Zheng Chang

Overview:

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.

Positions:

Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2006

University of British Columbia (Canada)

Grants:

Publications:

Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.

Magnetic resonance imaging (MRI) plays an important role in the modern radiation therapy (RT) workflow. In comparison with computed tomography (CT) imaging, which is the dominant imaging modality in RT, MRI possesses excellent soft-tissue contrast for radiographic evaluation. Based on quantitative models, MRI can be used to assess tissue functional and physiological information. With the developments of scanner design, acquisition strategy, advanced data analysis, and modeling, multiparametric MRI (mpMRI), a combination of morphologic and functional imaging modalities, has been increasingly adopted for disease detection, localization, and characterization. Integration of mpMRI techniques into RT enriches the opportunities to individualize RT. In particular, RT response assessment using mpMRI allows for accurate characterization of both tissue anatomical and biochemical changes to support decision-making in monotherapy of radiation treatment and/or systematic cancer management. In recent years, accumulating evidence have, indeed, demonstrated the potentials of mpMRI in RT response assessment regarding patient stratification, trial benchmarking, early treatment intervention, and outcome modeling. Clinical application of mpMRI for treatment response assessment in routine radiation oncology workflow, however, is more complex than implementing an additional imaging protocol; mpMRI requires additional focus on optimal study design, practice standardization, and unified statistical reporting strategy to realize its full potential in the context of RT. In this article, the mpMRI theories, including image mechanism, protocol design, and data analysis, will be reviewed with a focus on the radiation oncology field. Representative works will be discussed to demonstrate how mpMRI can be used for RT response assessment. Additionally, issues and limits of current works, as well as challenges and potential future research directions, will also be discussed.
Authors
Wang, C; Padgett, KR; Su, M-Y; Mellon, EA; Maziero, D; Chang, Z
MLA Citation
Wang, Chunhao, et al. “Multi-parametric MRI (mpMRI) for treatment response assessment of radiation therapy.Med Phys, vol. 49, no. 4, Apr. 2022, pp. 2794–819. Pubmed, doi:10.1002/mp.15130.
URI
https://scholars.duke.edu/individual/pub1493024
PMID
34374098
Source
pubmed
Published In
Med Phys
Volume
49
Published Date
Start Page
2794
End Page
2819
DOI
10.1002/mp.15130

Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma.

The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III-IV (7th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.
Authors
Rodrigues, A; Loman, K; Nawrocki, J; Hoang, JK; Chang, Z; Mowery, YM; Oyekunle, T; Niedzwiecki, D; Brizel, DM; Craciunescu, O
MLA Citation
Rodrigues, Anna, et al. “Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma.Front Oncol, vol. 11, 2021, p. 708398. Pubmed, doi:10.3389/fonc.2021.708398.
URI
https://scholars.duke.edu/individual/pub1497057
PMID
34540674
Source
pubmed
Published In
Frontiers in Oncology
Volume
11
Published Date
Start Page
708398
DOI
10.3389/fonc.2021.708398

Multimaterial three-dimensional printing in brachytherapy: Prototyping teaching tools for interstitial and intracavitary procedures in cervical cancers.

PURPOSE: As the utilization of brachytherapy procedures continues to decline in clinics, a need for accessible training tools is required to help bridge the gap between resident comfort in brachytherapy training and clinical practice. To improve the quality of intracavitary and interstitial high-dose-rate brachytherapy education, a multimaterial, modular, three-dimensionally printed pelvic phantom prototype simulating normal and cervical pathological conditions has been developed. METHODS AND MATERIALS: Patient anatomy was derived from pelvic CT and MRI scans from 50 representative patients diagnosed with localized cervical cancer. Dimensions measured from patients' uterine body and uterine canal sizes were used to construct a variety of uteri based off of the averages and standard deviations of the subjects in our study. Soft-tissue anatomy was three-dimensionally printed using Agilus blends (shore 30 and 70) and modular components using Vero (shore 85). RESULTS: The kit consists of four uteri, a standard bladder, a standard rectum, two embedded gross tumor volumes, and four clip-on gross tumor volume attachments. The three anteverted uteri in the kit are based on the smallest, the average, and the largest dimensions from our patient set, whereas the retroverted uterus assumes average dimensions. CONCLUSIONS: This educational high-dose-rate gynecological pelvic phantom is an accessible and cost-effective way to improve radiation oncology resident training in intracavitary/interstitial brachytherapy cases. Implementation of this phantom in resident education will allow for more thorough and comprehensive physician training through its ability to transform the patient scenario. It is expected that this tool will help improve confidence and efficiency when performing brachytherapy procedures in patients.
Authors
Campelo, S; Subashi, E; Meltsner, SG; Chang, Z; Chino, J; Craciunescu, O
MLA Citation
Campelo, Sabrina, et al. “Multimaterial three-dimensional printing in brachytherapy: Prototyping teaching tools for interstitial and intracavitary procedures in cervical cancers.Brachytherapy, vol. 19, no. 6, Nov. 2020, pp. 767–76. Pubmed, doi:10.1016/j.brachy.2020.07.013.
URI
https://scholars.duke.edu/individual/pub1459498
PMID
32893145
Source
pubmed
Published In
Brachytherapy
Volume
19
Published Date
Start Page
767
End Page
776
DOI
10.1016/j.brachy.2020.07.013

Remaining Useful Lifetime Prediction for the Equipment with the Random Failure Threshold

Prognostics and health management (PHM) technology is widely used in industrial production, and its core is to predict the remaining useful life (RUL) of the equipment. For the existing research of RUL prediction, the impact of random failure threshold (RFT) has not been analyzed. To solve this problem, an RUL prediction method based on the Kalman filter is proposed. Firstly, a nonlinear Wiener degradation model is built in this paper. Then, the parameters of the degradation model are estimated by the maximum likelihood estimation (MLE) method and the distribution coefficients of RFT are calculated by the expected maximum (EM) algorithm. In addition, the Kalman filtering technique is applied to renewal the degradation states by obtaining condition monitoring (CM) data. Finally, the analytical expression of probability density function (PDF) for the RUL is derived by considering the RFT. The simulation example shows that the method in this paper has advantages of RUL prediction, and thus can have potentially engineering application value.
Authors
Wang, Z; Chen, Y; Cai, Z; Chang, Z; Wang, T
MLA Citation
Wang, Z., et al. “Remaining Useful Lifetime Prediction for the Equipment with the Random Failure Threshold.” 2019 Prognostics and System Health Management Conference, Phm Qingdao 2019, 2019. Scopus, doi:10.1109/PHM-Qingdao46334.2019.8942960.
URI
https://scholars.duke.edu/individual/pub1451119
Source
scopus
Published In
2019 Prognostics and System Health Management Conference, Phm Qingdao 2019
Published Date
DOI
10.1109/PHM-Qingdao46334.2019.8942960

Equipment Maintenance Decision Model Based on Degradation Data and Failure Data

The degradation of data and failure data is used to make the maintenance decision of equipment in this paper. Firstly, the Wiener process is used to build the degradation model which is according to the degradation data of the equipment. Then, the random distribution coefficient of failure threshold is estimated by the failure data of the equipment, and we also derive the analytical expression of the remaining useful lifetime (RUL) distribution of the equipment. Finally, the maintenance decision model is established according to the renewal-reward theory and RUL prediction data which can achieve optimal maintenance strategy of equipment. The simulation example shows that the method in this paper can prolong the run time cost of equipment and effectively reduce the life cycle which has broad application prospects.
Authors
Wang, ZZ; Chen, YX; Cai, ZY; Xiang, HC; Chang, Z
MLA Citation
Wang, Z. Z., et al. “Equipment Maintenance Decision Model Based on Degradation Data and Failure Data.” Proceedings of 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, Qr2mse 2019, 2019, pp. 237–42. Scopus, doi:10.1109/QR2MSE46217.2019.9021194.
URI
https://scholars.duke.edu/individual/pub1451118
Source
scopus
Published In
Proceedings of 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, Qr2mse 2019
Published Date
Start Page
237
End Page
242
DOI
10.1109/QR2MSE46217.2019.9021194