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


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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Publication Info

Chang, Yushi, Kyle Lafata, Wenzheng Sun, Chunhao Wang, Zheng Chang, John P Kirkpatrick and Fang-Fang Yin (2019). An investigation of machine learning methods in delta-radiomics feature analysis. PloS one, 14(12). p. e0226348. 10.1371/journal.pone.0226348 Retrieved from

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Kyle Jon Lafata

Thaddeus V. Samulski Associate Professor of Radiation Oncology

Kyle Lafata is the Thaddeus V. Samulski Associate Professor at Duke University in the Departments of Radiation Oncology, Radiology, Medical Physics and Electrical & Computer Engineering. After earning his PhD in Medical Physics in 2018, he completed postdoctoral training at the U.S. Department of Veterans Affairs in the Big Data Scientist Training Enhancement Program. Prof. Lafata has broad expertise in imaging science, digital pathology, computer vision, biophysics, and applied mathematics. His dissertation work focused on the applied analysis of stochastic differential equations and high-dimensional radiomic phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization, and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems). 

Prof. Lafata has worked in various areas of computational medicine and biology, resulting in 39 peer-reviewed journal publications, 15 invited talks, and more than 50 national conference presentations. At Duke, the Lafata Lab focuses on the theory, development, and application of multiscale computational biomarkers. Using computational and mathematical methods, they study the appearance and behavior of disease across different physical length-scales (i.e., radiomics ~10−3 m, pathomics ~10−6 m, and genomics ~10−9 m) and time-scales (e.g., the natural history of disease, response to treatment). The overarching goal of the lab is to develop and apply new technology that transforms imaging into basic science findings and computational biomarker discovery.


Chunhao Wang

Assistant Professor of Radiation Oncology
  • Deep learning methods for image-based radiotherapy outcome prediction and assessment
  • Machine learning in outcome modelling
  • Automation in radiotherapy planning and delivery


Zheng Chang

Professor of Radiation Oncology

Dr. Chang's research interests include radiation therapy treatment assessment using MR quantitative imaging, image guided radiation therapy (IGRT), fast MR imaging using parallel imaging and strategic phase encoding, and motion management for IGRT.


John P. Kirkpatrick

Professor of Radiation Oncology

Malignant and benign tumors of the brain, spine and base of skull. Mathematical modelling of tumor metabolism, mass transfer and the response to ionizing radiation. Enhancing clinical outcome in stereotactic radiosurgery, fractionated stereotactic radiotherapy and stereotactic body radiotherapy.


Fang-Fang Yin

Gustavo S. Montana Distinguished Professor of Radiation Oncology

Stereotactic radiosurgery, Stereotactic body radiation therapy, treatment planning optimization, knowledge guided radiation therapy, intensity-modulated radiation therapy, image-guided radiation therapy, oncological imaging and informatics

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