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:

Deep Learning–Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost

Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a challenging task, especially with simultaneous integrated boost treatment approaches. We propose a deep learning (DL) framework to accurately predict fluence maps from patient anatomy and directly generate intensity modulated radiation therapy plans. Methods and Materials: The framework employs 2 convolutional neural networks (CNNs) to sequentially generate beam dose prediction and fluence map prediction, creating a deliverable 9-beam intensity modulated radiation therapy plan. Within the beam dose prediction CNN, axial slices of combined structure contour masks are used to predict 3-dimensional (3D) beam doses for each beam. Each 3D beam dose is projected along its beam's-eye-view to form a 2D beam dose map, which is subsequently used by the fluence map prediction CNN to predict its fluence map. Finally, the 9 predicted fluence maps are imported into the treatment planning system to finalize the plan by leaf sequencing and dose calculation. One hundred patients receiving pancreas SBRT were retrospectively collected for this study. Benchmark plans with unified simultaneous integrated boost prescription (25/33 Gy) were manually optimized for each case. The data set was split into 80/20 cases for training and testing. We evaluated the proposed DL framework by assessing both the fluence maps and the final predicted plans. Further, clinical acceptability of the plans was evaluated by a physician specializing in gastrointestinal cancer. Results: The DL-based planning was, on average, completed in under 2 minutes. In testing, the predicted plans achieved similar dose distribution compared with the benchmark plans (-1.5% deviation for planning target volume 33 V ), with slightly higher planning target volume maximum (+1.03 Gy) and organ at risk maximum (+0.95 Gy) doses. After renormalization, the physician rated 19 cases clinically acceptable and 1 case requiring minor improvement. Conclusions: The DL framework can effectively plan pancreas SBRT cases within 2 minutes. The predicted plans are clinically deliverable, with plan quality approaching that of manual planning. 33Gy
Authors
Wang, W; Sheng, Y; Palta, M; Czito, B; Willett, C; Hito, M; Yin, FF; Wu, Q; Ge, Y; Wu, QJ
MLA Citation
Wang, W., et al. “Deep Learning–Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost (Accepted).” Advances in Radiation Oncology, vol. 6, no. 4, July 2021. Scopus, doi:10.1016/j.adro.2021.100672.
URI
https://scholars.duke.edu/individual/pub1481730
Source
scopus
Published In
Advances in Radiation Oncology
Volume
6
Published Date
DOI
10.1016/j.adro.2021.100672

Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients

Authors
Wang, C; Li, X; Chang, Y; Sheng, Y; Zhang, J; Yin, FF; Wu, QJJ
MLA Citation
Wang, C., et al. “Rapid Auto IMRT Planning Using Cascade Dense Convolutional Neural Network (CDCNN): A Feasibility Study for Fluence Map Prediction Using Deep Learning on Prostate IMRT Patients.” International Journal of Radiation Oncology*Biology*Physics, vol. 105, no. 1, Elsevier BV, 2019, pp. E789–90. Crossref, doi:10.1016/j.ijrobp.2019.06.760.
URI
https://scholars.duke.edu/individual/pub1415074
Source
crossref
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
105
Published Date
Start Page
E789
End Page
E790
DOI
10.1016/j.ijrobp.2019.06.760

Innovation in Radiation Therapy Planning I: Knowledge Guided Treatment Planning

Authors
MLA Citation
Wu, Q., and L. Olsen. “Innovation in Radiation Therapy Planning I: Knowledge Guided Treatment Planning.” Medical Physics, vol. 41, no. 6, WILEY, 2014, pp. 486–486.
URI
https://scholars.duke.edu/individual/pub1339475
Source
wos
Published In
Medical Physics
Volume
41
Published Date
Start Page
486
End Page
486

An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.

PURPOSE: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. METHODS AND MATERIALS: The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set. RESULTS: All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process. CONCLUSIONS: We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies.
Authors
MLA Citation
Zhang, Jiahan, et al. “An Interpretable Planning Bot for Pancreas Stereotactic Body Radiation Therapy.Int J Radiat Oncol Biol Phys, vol. 109, no. 4, Mar. 2021, pp. 1076–85. Pubmed, doi:10.1016/j.ijrobp.2020.10.019.
URI
https://scholars.duke.edu/individual/pub1464387
PMID
33115686
Source
pubmed
Published In
Int J Radiat Oncol Biol Phys
Volume
109
Published Date
Start Page
1076
End Page
1085
DOI
10.1016/j.ijrobp.2020.10.019

Comparison of Photon and Proton Liver SBRT Plan Quality Affected by Daily Positioning and Anatomic Deviations

Authors
Lin, Y; Fang, M; Zhu, M; Wu, Q; Yin, FF
MLA Citation
Lin, Y., et al. “Comparison of Photon and Proton Liver SBRT Plan Quality Affected by Daily Positioning and Anatomic Deviations.” International Journal of Radiation Oncology*Biology*Physics, vol. 99, no. 2, Elsevier BV, 2017, pp. E690–E690. Crossref, doi:10.1016/j.ijrobp.2017.06.2265.
URI
https://scholars.duke.edu/individual/pub1284934
Source
crossref
Published In
International Journal of Radiation Oncology, Biology, Physics
Volume
99
Published Date
Start Page
E690
End Page
E690
DOI
10.1016/j.ijrobp.2017.06.2265

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