Christopher Willett

Positions:

Chair, Department of Radiation Oncology

Radiation Oncology
School of Medicine

Professor of Radiation Oncology

Radiation Oncology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

B.S. 1977

Tufts University

M.D. 1981

Tufts University

Grants:

Clinical Oncology Research Career Development Program

Awarded By
National Institutes of Health
Role
Mentor
Start Date
End Date

Cancer Care Quality Measures: Diagnosis and Treatment of Colorectal Cancer

Administered By
Institutes and Centers
Awarded By
Agency for Healthcare Research and Quality
Role
Investigator
Start Date
End Date

Angiogenic Profile of Rectal Cancer

Administered By
Radiation Oncology
Awarded By
National Cancer Institute
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

Colon Cancer, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology.

This selection from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Colon Cancer focuses on systemic therapy options for the treatment of metastatic colorectal cancer (mCRC), because important updates have recently been made to this section. These updates include recommendations for first-line use of checkpoint inhibitors for mCRC, that is deficient mismatch repair/microsatellite instability-high, recommendations related to the use of biosimilars, and expanded recommendations for biomarker testing. The systemic therapy recommendations now include targeted therapy options for patients with mCRC that is HER2-amplified, or BRAF V600E mutation-positive. Treatment and management of nonmetastatic or resectable/ablatable metastatic disease are discussed in the complete version of the NCCN Guidelines for Colon Cancer available at NCCN.org. Additional topics covered in the complete version include risk assessment, staging, pathology, posttreatment surveillance, and survivorship.
Authors
Benson, AB; Venook, AP; Al-Hawary, MM; Arain, MA; Chen, Y-J; Ciombor, KK; Cohen, S; Cooper, HS; Deming, D; Farkas, L; Garrido-Laguna, I; Grem, JL; Gunn, A; Hecht, JR; Hoffe, S; Hubbard, J; Hunt, S; Johung, KL; Kirilcuk, N; Krishnamurthi, S; Messersmith, WA; Meyerhardt, J; Miller, ED; Mulcahy, MF; Nurkin, S; Overman, MJ; Parikh, A; Patel, H; Pedersen, K; Saltz, L; Schneider, C; Shibata, D; Skibber, JM; Sofocleous, CT; Stoffel, EM; Stotsky-Himelfarb, E; Willett, CG; Gregory, KM; Gurski, LA
MLA Citation
Benson, Al B., et al. “Colon Cancer, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology.J Natl Compr Canc Netw, vol. 19, no. 3, Mar. 2021, pp. 329–59. Pubmed, doi:10.6004/jnccn.2021.0012.
URI
https://scholars.duke.edu/individual/pub1476779
PMID
33724754
Source
pubmed
Published In
J Natl Compr Canc Netw
Volume
19
Published Date
Start Page
329
End Page
359
DOI
10.6004/jnccn.2021.0012

Treatment of Locally Advanced Esophageal Carcinoma: ASCO Guideline.

PURPOSE: To develop an evidence-based clinical practice guideline to assist in clinical decision making for patients with locally advanced esophageal cancer. METHODS: ASCO convened an Expert Panel to conduct a systematic review of the more recently published literature (1999-2019) on therapy options for patients with locally advanced esophageal cancer and provide recommended care options for this patient population. RESULTS: Seventeen randomized controlled trials met the inclusion criteria. Where possible, data were extracted separately for squamous cell carcinoma and adenocarcinoma. RECOMMENDATIONS: Multimodality therapy for patients with locally advanced esophageal carcinoma is recommended. For the subgroup of patients with adenocarcinoma, preoperative chemoradiotherapy or perioperative chemotherapy should be offered. For the subgroup of patients with squamous cell carcinoma, preoperative chemoradiotherapy or chemoradiotherapy without surgery should be offered. Additional subgroup considerations are provided to assist with implementation of these recommendations. Additional information is available at www.asco.org/gastrointestinal-cancer-guidelines.
Authors
Shah, MA; Kennedy, EB; Catenacci, DV; Deighton, DC; Goodman, KA; Malhotra, NK; Willett, C; Stiles, B; Sharma, P; Tang, L; Wijnhoven, BPL; Hofstetter, WL
MLA Citation
Shah, Manish A., et al. “Treatment of Locally Advanced Esophageal Carcinoma: ASCO Guideline.J Clin Oncol, vol. 38, no. 23, Aug. 2020, pp. 2677–94. Pubmed, doi:10.1200/JCO.20.00866.
URI
https://scholars.duke.edu/individual/pub1448230
PMID
32568633
Source
pubmed
Published In
Journal of Clinical Oncology
Volume
38
Published Date
Start Page
2677
End Page
2694
DOI
10.1200/JCO.20.00866

Trimodal therapy approaches for localized rectal cancer

Excellent long-term outcomes and manageable toxicity are being achieved with contemporary treatment strategies for rectal cancer. Short-course radiotherapy is now an acceptable standard. Total neoadjuvant therapy (TNT), which incorporates induction or consolidation chemotherapy, has improved the delivery of treatment regiments. TNT is now a standard of care, although the sequencing of radiation and chemotherapy in TNT, appropriate amount of chemotherapy in TNT, and addition of irinotecan to the regimen are still being debated. Nonoperative management of rectal cancer appears to be a safe option for select patients, but it is not yet an NCCN recommendation. In addition, the omission of radiation is being evaluated as a treatment option in some cases.
Authors
MLA Citation
Willett, C. G. “Trimodal therapy approaches for localized rectal cancer.” Jnccn Journal of the National Comprehensive Cancer Network, vol. 18, no. 7.5, 2020, pp. 954–57. Scopus, doi:10.6004/JNCCN.2020.5015.
URI
https://scholars.duke.edu/individual/pub1461601
Source
scopus
Published In
Jnccn Journal of the National Comprehensive Cancer Network
Volume
18
Published Date
Start Page
954
End Page
957
DOI
10.6004/JNCCN.2020.5015

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