Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Bin Zhang
  • Fusheng Ouyang
  • Dongsheng Gu
  • Yuhao Dong
  • Lu Zhang
  • Xiaokai Mo
  • Wenhui Huang
  • Jie Tian
  • Shuixing Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)21-27
Journal / PublicationCancer Letters
Volume403
Online published10 Jun 2017
Publication statusPublished - 10 Sep 2017

Abstract

We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 ± 0.0069; test error, 0.3135 ± 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 ± 0.0095; test error, 0.3384 ± 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 ± 0.0096; test error, 0.3985 ± 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice.

Research Area(s)

  • Imaging, Machine-learning, Nasopharyngeal carcinoma, Radiomics

Citation Format(s)

Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. / Zhang, Bin; He, Xin; Ouyang, Fusheng; Gu, Dongsheng; Dong, Yuhao; Zhang, Lu; Mo, Xiaokai; Huang, Wenhui; Tian, Jie; Zhang, Shuixing.

In: Cancer Letters, Vol. 403, 10.09.2017, p. 21-27.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review