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Shayesteh, S P, Shiri, I, Karami, A H, Hashemian, R, Kooranifar, S, Ghaznavi, H, Shakeri-Zadeh, A. (1399). Predicting Lung Cancer Patients’ Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework. سامانه مدیریت نشریات علمی, 10(4), 479-492. doi: 10.31661/jbpe.v0i0.1027
S P Shayesteh; I Shiri; A H Karami; R Hashemian; S Kooranifar; H Ghaznavi; A Shakeri-Zadeh. "Predicting Lung Cancer Patients’ Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework". سامانه مدیریت نشریات علمی, 10, 4, 1399, 479-492. doi: 10.31661/jbpe.v0i0.1027
Shayesteh, S P, Shiri, I, Karami, A H, Hashemian, R, Kooranifar, S, Ghaznavi, H, Shakeri-Zadeh, A. (1399). 'Predicting Lung Cancer Patients’ Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework', سامانه مدیریت نشریات علمی, 10(4), pp. 479-492. doi: 10.31661/jbpe.v0i0.1027
Shayesteh, S P, Shiri, I, Karami, A H, Hashemian, R, Kooranifar, S, Ghaznavi, H, Shakeri-Zadeh, A. Predicting Lung Cancer Patients’ Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework. سامانه مدیریت نشریات علمی, 1399; 10(4): 479-492. doi: 10.31661/jbpe.v0i0.1027

Predicting Lung Cancer Patients’ Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework

Journal of Biomedical Physics and Engineering
مقاله 10، دوره 10، شماره 4، آبان 2020، صفحه 479-492    اصل مقاله (1.15 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.1027
نویسندگان
S P Shayesteh1، 2؛ I Shiri3؛ A H Karami2؛ R Hashemian4؛ S Kooranifar5؛ H Ghaznavi* 6؛ A Shakeri-Zadeh2
1PhD, Department of Physiology, Pharmacology and medical physics, Faculty of Medicine, Alborz University of Medical Sciences, Karaj. Iran
2PhD, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
3MSc, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran
4MD, PhD, US oncology Inc, Cincinnati, OH, USA
5MD, Department of Pulmonary Sciences, Hazrat Rasoul Akram Hospital, Iran University of Medical Sciences (IUMS), Tehran, Iran
6MD, Zahedan University of Medical Sciences (ZaUMS), Zahedan, Iran
چکیده
Background: Selection of the best treatment modalities for lung cancer depends on many factors, like survival time, which are usually determined by imaging.
Objectives: To predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models.
Material and Methods: Fifty-nine patients with primary lung adenocarcinoma were included in this retrospective study and pre-treatment contrast-enhanced CT images were acquired. The patients lived more than 2 years were classified as the ‘Alive’ class and otherwise as the ‘Dead’ class. In our proposed quantitative radiomic framework, we first extracted the associated regions of each lung lesion from pre-treatment CT images for each patient via grow cut segmentation algorithm. Then, 40 radiomic features were extracted from the segmented lung lesions. In order to enhance the generalizability of the classification models, the mutual information-based feature selection method was applied to each feature vector. We investigated the performance of six logistic regression-based classification models.
Results: It was observed that the mutual information feature selection method can help the classifier to achieve better predictive results. In our study, the Logistic regression (LR) and Dual Coordinate Descent method for Logistic Regression (DCD-LR) models achieved the best results indicating that these classification models have strong potential for classifying the more important class (i.e., the ‘Alive’ class).
Conclusion: The proposed quantitative radiomic framework yielded promising results, which can guide physicians to make better and more precise decisions and increase the chance of treatment success.
کلیدواژه‌ها
Radiomics؛ Lung Cancer؛ Survival analysis؛ Computed Tomography
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