Developing an AI-assisted planning pipeline for hippocampal avoidance whole brain radiotherapy

Chih-Yuan Lin; Lin-Shan Chou; Yuan-Hung Wu; John S Kuo; Minesh P Mehta; An-Cheng Shiau; Ji-An Liang; Shih-Ming Hsu; Ti-Hao Wang

doi:10.1016/j.radonc.2023.109528

Developing an AI-assisted planning pipeline for hippocampal avoidance whole brain radiotherapy

Radiother Oncol. 2023 Apr:181:109528. doi: 10.1016/j.radonc.2023.109528. Epub 2023 Feb 10.

Authors

Chih-Yuan Lin¹, Lin-Shan Chou², Yuan-Hung Wu³, John S Kuo⁴, Minesh P Mehta⁵, An-Cheng Shiau⁶, Ji-An Liang⁷, Shih-Ming Hsu⁸, Ti-Hao Wang⁹

Affiliations

¹ Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Medical Physics and Radiation Measurements Laboratory, National Yang Ming Chiao Tung University, Taipei, Taiwan.
² Division of Radiation Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan.
³ Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Medical Physics and Radiation Measurements Laboratory, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Radiation Oncology, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
⁴ Neuroscience and Brain Disease Center, China Medical University, Taichung, Taiwan; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Neurosurgery, Dell Medical School, The University of Texas at Austin, Austin, TX, USA.
⁵ Department of Radiation Oncology, Miami Cancer Institute, Baptist Health South Florida, Miami, Florida, USA; Florida International University, Miami, Florida, USA.
⁶ Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan; Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.
⁷ Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan; Department of Medicine, China Medical University, Taichung, Taiwan.
⁸ Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan; Medical Physics and Radiation Measurements Laboratory, National Yang Ming Chiao Tung University, Taipei, Taiwan. Electronic address: smhsu@nycu.edu.tw.
⁹ Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan. Electronic address: thothwang@gmail.com.

PMID: 36773828
DOI: 10.1016/j.radonc.2023.109528

Abstract

Background and purpose: Hippocampal avoidance whole brain radiotherapy (HA-WBRT) is effective for controlling disease and preserving neuro-cognitive function for brain metastases. However, contouring and planning of HA-WBRT is complex and time-consuming. We designed and evaluated a pipeline using deep learning tools for a fully automated treatment planning workflow to generate HA-WBRT radiotherapy plans.

Materials and methods: We retrospectively collected 50 adult patients who received HA-WBRT. Using RTOG- 0933 clinical trial protocol guidelines, all organs-at-risk (OARs) and the clinical target volume (CTV) were contoured by experienced radiation oncologists. A deep-learning segmentation model was designed and trained. Next, we developed a volumetric-modulated arc therapy (VMAT) auto-planning algorithm for 30 Gy in 10 fractions. Automated segmentations were evaluated using the Dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (95 % HD). Auto-plans were evaluated by the percentage of PTV volume that receives 30 Gy (V_30Gy), conformity index (CI), and homogeneity index (HI) of planning target volume (PTV) and the minimum dose (D_100%) and maximum dose (D_max) for the hippocampus, D_max for the lens, eyes, optic nerve, brain stem, and chiasm.

Results: We developed a deep-learning segmentation model and an auto-planning script. For the 10 cases in the independent test set, the overall average DSC and 95 % HD of contours were greater than 0.8 and less than 7 mm, respectively. All auto-plans met the RTOG- 0933 criteria. The HA-WBRT plan automatically created time was about 10 min.

Conclusions: An artificial intelligence (AI)-assisted pipeline using deep learning tools can rapidly and accurately generate clinically acceptable HA-WBRT plans with minimal manual intervention and increase efficiency of this treatment for brain metastases.

Keywords: Autoplanning; Deep learning; Hippocampal avoidance whole brain; Radiotherapy.

Publication types

Clinical Trial

MeSH terms

Adult
Artificial Intelligence
Brain Neoplasms* / radiotherapy
Brain Neoplasms* / secondary
Hippocampus
Humans
Organ Sparing Treatments
Organs at Risk
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Intensity-Modulated*
Retrospective Studies