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

Chang, Yushi

Lafata, Kyle

Sun, Wenzheng

Wang, Chunhao

Chang, Zheng

Kirkpatrick, John P

Yin, Fang-Fang

Li, Taoran

2020-03-24T13:19:02Z

2020-03-24T13:19:02Z

2019-01

2020-03-24T13:18:59Z

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.

PONE-D-19-10297

1932-6203

1932-6203

https://hdl.handle.net/10161/20269

eng

Public Library of Science (PLoS)

PloS one

10.1371/journal.pone.0226348

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

Journal article

Kirkpatrick, John P|0000-0002-4019-0350

Yin, Fang-Fang|0000-0002-2025-4740|0000-0003-1064-2149

e0226348

12

School of Medicine

Duke Cancer Institute

Radiation Oncology

Duke

Institutes and Centers

Clinical Science Departments

Neurosurgery

Duke Kunshan University Faculty

Duke Kunshan University

Staff

Physics

Trinity College of Arts & Sciences

Published

14