Gu Liujie, Deng Handi, Bai Yizhou, Gao Jianpan, Wang Xuewei, Yue Tong, Luo Bin, Ma Cheng
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China.
Institute for Intelligent Healthcare, Tsinghua University, Beijing 100084, China.
Biomed Opt Express. 2023 Feb 3;14(3):1003-1014. doi: 10.1364/BOE.482126. eCollection 2023 Mar 1.
Assessing the metastatic status of axillary lymph nodes is a common clinical practice in the staging of early breast cancers. Yet sentinel lymph nodes (SLNs) are the regional lymph nodes believed to be the first stop along the lymphatic drainage path of the metastasizing cancer cells. Compared to axillary lymph node dissection, sentinel lymph node biopsy (SLNB) helps reduce morbidity and side effects. Current SLNB methods, however, still have suboptimum properties, such as restrictions due to nuclide accessibility and a relatively low therapeutic efficacy when only a single contrast agent is used. To overcome these limitations, researchers have been motivated to develop a non-radioactive SLN mapping method to replace or supplement radionuclide mapping. We proposed and demonstrated a clinical procedure using a dual-modality photoacoustic (PA)/ultrasound (US) imaging system to locate the SLNs to offer surgical guidance. In our work, the high contrast of PA imaging and its specificity to SLNs were based on the accumulation of carbon nanoparticles (CNPs) in the SLNs. A machine-learning model was also trained and validated to distinguish stained SLNs based on single-wavelength PA images. In the pilot study, we imaged 11 patients in vivo, and the specimens from 13 patients were studied ex vivo. PA/US imaging identified stained SLNs in vivo without a single false positive (23 SLNs), yielding 100% specificity and 52.6% sensitivity based on the current PA imaging system. Our machine-learning model can automatically detect SLNs in real time. In the new procedure, single-wavelength PA/US imaging uses CNPs as the contrast agent. The new system can, with that contrast agent, noninvasively image SLNs with high specificity in real time based on the unique features of the SLNs in the PA images. Ultimately, we aim to use our systems and approach to substitute or supplement nuclide tracers for a non-radioactive, less invasive SLN mapping method in SLNB for the axillary staging of breast cancer.
评估腋窝淋巴结的转移状态是早期乳腺癌分期中的常见临床操作。然而,前哨淋巴结(SLN)是被认为是转移癌细胞淋巴引流路径上的第一站的区域淋巴结。与腋窝淋巴结清扫相比,前哨淋巴结活检(SLNB)有助于降低发病率和副作用。然而,目前的SLNB方法仍具有一些不理想的特性,例如由于核素可及性的限制以及仅使用单一造影剂时治疗效果相对较低。为了克服这些限制,研究人员一直致力于开发一种非放射性的SLN定位方法来替代或补充放射性核素定位。我们提出并展示了一种使用双模态光声(PA)/超声(US)成像系统定位SLN以提供手术指导的临床程序。在我们的工作中,PA成像的高对比度及其对SLN的特异性基于碳纳米颗粒(CNP)在SLN中的积累。还训练并验证了一个机器学习模型,以基于单波长PA图像区分染色的SLN。在初步研究中,我们对11名患者进行了体内成像,并对13名患者的标本进行了体外研究。PA/US成像在体内识别出染色的SLN,无一例假阳性(23个SLN),基于当前的PA成像系统,特异性为100%,灵敏度为52.6%。我们的机器学习模型可以实时自动检测SLN。在新程序中,单波长PA/US成像使用CNP作为造影剂。基于PA图像中SLN的独特特征,新系统可以使用该造影剂实时无创地对SLN进行高特异性成像。最终,我们旨在使用我们的系统和方法替代或补充核素示踪剂,以在乳腺癌腋窝分期的SLNB中实现一种非放射性、侵入性较小的SLN定位方法。