In:
Medical Physics, Wiley
Abstract:
Leveraging the precision of its radiation dose distribution and the minimization of postoperative complications, low‐dose‐rate (LDR) permanent seed brachytherapy is progressively adopted in addressing hepatic malignancies. Purpose The present study endeavors to devise a sophisticated treatment planning system (TPS) to optimize LDR brachytherapy for hepatic lesions. Methods Our TPS encompasses four integral modules: multi‐organ segmentation, seed distribution initialization, puncture pathway selection, and inverse dose planning. By amalgamating an array of deep learning models, the segmentation module proficiently labels 17 discrete abdominal targets within the images. We introduce a knowledge‐based seed distribution initialization methodology that discerns the most analogous tumor shape in the reference treatment plan from the knowledge base. Subsequently, the seed distribution from the reference plan is transmuted to the current case, thus establishing seed distribution initialization. Furthermore, we parameterize the puncture needles and seeds, while concurrently constraining the puncture needle angle through the employment of a virtual puncture panel to augment planning algorithm efficiency. We also presented a user interface that includes a range of interactive features, seamlessly integrated with the treatment planning generation function. Results The multi‐organ segmentation module, which is trained by 50 cases of in‐house CT scans and 694 cases of publicly available CT scans, achieved average Dice of 0.80 and Hausdorff distance of 5.2 mm in testing datasets. The results demonstrate that knowledge‐based initialization exhibits a marked enhancement in expediting the convergence rate. Our TPS also demonstrates a dominant advantage in dose‐volume‐histogram criteria and execution time in comparison to commercial TPS. Conclusion The study proposes an innovative treatment planning system for low‐dose‐rate permanent seed brachytherapy for hepatic malignancies. We show that the generated treatment plans meet clinical requirement.
Type of Medium:
Online Resource
ISSN:
0094-2405
,
2473-4209
Language:
English
Publisher:
Wiley
Publication Date:
2023
detail.hit.zdb_id:
1466421-5
