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:

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:

A Deep-Learning-Based Dual-Arc VMAT Plan Generation from Patient Anatomy for Prostate Simultaneous Integrated Boost (SIB) Cases

Authors
Zhu, Q; Li, X; Ni, Y; Wang, C; Wu, Q; Ge, Y; Yin, F
URI
https://scholars.duke.edu/individual/pub1495080
Source
wos-lite
Published In
Medical Physics
Volume
48
Published Date

Introducing Kernel Truncation and Sparsity into Dose Calculations for IMRT Optimization

Authors
Stephens, H; Wu, Q
MLA Citation
Stephens, H., and Q. Wu. “Introducing Kernel Truncation and Sparsity into Dose Calculations for IMRT Optimization.” Medical Physics, vol. 48, no. 6, 2021.
URI
https://scholars.duke.edu/individual/pub1495082
Source
wos-lite
Published In
Medical Physics
Volume
48
Published Date

Radiation Therapy for Rectal Cancer: Executive Summary of an ASTRO Clinical Practice Guideline.

PURPOSE: This guideline reviews the evidence and provides recommendations for the indications and appropriate technique and dose of neoadjuvant radiation therapy (RT) in the treatment of localized rectal cancer. METHODS: The American Society for Radiation Oncology convened a task force to address 4 key questions focused on the use of RT in preoperative management of operable rectal cancer. These questions included the indications for neoadjuvant RT, identification of appropriate neoadjuvant regimens, indications for consideration of a nonoperative or local excision approach after chemoradiation, and appropriate treatment volumes and techniques. Recommendations were based on a systematic literature review and created using a predefined consensus-building methodology and system for grading evidence quality and recommendation strength. RESULTS: Neoadjuvant RT is recommended for patients with stage II-III rectal cancer, with either conventional fractionation with concurrent 5-FU or capecitabine or short-course RT. RT should be performed preoperatively rather than postoperatively. Omission of preoperative RT is conditionally recommended in selected patients with lower risk of locoregional recurrence. Addition of chemotherapy before or after chemoradiation or after short-course RT is conditionally recommended. Nonoperative management is conditionally recommended if a clinical complete response is achieved after neoadjuvant treatment in selected patients. Inclusion of the rectum and mesorectal, presacral, internal iliac, and obturator nodes in the clinical treatment volume is recommended. In addition, inclusion of external iliac nodes is conditionally recommended in patients with tumors invading an anterior organ or structure, and inclusion of inguinal and external iliac nodes is conditionally recommended in patients with tumors involving the anal canal. CONCLUSIONS: Based on currently published data, the American Society for Radiation Oncology task force has proposed evidence-based recommendations regarding the use of RT for rectal cancer. Future studies will look to further personalize treatment recommendations to optimize treatment outcomes and quality of life.
Authors
Wo, JY; Anker, CJ; Ashman, JB; Bhadkamkar, NA; Bradfield, L; Chang, DT; Dorth, J; Garcia-Aguilar, J; Goff, D; Jacqmin, D; Kelly, P; Newman, NB; Olsen, J; Raldow, AC; Ruiz-Garcia, E; Stitzenberg, KB; Thomas, CR; Wu, QJ; Das, P
MLA Citation
Wo, Jennifer Y., et al. “Radiation Therapy for Rectal Cancer: Executive Summary of an ASTRO Clinical Practice Guideline.Pract Radiat Oncol, vol. 11, no. 1, Jan. 2021, pp. 13–25. Pubmed, doi:10.1016/j.prro.2020.08.004.
URI
https://scholars.duke.edu/individual/pub1463553
PMID
33097436
Source
pubmed
Published In
Pract Radiat Oncol
Volume
11
Published Date
Start Page
13
End Page
25
DOI
10.1016/j.prro.2020.08.004

A Novel Machine Learning Model for Dose Prediction in Prostate Volumetric Modulated Arc Therapy Using Output Initialization and Optimization Priorities.

Treatment planning for prostate volumetric modulated arc therapy (VMAT) can take 5-30 min per plan to optimize and calculate, limiting the number of plan options that can be explored before the final plan decision. Inspired by the speed and accuracy of modern machine learning models, such as residual networks, we hypothesized that it was possible to use a machine learning model to bypass the time-intensive dose optimization and dose calculation steps, arriving directly at an estimate of the resulting dose distribution for use in multi-criteria optimization (MCO). In this study, we present a novel machine learning model for predicting the dose distribution for a given patient with a given set of optimization priorities. Our model innovates upon the existing machine learning techniques by utilizing optimization priorities and our understanding of dose map shapes to initialize the dose distribution before dose refinement via a voxel-wise residual network. Each block of the residual network individually updates the initialized dose map before passing to the next block. Our model also utilizes contiguous and atrous patch sampling to effectively increase the receptive fields of each layer in the residual network, decreasing its number of layers, increasing model prediction and training speed, and discouraging overfitting without compromising on the accuracy. For analysis, 100 prostate VMAT cases were used to train and test the model. The model was evaluated by the training and testing errors produced by 50 iterations of 10-fold cross-validation, with 100 cases randomly shuffled into the subsets at each iteration. The error of the model is modest for this data, with average dose map root-mean-square errors (RMSEs) of 2.38 ± 0.47% of prescription dose overall patients and all optimization priority combinations in the patient testing sets. The model was also evaluated at iteratively smaller training set sizes, suggesting that the model requires between 60 and 90 patients for optimal performance. This model may be used for quickly estimating the Pareto set of feasible dose objectives, which may directly accelerate the treatment planning process and indirectly improve final plan quality by allowing more time for plan refinement.
Authors
Jensen, PJ; Zhang, J; Koontz, BF; Wu, QJ
MLA Citation
Jensen, P. James, et al. “A Novel Machine Learning Model for Dose Prediction in Prostate Volumetric Modulated Arc Therapy Using Output Initialization and Optimization Priorities.Front Artif Intell, vol. 4, 2021, p. 624038. Pubmed, doi:10.3389/frai.2021.624038.
URI
https://scholars.duke.edu/individual/pub1482081
PMID
33969289
Source
pubmed
Published In
Front Artif Intell
Volume
4
Published Date
Start Page
624038
DOI
10.3389/frai.2021.624038

A data-driven approach to optimal beam/arc angle selection for liver stereotactic body radiation therapy treatment planning

Authors
Sheng, Y; Li, T; Ge, Y; Lin, H; Wang, W; Yuan, L; Wu, QJ
MLA Citation
Sheng, Yang, et al. “A data-driven approach to optimal beam/arc angle selection for liver stereotactic body radiation therapy treatment planning (Accepted).” Quantitative Imaging in Medicine and Surgery, vol. 11, no. 12, AME Publishing Company, Dec. 2021, pp. 4797–806. Crossref, doi:10.21037/qims-21-169.
URI
https://scholars.duke.edu/individual/pub1493446
Source
crossref
Published In
Quantitative Imaging in Medicine and Surgery
Volume
11
Published Date
Start Page
4797
End Page
4806
DOI
10.21037/qims-21-169

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