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:

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:

Treatment of locally advanced/metastatic colorectal cancer

Few treatment advances have been observed in recent years for the treatment of advanced colorectal cancer (CRC). The goal remains to find approaches beyond FOLFOX and bevacizumab that will prolong remission. Immunotherapy for patients with microsatellite instability-high tumors represents progress, but this is a very small subset and approximately 30% of patients will not experience response. In locally advanced CRC, good long-term outcomes and manageable toxicity are being achieved with contemporary treatment strategies. Total neoadjuvant therapy, which incorporates induction or consolidation chemotherapy, has improved the treatment of patients with rectal cancer and is now a standard of care, although optimal sequencing is still being debated. Nonoperative management is an emerging option for sphincter preservation, and ongoing studies are evaluating the omission of radiation in select patients.
Authors
Venook, AP; Willett, CG
MLA Citation
Venook, A. P., and C. G. Willett. “Treatment of locally advanced/metastatic colorectal cancer.” Jnccn Journal of the National Comprehensive Cancer Network, vol. 19, 2021, pp. 617–21. Scopus, doi:10.6004/jnccn.2021.5014.
URI
https://scholars.duke.edu/individual/pub1488162
Source
scopus
Published In
Jnccn Journal of the National Comprehensive Cancer Network
Volume
19
Published Date
Start Page
617
End Page
621
DOI
10.6004/jnccn.2021.5014

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 V33Gy), 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.
Authors
Wang, W; Sheng, Y; Palta, M; Czito, B; Willett, C; Hito, M; Yin, F-F; Wu, Q; Ge, Y; Wu, QJ
MLA Citation
Wang, Wentao, et al. “Deep Learning-Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost.Adv Radiat Oncol, vol. 6, no. 4, July 2021, p. 100672. Pubmed, doi:10.1016/j.adro.2021.100672.
URI
https://scholars.duke.edu/individual/pub1481730
PMID
33997484
Source
pubmed
Published In
Advances in Radiation Oncology
Volume
6
Published Date
Start Page
100672
DOI
10.1016/j.adro.2021.100672

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

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans by direct prediction of fluence maps from patient anatomy using convolutional neural networks (CNNs). Materials and Methods: Our proposed framework utilizes two CNNs to predict intensity-modulated radiation therapy fluence maps and generate deliverable plans: (1) Field-dose CNN predicts field-dose distributions in the region of interest using planning images and structure contours; (2) a fluence map CNN predicts the final fluence map per beam using the predicted field dose projected onto the beam's eye view. The predicted fluence maps were subsequently imported into the treatment planning system for leaf sequencing and final dose calculation (model-predicted plans). One hundred patients previously treated with pancreas SBRT were included in this retrospective study, and they were split into 85 training cases and 15 test cases. For each network, 10% of training data were randomly selected for model validation. Nine-beam benchmark plans with standardized target prescription and organ-at-risk constraints were planned by experienced clinical physicists and used as the gold standard to train the model. Model-predicted plans were compared with benchmark plans in terms of dosimetric endpoints, fluence map deliverability, and total monitor units. Results: The average time for fluence-map prediction per patient was 7.1 s. Comparing model-predicted plans with benchmark plans, target mean dose, maximum dose (0.1 cc), and D95% absolute differences in percentages of prescription were 0.1, 3.9, and 2.1%, respectively; organ-at-risk mean dose and maximum dose (0.1 cc) absolute differences were 0.2 and 4.4%, respectively. The predicted plans had fluence map gamma indices (97.69 ± 0.96% vs. 98.14 ± 0.74%) and total monitor units (2,122 ± 281 vs. 2,265 ± 373) that were comparable to the benchmark plans. Conclusions: We develop a novel deep learning framework for pancreas SBRT planning, which predicts a fluence map for each beam and can, therefore, bypass the lengthy inverse optimization process. The proposed framework could potentially change the paradigm of treatment planning by harnessing the power of deep learning to generate clinically deliverable plans in seconds.
Authors
Wang, W; Sheng, Y; Wang, C; Zhang, J; Li, X; Palta, M; Czito, B; Willett, CG; Wu, Q; Ge, Y; Yin, F-F; Wu, QJ
MLA Citation
Wang, Wentao, et al. “Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.Front Artif Intell, vol. 3, 2020, p. 68. Pubmed, doi:10.3389/frai.2020.00068.
URI
https://scholars.duke.edu/individual/pub1476685
PMID
33733185
Source
pubmed
Published In
Front Artif Intell
Volume
3
Published Date
Start Page
68
DOI
10.3389/frai.2020.00068