S P Shayesteh, I Shiri, A H Karami, R Hashemian, S Kooranifar, H Ghaznavi, A Shakeri-Zadeh
PhD, Department of Physiology, Pharmacology and medical physics, Faculty of Medicine, Alborz University of Medical Sciences, Karaj. Iran.
PhD, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences (IUMS), Tehran, Iran.
J Biomed Phys Eng. 2020 Aug 1;10(4):479-492. doi: 10.31661/JBPE.V0I0.1027. eCollection 2020 Aug.
Selection of the best treatment modalities for lung cancer depends on many factors, like survival time, which are usually determined by imaging.
To predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models.
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.
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).
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.
肺癌最佳治疗方式的选择取决于许多因素，如生存时间，而生存时间通常由影像学确定。
利用放射组学和基于逻辑回归的分类模型的优势预测肺癌患者的生存时间。
本回顾性研究纳入了59例原发性肺腺癌患者，并获取了治疗前的对比增强CT图像。生存超过2年的患者被分类为“存活”组，否则为“死亡”组。在我们提出的定量放射组学框架中，我们首先通过生长切割分割算法从每位患者的治疗前CT图像中提取每个肺病变的相关区域。然后，从分割后的肺病变中提取40个放射组学特征。为了提高分类模型的通用性，将基于互信息的特征选择方法应用于每个特征向量。我们研究了六种基于逻辑回归的分类模型的性能。
观察到互信息特征选择方法可以帮助分类器获得更好的预测结果。在我们的研究中，逻辑回归（LR）和逻辑回归的对偶坐标下降法（DCD-LR）模型取得了最佳结果，表明这些分类模型在对更重要的类别（即“存活”组）进行分类方面具有强大的潜力。
所提出的定量放射组学框架产生了有前景的结果，可指导医生做出更好、更精确的决策，并增加治疗成功的机会。
