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Non-Small Cell Lung Carcinoma Histopathological Subtype Phenotyping Using High-Dimensional Multinomial Multiclass Ct Radiomics Signature Publisher Pubmed



Khodabakhshi Z1 ; Mostafaei S2, 3 ; Arabi H4 ; Oveisi M5, 6 ; Shiri I4 ; Zaidi H4, 7, 8, 9
Authors
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Authors Affiliations
  1. 1. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran
  2. 2. Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran
  3. 3. Epidemiology and Biostatistics Unit, Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, CH-1211, Switzerland
  5. 5. Department of Computer Science, University of British Columbia, Vancouver BC, Canada
  6. 6. Comprehensive Cancer Centre, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
  7. 7. Geneva University Neurocenter, Geneva University, Geneva, Switzerland
  8. 8. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
  9. 9. Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark

Source: Computers in Biology and Medicine Published:2021


Abstract

Objective: The aim of this study was to identify the most important features and assess their discriminative power in the classification of the subtypes of NSCLC. Methods: This study involved 354 pathologically proven NSCLC patients including 134 squamous cell carcinoma (SCC), 110 large cell carcinoma (LCC), 62 not other specified (NOS), and 48 adenocarcinoma (ADC). In total, 1433 radiomics features were extracted from 3D volumes of interest drawn on the malignant lesion identified on CT images. Wrapper algorithm and multivariate adaptive regression splines were implemented to identify the most relevant/discriminative features. A multivariable multinomial logistic regression was employed with 1000 bootstrapping samples based on the selected features to classify four main subtypes of NSCLC. Results: The results revealed that the texture features, specifically gray level size zone matrix features (GLSZM), were the significant indicators of NSCLC subtypes. The optimized classifier achieved an average precision, recall, F1-score, and accuracy of 0.710, 0.703, 0.706, and 0.865, respectively, based on the selected features by the wrapper algorithm. Conclusions: Our CT radiomics approach demonstrated impressive potential for the classification of the four main histological subtypes of NSCLC, It is anticipated that CT radiomics could be useful in treatment planning and precision medicine. © 2021 The Author(s)
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