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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

46 Scopus Citations
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Author(s)

  • Jian Liu
  • Jingjing Cui
  • Fei Liu
  • Feng Guo
  • Guanglei Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)3091-3100
Journal / PublicationMedical Physics
Volume46
Issue number7
Online published19 Apr 2019
Publication statusPublished - Jul 2019

Abstract

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.

Research Area(s)

  • feature selection, non-small cell lung cancer (NSCLC), radiomics, subtype classification

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model. / Liu, Jian; Cui, Jingjing; Liu, Fei et al.
In: Medical Physics, Vol. 46, No. 7, 07.2019, p. 3091-3100.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review