Browsing by Author "Willett, Christopher G"

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    A current perspective on stereotactic body radiation therapy for pancreatic cancer.
    (Onco Targets Ther, 2016) Hong, Julian C; Czito, Brian G; Willett, Christopher G; Palta, Manisha
    Pancreatic cancer is a formidable malignancy with poor outcomes. The majority of patients are unable to undergo resection, which remains the only potentially curative treatment option. The management of locally advanced (unresectable) pancreatic cancer is controversial; however, treatment with either chemotherapy or chemoradiation is associated with high rates of local tumor progression and metastases development, resulting in low survival rates. An emerging local modality is stereotactic body radiation therapy (SBRT), which uses image-guided, conformal, high-dose radiation. SBRT has demonstrated promising local control rates and resultant quality of life with acceptable rates of toxicity. Over the past decade, increasing clinical experience and data have supported SBRT as a local treatment modality. Nevertheless, additional research is required to further evaluate the role of SBRT and improve upon the persistently poor outcomes associated with pancreatic cancer. This review discusses the existing clinical experience and technical implementation of SBRT for pancreatic cancer and highlights the directions for ongoing and future studies.
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    Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.
    (Frontiers in artificial intelligence, 2020-01) Wang, Wentao; Sheng, Yang; Wang, Chunhao; Zhang, Jiahan; Li, Xinyi; Palta, Manisha; Czito, Brian; Willett, Christopher G; Wu, Qiuwen; Ge, Yaorong; Yin, Fang-Fang; Wu, Q Jackie
    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.
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    Fostering Radiation Oncology Physician Scientist Trainees Within a Diverse Workforce: The Radiation Oncology Research Scholar Track.
    (International journal of radiation oncology, biology, physics, 2021-06) Salama, Joseph K; Floyd, Scott R; Willett, Christopher G; Kirsch, David G
    There is a need to foster future generations of radiation oncology physician scientists, but the number of radiation oncologists with sufficient education, training, and funding to make transformative discoveries is relatively small. A large number of MD/PhD graduates have entered he field of radiation oncology over the past 2 decades, but this has not led to a significant cohort of externally funded physician scientists. Because radiation oncologists leading independent research labs have the potential to make transformative discoveries that advance our field and positively affect patients with cancer, we created the Duke Radiation Oncology Research Scholar (RORS) Program. In crafting this program, we sought to eliminate barriers preventing radiation oncology trainees from becoming independent physician scientists. The RORS program integrates the existing American Board of Radiology Holman Pathway with a 2-year post-graduate medical education instructor position with 80% research effort at the same institution. We use a separate match for RORS and traditional residency pathways, which we hope will increase the diversity of our residency program. Since the inception of the RORS program, we have matched 2 trainees into our program. We encourage other radiation oncology residency programs at peer institutions to consider this training pathway as a means to foster the development of independent physician scientists and a diverse workforce in radiation oncology.