Abstract

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Title:: Prediction model for lymph node metastasis in non-small cell lung cancer

Author(s):: LU Kongyao1; HUANG Gang2; ZUO Yan1; 1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Key Laboratory of Molecular Imaging, Jiading District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai 201318, China

Keywords:: Keywords: non-small cell lung cancer radiomics lymph node metastasis machine learning prediction model

Abstract:: Abstract: In the treatment of non-small cell lung cancer, the state of lymph node metastasis is an important factor when deciding the therapeutic schedule. In order to assist clinicians in formulating a more accurate therapeutic schedule, a CT radiomics-based prediction model for lymph node metastasis in non-small cell lung cancer is developed and validated. The patient data of 134 cases meeting the requirement of experiment are selected from the NSCLC-Radiogenomics public data set in TCIA database, and then 1 648 features are extracted from the CT image data of each patient. After feature reduction and selection by feature optimization method, prediction models are established by different machine learning methods, namely na?e Bayes, linear discriminant analysis, support vector machine and Gaussian process. Finally, patient data collected from 44 cases in Shanghai Chest Hospital are used for external validation. Among the established models, the optimal model for predicting lymph node metastasis has an accuracy of 0.802 and 0.795 in training set and test set, respectively, and the AUC is 0.852 and 0.810, respectively. The experimental results show that the proposed prediction model has good classification performances, and can assist doctors in assessing the state of lymph node metastasis more accurately, so as to develop a more precise and personalized therapeutic schedule.