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An investigation of machine learning methods in delta-radiomics feature analysis.

Publication ,  Journal Article
Chang, Y; Lafata, K; Sun, W; Wang, C; Chang, Z; Kirkpatrick, JP; Yin, F-F
Published in: PLoS One
2019

PURPOSE: This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models. METHODS: The pre-treatment, one-week post-treatment, and two-month post-treatment T1 and T2 fluid-attenuated inversion recovery (FLAIR) MRI were acquired. 61 radiomic features (intensity histogram-based, morphological, and texture features) were extracted from the gross tumor volume in each image. Delta-radiomics were calculated between the pre-treatment and post-treatment features. Univariate Cox regression and 3 multivariate machine learning methods (L1-regularized logistic regression [L1-LR], random forest [RF] or neural networks [NN]) were used to select a reduced number of features, and 7 machine learning methods (L1-LR, L2-LR, RF, NN, kernel support vector machine [KSVM], linear support vector machine [LSVM], or naïve bayes [NB]) was used to build classification models for predicting OS. The performances of the total 21 model combinations built based on single-time-point radiomics (pre-treatment, one-week post-treatment, and two-month post-treatment) and delta-radiomics were evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS: For a small cohort of 12 patients, delta-radiomics resulted in significantly higher AUC than pre-treatment radiomics (p-value<0.01). One-week/two-month delta-features resulted in significantly higher AUC (p-value<0.01) than the one-week/two-month post-treatment features, respectively. 18/21 model combinations were with higher AUC from one-week delta-features than two-month delta-features. With one-week delta-features, RF feature selector + KSVM classifier and RF feature selector + NN classifier showed the highest AUC of 0.889. CONCLUSIONS: The results indicated that delta-features could potentially provide better treatment assessment than single-time-point features. The treatment assessment is substantially affected by the time point for computing the delta-features and the combination of machine learning methods for feature selection and classification.

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Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2019

Volume

14

Issue

12

Start / End Page

e0226348

Location

United States

Related Subject Headings

  • Radiosurgery
  • ROC Curve
  • Neoplasm Recurrence, Local
  • Magnetic Resonance Imaging
  • Machine Learning
  • Image Interpretation, Computer-Assisted
  • Humans
  • Glioma
  • General Science & Technology
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
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Chang, Y., Lafata, K., Sun, W., Wang, C., Chang, Z., Kirkpatrick, J. P., & Yin, F.-F. (2019). An investigation of machine learning methods in delta-radiomics feature analysis. PLoS One, 14(12), e0226348. https://doi.org/10.1371/journal.pone.0226348
Chang, Yushi, Kyle Lafata, Wenzheng Sun, Chunhao Wang, Zheng Chang, John P. Kirkpatrick, and Fang-Fang Yin. “An investigation of machine learning methods in delta-radiomics feature analysis.PLoS One 14, no. 12 (2019): e0226348. https://doi.org/10.1371/journal.pone.0226348.
Chang Y, Lafata K, Sun W, Wang C, Chang Z, Kirkpatrick JP, et al. An investigation of machine learning methods in delta-radiomics feature analysis. PLoS One. 2019;14(12):e0226348.
Chang, Yushi, et al. “An investigation of machine learning methods in delta-radiomics feature analysis.PLoS One, vol. 14, no. 12, 2019, p. e0226348. Pubmed, doi:10.1371/journal.pone.0226348.
Chang Y, Lafata K, Sun W, Wang C, Chang Z, Kirkpatrick JP, Yin F-F. An investigation of machine learning methods in delta-radiomics feature analysis. PLoS One. 2019;14(12):e0226348.

Published In

PLoS One

DOI

EISSN

1932-6203

Publication Date

2019

Volume

14

Issue

12

Start / End Page

e0226348

Location

United States

Related Subject Headings

  • Radiosurgery
  • ROC Curve
  • Neoplasm Recurrence, Local
  • Magnetic Resonance Imaging
  • Machine Learning
  • Image Interpretation, Computer-Assisted
  • Humans
  • Glioma
  • General Science & Technology
  • Algorithms