Recurrence Detection of Non-small cell Lung Cancer (NSCLC) Tumors Using Imaging and Clinical Features

Abstract

Introduction: Evaluation of treatment response is one of the most challenging tasks in the treatment planning of cancer cases. Regarding the significant effect of tumor recurrence in the treatment planning of patients with lung cancer, finding an approach to predict the recurrence of these tumors is of great importance. Nowadays, this process is done experimentally, and its accuracy depends on the experience and proficiency of the oncologist. This study aimed to provide an automated method to detect the recurrence of lung cancer based on imaging and clinical features. Method: Our proposed method was evaluated in 162 patients with non-small cell lung cancer (NSCLC) using the NSCLC radiogenomic database in the Cancer Imaging Archive (TCIA) portal. After pre-processing, segmentation was performed using the Otsu method. In the next step, the radiomic features were extracted using pre-trained AlexNet and GoogleNet models, and along with clinical features, they were used to detect lesion recurrence. Finally, all cases were classified into two classes using machine learning methods. Results: The proposed method used clinical and deep features. The classification was done using various models, and the accuracy of the support vector machine by AlexNet features resulted in the highest performance. The mean values of accuracy, sensitivity, and specificity for this model are 99.76, 99.77, and 99.76%, respectively. Conclusion: The main finding of this study was revealing the capability of deep learning methods in extracting features from medical images. For example, the AlexNet was able to extract features from CT images of NSCLC patients, which are very helpful in the recurrence prediction of these lesions.

Keywords

• non-small cell lung cancer
• image processing
• machine learning
• deep learning
• radiomics features