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Published on: June 7, 2015
Intrafractional rectum anatomy shape prediction based on 3D point cloud representation in online adaptive radiation
Wenyu Wang1, Zihong Zhou2, Ran Wei1
1Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Purpose:
This study aimed to develop an anatomical structure generative model to predict intrafractional rectal shape in prostate cancer online adaptive radiation therapy (OART).
Methods:
A retrospective analysis was conducted on clinical data from 42 prostate cancer patients treated with online adaptive radiotherapy (OART). Rectal shapes were extracted from MRI scans acquired at the pretreatment (Pre-) and position verification (Pv-) stages, and represented as 3D point clouds. Data augmentation was applied to construct the rectal dataset. Then, we developed SA-UNet, among the earliest generative AI-based models for intrafractional anatomical shape prediction, and benchmarked its performance against two conventional deep learning baseline models.
Results:
The SA-UNet model demonstrated superior performance in anatomical structure prediction, yielding the lowest average CD (29.06 ± 12.56 mm) and EMD (4.82 ± 1.31 mm) values and the highest average JAC value (0.69 ± 0.07). Compared with Baseline-MLP, SA-UNet achieved significantly greater consistency across treatment fractions, with reduced variability and fewer outliers (p < 0.025, Bonferroni-adjusted). Meanwhile, the SA-UNet significantly outperformed Baseline-PointCNN, which had the highest average CD (42.85 ± 14.18 mm) and EMD (6.07 ± 1.31 mm), and the lowest JAC (0.62 ± 0.07), all significantly inferior to those of SA-UNet (p < 0.01, Bonferroni-adjusted).
Conclusions:
The SA-UNet model showed preliminary feasibility for intrafractional rectal shape prediction in OART, offering potential for early alerting and advancing precision radiotherapy.
