Qingrong Wu

Positions:

Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

B.S. 1990

Zhejiang University (China)

Ph.D. 1996

Mayo Graduate School

Grants:

Developing knowledge models to enable rapid learning in radiation therapy

Administered By
Radiation Oncology
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Decision support for dose prescription in radiation treatment planning

Administered By
Radiation Oncology
Awarded By
University of North Carolina, Charlotte
Role
Principal Investigator
Start Date
End Date

Publications:

Modeling of multiple planning target volumes for head and neck treatments in knowledge-based treatment planning.

PURPOSE: The purpose of this study is to develop an accurate and reliable dose volume histogram (DVH) prediction method for external beam radiation therapy plans with multiple planning target volumes (PTVs). MATERIALS AND METHODS: We present a novel DVH prediction workflow, including new features and a modeling methodology, that makes better use of multiple PTVs: (a) We propose a generalized feature to characterize the geometric relationship of organ-at-risk (OARs) with respect to two or more PTVs with different prescribed dose levels; (b) We incorporate a novel data augmentation method to improve the data distribution in the feature space; (c) A similarity metric that leverages such information is subsequently used to select a subset of similar cases from the training dataset for model building; (d) Finally, a DVH prediction model is trained with these selected cases. To evaluate this new modeling workflow, we used 120 head and neck (HN) cases to tune the model, and used a separate dataset consisting of 148 cases for validation. The proposed model has been compared with the conventional knowledge-based model in terms of model prediction accuracy, which was measured by the root mean squared error (RMSE) between the predicted DVHs and the actual clinical plan DVHs. Furthermore, 25 randomly selected plans were replanned guided by the proposed model and evaluated against clinical plans using clinical evaluation criteria. RESULTS: The proposed modeling workflow significantly improved DVH prediction accuracy for brainstem (P < 0.001), cord (P < 0.001), larynx (P = 0.004), mandible (P < 0.001), oral cavity (P = 0.011), parotid (P < 0.001) and pharynx (P = 0.001). Cases replanned with the guidance of the proposed model spared OARs significantly better by clinical evaluation criteria. The replanned cases showed a 15% increase in the number of satisfied criteria, compared with clinical plans. CONCLUSIONS: The proposed modeling workflow generates DVH predictions with improved accuracy and robustness when multiple PTVs exist in a plan. It has demonstrated that the improvement in the DVH prediction model translates into better plan quality in knowledge-based planning.
Authors
Zhang, J; Ge, Y; Sheng, Y; Yin, F-F; Wu, QJ
MLA Citation
Zhang, Jiahan, et al. “Modeling of multiple planning target volumes for head and neck treatments in knowledge-based treatment planning..” Med Phys, vol. 46, no. 9, Sept. 2019, pp. 3812–22. Pubmed, doi:10.1002/mp.13679.
URI
https://scholars.duke.edu/individual/pub1395502
PMID
31236943
Source
pubmed
Published In
Med Phys
Volume
46
Published Date
Start Page
3812
End Page
3822
DOI
10.1002/mp.13679

Three IMRT advanced planning tools: A multi-institutional side-by-side comparison.

PURPOSE: To assess three advanced radiation therapy treatment planning tools on the intensity-modulated radiation therapy (IMRT) quality and consistency when compared to the clinically approved plans, referred as manual plans, which were planned without using any of these advanced planning tools. MATERIALS AND METHODS: Three advanced radiation therapy treatment planning tools, including auto-planning, knowledge-based planning, and multiple criteria optimization, were assessed on 20 previously treated clinical cases. Three institutions participated in this study, each with expertise in one of these tools. The twenty cases were retrospectively selected from Cleveland Clinic, including five head-and-neck (HN) cases, five brain cases, five prostate with pelvic lymph nodes cases, and five spine cases. A set of general planning objectives and organs-at-risk (OAR) dose constraints for each disease site from Cleveland Clinic was shared with other two institutions. A total of 60 IMRT research plans (20 from each institution) were designed with the same beam configuration as in the respective manual plans. For each disease site, detailed isodoseline distributions and dose volume histograms for a randomly selected representative case were compared among the three research plans and manual plan. In addition, dosimetric endpoints of five cases for each site were compared. RESULTS: Compared to the manual plans, the research plans using advanced tools showed substantial improvement for the HN patient cases, including the maximum dose to the spinal cord and brainstem and mean dose to the parotid glands. For the brain, prostate, and spine cases, the four types of plans were comparable based on dosimetric endpoint comparisons. CONCLUSION: With minimal planner interventions, advanced treatment planning tools are clinically useful, producing a plan quality similarly to or better than manual plans, improving plan consistency. For difficult cases such as HN cancer, advanced planning tools can further reduce radiation doses to numerous OARs while delivering adequate dose to the tumor targets.
Authors
Lu, L; Sheng, Y; Donaghue, J; Liu Shen, Z; Kolar, M; Wu, QJ; Xia, P
MLA Citation
Lu, Lan, et al. “Three IMRT advanced planning tools: A multi-institutional side-by-side comparison..” J Appl Clin Med Phys, vol. 20, no. 8, Aug. 2019, pp. 65–77. Pubmed, doi:10.1002/acm2.12679.
URI
https://scholars.duke.edu/individual/pub1404635
PMID
31364798
Source
pubmed
Published In
Journal of Applied Clinical Medical Physics
Volume
20
Published Date
Start Page
65
End Page
77
DOI
10.1002/acm2.12679

An Efficient Tool for Structure Label Harmonization

Authors
Xie, T; Ge, Y; Kirkpatrick, J; Yoo, S; Yin, F; Mayo, C; Wu, Q
MLA Citation
Xie, T., et al. “An Efficient Tool for Structure Label Harmonization.” Medical Physics, vol. 44, no. 6, WILEY, 2017, pp. 3013–3013.
URI
https://scholars.duke.edu/individual/pub1308467
Source
wos
Published In
Medical Physics
Volume
44
Published Date
Start Page
3013
End Page
3013

TH-EF-BRD-07: Knowledge Based Automatic Lung IMRT Planning with Non-Coplanar Beams

Authors
Yuan, L; Ge, Y; Sheng, Y; Hedrick, K; Yin, F; Wu, QJ
MLA Citation
Yuan, L., et al. “TH-EF-BRD-07: Knowledge Based Automatic Lung IMRT Planning with Non-Coplanar Beams.” Medical Physics, vol. 42, no. 6Part44, Wiley, 2015, pp. 3741–3741. Crossref, doi:10.1118/1.4926294.
URI
https://scholars.duke.edu/individual/pub1081801
Source
crossref
Published In
Medical Physics
Volume
42
Published Date
Start Page
3741
End Page
3741
DOI
10.1118/1.4926294

Artificial Intelligence-Driven Pancreas Stereotactic Body Radiation Therapy (SBRT) Treatment Planning

Authors
Zhang, J; Ge, Y; Wang, C; Sheng, Y; Palta, M; Czito, B; Willet, C; Yin, F; Wu, Q
MLA Citation
Zhang, J., et al. “Artificial Intelligence-Driven Pancreas Stereotactic Body Radiation Therapy (SBRT) Treatment Planning.” Medical Physics, vol. 46, no. 6, WILEY, 2019, pp. E370–E370.
URI
https://scholars.duke.edu/individual/pub1395496
Source
wos
Published In
Medical Physics
Volume
46
Published Date
Start Page
E370
End Page
E370

Research Areas:

Bioinformatics
Body Burden
Calibration
Clinical Trial
Cone-Beam Computed Tomography
Decision Support Systems, Clinical
Dose Fractionation
Dose-Response Relationship, Radiation
Imaging, Three-Dimensional
Mammography
Online Systems
Quality Assurance, Health Care
Radiographic Image Enhancement
Radiosurgery
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Image-Guided
Radiotherapy, Intensity-Modulated
Stereotaxic Techniques