Shohadaye Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Shirzad-Aski H; Infectious Diseases Research Center, Golestan University of Medical Sciences, Gorgan, Iran.
  • Mortazavi R; Department of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Karimi J; Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran.
  • Mortazavi N; Dental Research Center, Golestan University of Medical Sciences, Gorgan, Iran.
  • Besharat S; Golestan Research Center of Gastroenterology and Hepatology, Golestan University of Medical Sciences, Gorgan, Iran.
  • Afsharpad M; Cancer Control Research Center, Cancer Control Foundation, Iran University of Medical Sciences, Tehran, Iran.
  • Abdollahi H; Department of Radiologic Technology, Faculty of Allied Medical Sciences, Kerman University of Medical Sciences, Kerman, Iran.
  • Geramifar P; Research Centre for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Radmard AR; Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Arabi H; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland.
  • Rezaei-Kalantari K; Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran.
  • Oveisi M; Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom.
  • Rahmim A; Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
  • Zaidi H; Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, 1211, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Gronin
Comput Biol Med ; 145: 105467, 2022 06.
Article in English | MEDLINE | ID: covidwho-1763671
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Lung Neoplasms Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article Affiliation country: J.compbiomed.2022.105467

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Lung Neoplasms Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Comput Biol Med Year: 2022 Document Type: Article Affiliation country: J.compbiomed.2022.105467