Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model.

Authors:
Dr Jian Liu, PhD
Dr Jian Liu, PhD
Curtin University
Senior Lecturer
Nanoporous materials
Perth, WA | Australia
Jingjing Cui
Jingjing Cui
Mississippi State University
United States
Fei Liu
Fei Liu
West China Hospital
Edina | United States
Yixuan Yuan
Yixuan Yuan
Northwestern Polytechnical University
China
Feng Guo
Feng Guo
The Pennsylvania State University
United States
Guanglei Zhang
Guanglei Zhang
Tsinghua University
China

Med Phys 2019 Apr 19. Epub 2019 Apr 19.

Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.

Purpose: Histological subtypes of non-small cell lung cancer (NSCLC) are crucial for systematic treatment decisions. However, the current studies which used noninvasive radiomic methods to classify NSCLC histology subtypes mainly focused on two main subtypes: squamous cell carcinoma (SCC) and adenocarcinoma (ADC), while multi-subtype classifications that included the other two subtypes of NSCLC: large cell carcinoma (LCC) and not otherwise specified (NOS), were very few in the previous studies. The aim of this work was to establish a multi-subtype classification model for the four main subtypes of NSCLC and improve the classification performance and generalization ability compared with previous studies.

Methods: In this work, we extracted 1029 features from regions of interest in computed tomography (CT) images of 349 patients from two different datasets using radiomic methods. Based on "three-in-one" concept, we proposed a model called SLS wrapping three algorithms, synthetic minority oversampling technique, ℓ2,1-norm minimization, and support vector machines, into one hybrid technique to classify the four main subtypes of NSCLC: SCC, ADC, LCC, and NOS, which could cover the whole range of NSCLC.

Results: We analyzed the 247 features obtained by dimension reduction, and found that the extracted features from three methods: first order statistics, gray level co-occurrence matrix, and gray level size zone matrix, were more conducive to the classification of NSCLC subtypes. The proposed SLS model achieved an average classification accuracy of 0.89 on the training set (95% confidence interval [CI]: 0.846 to 0.912) and a classification accuracy of 0.86 on the test set (95% CI: 0.779 to 0.941).

Conclusions: The experiment results showed that the subtypes of NSCLC could be well classified by radiomic method. Our SLS model can accurately classify and diagnose the four subtypes of NSCLC based on CT images, and thus it has the potential to be used in the clinical practice to provide valuable information for lung cancer treatment and further promote the personalized medicine.

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Source
https://onlinelibrary.wiley.com/doi/abs/10.1002/mp.13551
Publisher Site
http://dx.doi.org/10.1002/mp.13551DOI Listing
April 2019
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