Improving the diagnosis of lung cancer based on multiparametric MRI

Xinhui Wang; Houjin Chen; Yanfeng Li; Chen Yan; Yahui Peng; Xinchun Li

doi:10.1117/12.2557474

3 January 2020 Improving the diagnosis of lung cancer based on multiparametric MRI

Xinhui Wang, Houjin Chen, Yanfeng Li, Chen Yan, Yahui Peng, Xinchun Li

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113731Z (2020) https://doi.org/10.1117/12.2557474
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

In order to improve the diagnostic effect of MRI images, a multiparametric magnetic resonance imaging (MRI) based classification method was proposed in this paper. The study included 85 patients. The radiomics method was used to extract morphological and texture features, while Apparent diffusion coefficient (ADC) was used as functional feature.Three classification methods, including Linear Discriminate Analysis (LDA), Support Vector Machine (SVM) and Random Forest (RF), were used to distinguish benign and malignant of pulmonary lesions. The performance of multiparametric MRI sequences and single sequences were compared. The experimental results shown that multiparametric MRI classification with SVM classifier had best performence (AUC=0.82±0.03), indicating that multiparametric MR diagnosis has great potential.

Citation Download Citation

Xinhui Wang, Houjin Chen, Yanfeng Li, Chen Yan, Yahui Peng, and Xinchun Li "Improving the diagnosis of lung cancer based on multiparametric MRI", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731Z (3 January 2020); https://doi.org/10.1117/12.2557474

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