卜菊,聂生东,魏珑(上海理工大学医疗器械与食品学院, 上海 200093;山东建筑大学计算机科学与技术学院, 济南 250101)
Review of CT images-based molecular typing for lung adenocarcinoma
Bu Ju,Nie Shengdong,Wei Long(School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China)
The lung adenocarcinoma oriented median survival intervals can be significantly extended through specific targeted therapy based on the identified gene-driven following. Current biopsy is regarded as the "gold standard" for gene-driven tumor detection in clinical practice. Such invasive examination has a certain probability of misdiagnosis and missed diagnosis due to the tumor heterogeneity. Moreover, some of the molecular biology detection technologies are time-consuming and costly, such as the next generation of sequencing and fluorescence in situ hybridization. Therefore, radiogenomics has emerged and provided a new non-invasive method for the prediction of tumor molecular typing. As the most commonly-used way to monitor the lung cancer-related curative effect, computed tomography (CT) has its potential of short-term scanning, high resolution and relatively low-cost, which can carry out the tumor evaluation overall. It makes up the deficiency of biopsy to a certain extent. Thanks to the development of molecular targeted drugs in the context of lung adenocarcinoma treatment, most of researchers have been committed to using medical images to predict the molecular typing of lung adenocarcinoma. We carry out the critical review to harness CT images based molecular typing of lung adenocarcinoma. 1) Current situation of lung adenocarcinoma-oriented molecular typing and the key gene mutation types are introduced. 2) Existing CT images-related methods are divided into two categories:the correlation analysis of CT semantic features and the molecular subtype of lung adenocarcinoma, and the prediction model of molecular typing based on machine learning (ML). Among them, the ML-based prediction model is mainly introduced, which includes radiomics model and deep learning neural network model. 3) Some challenging problems are summarized in this field, and the future research direction is predicted. The correlation between semantic features and molecular typing of lung adenocarcinoma is derived of naked eyes visible tumor features. But, the predicted accuracy is still relatively low. Furthermore, the prediction model is demonstrated based on extracted features of radiomics from the segmented tumor images, and the selected radiomics features are input into the machine learning classifier to obtain the final prediction results. This method is still subject to human subjective influence to some extent, such as the stage of tumor segmentation and pre-setting features. To extract higher-level features for higher prediction accuracy, the convolutional neural network based (CNN-based) deep learning technology can beneficial for low-level features learning in tumor images. The deep learning model has less human-derived intervention, but it needs to be trained and verified through a large amount of data, and the expected effect cannot be achieved temporarily via a small sample. A challenging issue is to be tackled for the complex status of genetic mutations in lung adenocarcinoma and a complete and standardized database for generalization ability. With the database development of gradual standardization and expansion, future research direction can be focused on the construction of large-sample deep learning prediction model based on the integration of multiple medical images. To achieve noninvasive and accurate prediction of molecular typing of lung adenocarcinoma, the model optimization should combine clinical information, CT semantic features and radiomics features further.