Predicting lung cancer treatment response from CT images using deep learning

Abstract

Lung cancer is the deadliest type of cancer and is one of the most frequently occurring cancers. It is primarily diagnosed in later stages when treatment becomes difficult. For better treatment and higher chances of survival, the treatment response of lung cancer patients needs to be analyzed to check whether the patients are responding to the treatment or not. This analysis can be done with the help of follow-up computed tomography (CT) imaging before and after the treatment. However, manually analyzing the baseline and post-treatment CT scan images of so many lung cancer patients is a tedious task. This study proposes an intuitive approach based on deep learning to analyze lung cancer through CT scan images before and after the treatment. In this approach, we utilized a segmentation network to segment the lung tumor in the follow-up CT images. The segmented tumor is then analyzed to check the treatment effect, as suggested by the Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. The segmentation network combines a vision transformer and a convolutional neural network. The segmentation network is first trained on a public dataset and then fine-tuned on the local dataset to improve the segmentation performance. For this study, we have collected a lung cancer dataset from an Indian hospital. The dataset is divided into two parts dataset I and dataset II. Dataset I consists of 100 CT scans, which we use to fine-tune the proposed segmentation network. Dataset II comprises 220 CT scans of 110 patients, consisting of baseline and post-treatment scans. We use dataset II for testing. We achieved significant performance.