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:

Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.

PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning. METHODS AND MATERIALS: We propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose-volume parameters before planning. For a given case, a case reference set (CRS) is selected using a novel anatomic similarity metric from a large reference plan pool. Then, a regression model is built on the CRS to estimate the expected dose-volume histograms (DVHs) for the current case. This model also predicts the DVHs for all CRS cases and captures the variation from the corresponding DVHs in the clinical plans. Finally, these DVH variations are analyzed using the principal component analysis to determine the tradeoff hyperplane for the current case. To evaluate the effectiveness of the proposed approach, 244 head and neck cases were randomly partitioned into reference (214) and validation (30) sets. A tradeoff hyperplane was built for each validation case and evenly sampled for 12 tradeoff predictions. Each prediction yielded a tradeoff plan. The root-mean-square errors of the predicted and the realized plan DVHs were computed for prediction achievability evaluation. RESULTS: The tradeoff hyperplane with 3 principal directions accounts for 57.8% ± 3.6% of variations in the validation cases, suggesting the hyperplanes capture a significant portion of the clinical tradeoff space. The average root-mean-square errors in 3 tradeoff directions are 5.23 ± 2.46, 5.20 ± 2.52, and 5.19 ± 2.49, compared with 4.96 ± 2.48 of the knowledge-based planning predictions, indicating that the tradeoff predictions are comparably achievable. CONCLUSIONS: Clinically relevant tradeoffs can be effectively extracted from existing plans and characterized by a tradeoff hyperplane model. The hyperplane allows physicians and planners to explore the best clinically achievable plans with different organ-at-risk sparing goals before inverse planning and is a natural extension of the current knowledge-based planning framework.
Authors
MLA Citation
Zhang, Jiahan, et al. “Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.Int J Radiat Oncol Biol Phys, vol. 106, no. 5, Apr. 2020, pp. 1095–103. Pubmed, doi:10.1016/j.ijrobp.2019.12.034.
URI
https://scholars.duke.edu/individual/pub1428932
PMID
31982497
Source
pubmed
Published In
Int J Radiat Oncol Biol Phys
Volume
106
Published Date
Start Page
1095
End Page
1103
DOI
10.1016/j.ijrobp.2019.12.034

Dose Prediction for Prostate Radiation Treatment: Feasibility of a Distance-Based Deep Learning Model

© 2019 IEEE. This study aims to demonstrate the feasibility of using a novel distance-based representation of 3D CT-scan images to train a deep learning model for dose predictions in radiation treatment planning. The distance representation is inspired by previous knowledge of the domain to increase the generalizability of the deep learning models for radiation treatment planning. Conventional knowledge-based planning methods extract engineered features from 3D CT-scan images, as well as other patients' features, to predict the best achievable dose in a cancerous area and other organs at risk. Recent studies have shown higher accuracy in voxel-level dose prediction using deep learning models compared to the conventional machine learning approaches. Since the data resources for training these models are limited, most of the studies use 2D contour information to represent the patient anatomy. This representation loses volumetric information, and it is sensitive to small changes in patient orientation and translation. The distance-based representation introduced in this paper is inspired by the domain knowledge and is able to maintain the volumetric distance information despite the 2D slicing of 3D CT-image. According to prior studies in the radiation treatment planning domain, there is a strong association between the organs-at-risk distance from the cancerous volume and the patient's vulnerability to receive excessive dose. Therefore, the contour value in prior representation was replaced by voxel distance from cancerous volume. This modification in representation makes it transition and orientation invariant and adds potential robustness to patient positioning differences during the imaging/planning process. We evaluated the distance-based deep learning models through experiments for prediction of prostate cancer patients' vulnerability and voxel-level dose distribution using convolutional neural network and U-net models, respectively. The results were compared with contour-based U-net model as well as conventional machine learning with engineered representations. We found that the performance was comparable or higher than the prior state-of-the-art results for prostate-cancer dose distribution prediction.
Authors
Maryam, TH; Ru, B; Xie, T; Hadzikadic, M; Wu, QJ; Ge, Y
MLA Citation
Maryam, T. H., et al. “Dose Prediction for Prostate Radiation Treatment: Feasibility of a Distance-Based Deep Learning Model.” Proceedings  2019 Ieee International Conference on Bioinformatics and Biomedicine, Bibm 2019, 2019, pp. 2379–86. Scopus, doi:10.1109/BIBM47256.2019.8983412.
URI
https://scholars.duke.edu/individual/pub1441986
Source
scopus
Published In
Proceedings 2019 Ieee International Conference on Bioinformatics and Biomedicine, Bibm 2019
Published Date
Start Page
2379
End Page
2386
DOI
10.1109/BIBM47256.2019.8983412

A Fluence Scaled Tensor Kernel Dose Calculation Algorithm for IMRT and VMAT Optimization

Authors
Stephens, H; Wu, Q
MLA Citation
Stephens, H., and Q. Wu. “A Fluence Scaled Tensor Kernel Dose Calculation Algorithm for IMRT and VMAT Optimization.” Medical Physics, vol. 46, no. 6, WILEY, 2019, pp. E179–80.
URI
https://scholars.duke.edu/individual/pub1396539
Source
wos
Published In
Medical Physics
Volume
46
Published Date
Start Page
E179
End Page
E180

Ensemble Learning: A Case Study with Knowledge Based Treatment Planning

Authors
Zhang, J; Xie, T; Sheng, Y; Wu, Q; Yin, F; Ge, Y
MLA Citation
Zhang, J., et al. “Ensemble Learning: A Case Study with Knowledge Based Treatment Planning.” Medical Physics, vol. 45, no. 6, WILEY, 2018, pp. E626–E626.
URI
https://scholars.duke.edu/individual/pub1333267
Source
wos
Published In
Medical Physics
Volume
45
Published Date
Start Page
E626
End Page
E626

Using Knowledge-Based Models to Train Human Planners with Lung and Mediastinum IMRT Planning

Authors
Mistro, M; Sheng, Y; Ge, Y; Palta, J; Salama, J; Kelsey, C; Wu, Q; Yin, F
MLA Citation
Mistro, M., et al. “Using Knowledge-Based Models to Train Human Planners with Lung and Mediastinum IMRT Planning.” Medical Physics, vol. 46, no. 6, WILEY, 2019, pp. E473–E473.
URI
https://scholars.duke.edu/individual/pub1396543
Source
wos
Published In
Medical Physics
Volume
46
Published Date
Start Page
E473
End Page
E473

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