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

• Main Authors: S P Shayesteh, I Shiri, A H Karami, R Hashemian, S Kooranifar, H Ghaznavi, A Shakeri-Zadeh
• Format: Article
• Language: English
• Published: Shiraz University of Medical Sciences 2020-08-01
• Series: Journal of Biomedical Physics and Engineering
• Subjects: radiomics; lung cancer; survival analysis; computed tomography
• Online Access: https://jbpe.sums.ac.ir/article_45726_ee5e01534e3a4c6a1622eb450f9311f0.pdf
• ISSN: 2251-7200; 2251-7200

Background: Selection of the best treatment modalities for lung cancer depends on many factors, like survival time, which are usually determined by imaging. <br />Objectives: To predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models.<br />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. <br />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). <br />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.