Department of Radiation Oncology, Zhejiang Hospital, Hangzhou 310013, China.
Instiute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China.
Nano Lett. 2024 Aug 28;24(34):10614-10623. doi: 10.1021/acs.nanolett.4c02823. Epub 2024 Jul 24.
Because of the challenges posed by anatomical uncertainties and the low resolution of plain computed tomography (CT) scans, implementing adaptive radiotherapy (ART) for small hepatocellular carcinoma (sHCC) using artificial intelligence (AI) faces obstacles in tumor identification-alignment and automatic segmentation. The current study aims to improve sHCC imaging for ART using a gold nanoparticle (Au NP)-based CT contrast agent to enhance AI-driven automated image processing. The synthesized charged Au NPs demonstrated notable aggregation, low cytotoxicity, and minimal organ toxicity. Over time, an sHCC mouse model was established for CT imaging at multiple time points. The enhanced CT images processed using 3D U-Net and 3D Trans U-Net AI models demonstrated high geometric and dosimetric accuracy. Therefore, charged Au NPs enable accurate and automatic sHCC segmentation in CT images using classical AI models, potentially addressing the technical challenges related to tumor identification, alignment, and automatic segmentation in CT-guided online ART.
由于解剖不确定性和普通计算机断层扫描 (CT) 分辨率低带来的挑战，使用人工智能 (AI) 为小肝细胞癌 (sHCC) 实施自适应放疗 (ART) 在肿瘤识别-配准和自动分割方面面临障碍。本研究旨在使用基于金纳米颗粒 (AuNP) 的 CT 造影剂来改善用于 ART 的 sHCC 成像，以增强人工智能驱动的自动图像处理。合成的带电荷的 AuNP 表现出明显的聚集、低细胞毒性和最小的器官毒性。随着时间的推移，建立了 sHCC 小鼠模型，以便在多个时间点进行 CT 成像。使用 3D U-Net 和 3D Trans U-Net AI 模型处理的增强 CT 图像显示出很高的几何和剂量学精度。因此，带电荷的 AuNP 可以使用经典的 AI 模型在 CT 图像中实现准确和自动的 sHCC 分割，可能解决与 CT 引导在线 ART 中肿瘤识别、配准和自动分割相关的技术挑战。
