Goulet Mathieu, Duguay-Drouin Patricia, Mascolo-Fortin Julia, Mégrourèche Julien, Octave Nadia, Tsui James Man Git
Département de radio-oncologie, CISSS de Chaudière-Appalaches, Lévis, Québec, Canada.
Département de radio-oncologie, CISSS de Chaudière-Appalaches, Lévis, Québec, Canada.
Int J Radiat Oncol Biol Phys. 2025 May 1;122(1):199-207. doi: 10.1016/j.ijrobp.2024.12.026. Epub 2025 Jan 11.
High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of ultrasound (US)-guided prostate BT planning.
This study included a total of 102 US-planned BT images from a single institution and split into 3 groups: 50 for model training and validation, 11 to evaluate reconstruction accuracy (test set), and 41 to evaluate the AI tool in a clinical implementation (clinical set). Reconstruction accuracy for the test set was evaluated by comparing the performance of AI-derived and manually reconstructed needles from 5 medical physicists on the 3D-US scans after treatment. The needle total reconstruction time for the clinical set was defined as the timestamp difference from scan acquisition to the start of dose calculations and was compared with values recorded before the clinical implementation of the AI-assisted tool.
A mean error of (0.44 ± 0.32) mm was found between the AI-reconstructed and the human consensus needle positions in the test set, with 95.0% of AI needle points falling below 1 mm from their human-made counterparts. Post-hoc analysis showed that only one of the human observers' reconstructions were significantly different from the others including the AIs. In the clinical set, the AI algorithm achieved a true positive reconstruction rate of 93.4% with only 4.5% of these needles requiring manual corrections from the planner before dosimetry. Total time required to perform AI-assisted catheter reconstruction on clinical cases was on average 15.2 min lower (P < .01) compared with procedure without AI assistance.
This study demonstrates the feasibility of an AI-assisted needle reconstructing tool for 3D-US-based HDR prostate BT. This is a step toward treatment planning automation and increased efficiency in HDR prostate BT.
高剂量率（HDR）前列腺近距离放射治疗（BT）程序需要图像引导下的针插入。鉴于该程序通常采用全身麻醉，尽量缩短总体计划时间至关重要。在本研究中，我们探讨了在超声（US）引导的前列腺BT计划中，人工智能（AI）驱动的经会阴针自动重建的临床可行性和节省时间的潜力。
本研究共纳入来自单一机构的102张US计划的BT图像，并分为3组：50张用于模型训练和验证，11张用于评估重建准确性（测试集），41张用于评估AI工具在临床应用中的效果（临床集）。通过比较5名医学物理学家在治疗后对3D-US扫描中AI衍生针和手动重建针的性能，评估测试集的重建准确性。临床集的针总重建时间定义为从扫描采集到剂量计算开始的时间戳差异，并与AI辅助工具临床应用前记录的值进行比较。
在测试集中，AI重建针与人类共识针位置之间的平均误差为（0.44±0.32）mm，95.0%的AI针点与其人工对应点的距离在1mm以内。事后分析表明，只有一名人类观察者的重建与包括AI在内的其他重建有显著差异。在临床集中,AI算法实现了93.4%的真阳性重建率，其中只有4.5%的针在剂量测定前需要计划者进行手动校正。与无AI辅助的程序相比，在临床病例上执行AI辅助导管重建所需的总时间平均减少了15.2分钟（P<.01）。
本研究证明了AI辅助针重建工具用于基于3D-US的HDR前列腺BT的可行性。这是朝着治疗计划自动化和提高HDR前列腺BT效率迈出的一步。
