Deep Learning-Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost

Wentao Wang; Yang Sheng; Manisha Palta; Brian Czito; Christopher Willett; Martin Hito; Fang-Fang Yin; Qiuwen Wu; Yaorong Ge; Q Jackie Wu

doi:10.1016/j.adro.2021.100672

Deep Learning-Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost

Adv Radiat Oncol. 2021 Feb 16;6(4):100672. doi: 10.1016/j.adro.2021.100672. eCollection 2021 Jul-Aug.

Authors

Wentao Wang^{1

2}, Yang Sheng¹, Manisha Palta¹, Brian Czito¹, Christopher Willett¹, Martin Hito^{1

3}, Fang-Fang Yin^{1

2}, Qiuwen Wu^{1

2}, Yaorong Ge⁴, Q Jackie Wu^{1

2}

Affiliations

¹ Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina.
² Medical Physics Graduate Program, Duke University, Durham, North Carolina.
³ Department of Computer Science, Princeton University, New Jersey.
⁴ Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, North Carolina.

Abstract

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_33Gy), 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.