Li Huijun, Wang Huan, Chen Fangfang, Gao Lei, Zhou Yurong, Zhou Zhou, Huang Jinbai, Xu Liying
Department of Medical Imaging, School of Medicine, Yangtze University, Jingzhou, China.
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Front Oncol. 2022 Oct 10;12:967655. doi: 10.3389/fonc.2022.967655. eCollection 2022.
To investigate the value of contrast-enhanced dual-layer spectral computed tomography (DLCT) in the detection of axillary lymph node (ALN) metastasis in breast cancer.
In this prospective study, 31 females with breast cancer underwent contrast-enhanced DLCT from August 2019 to June 2020. All ALNs were confirmed by postoperative histology. Spectral quantitative parameters, including (in Hounsfield units per kiloelectron-volt), nIC (normalized iodine concentration), and Z (Z-effective value) in both arterial and delay phases, were calculated and contrasted between metastatic and nonmetastatic ALNs using the McNemar test. Discriminating performance from metastatic and nonmetastatic ALNs was analyzed using receiver operating characteristic curves.
In total, 132 ALNs (52 metastatic and 80 nonmetastatic) were successfully matched between surgical labels and preoperative labels on DLCT images. All spectral quantitative parameters ( , nIC, and Z) derived from both arterial and delayed phases were greater in metastatic ALNs than in nonmetastatic SLNs (all < 0.001). Logistic regression analyses showed that in the delayed phase was the best single parameter for the detection of metastatic ALNs on a per-lymph node basis, with an area under the curve of 0.93, accuracy of 86.4% (114/132), sensitivity of 92.3% (48/52), and specificity of 87.5% (70/80).
The spectral quantitative parameters derived from contrast-enhanced DLCT, such as , can be applied for the preoperative detection of ALN metastasis in breast cancer.
探讨对比增强双层光谱计算机断层扫描（DLCT）在检测乳腺癌腋窝淋巴结（ALN）转移中的价值。
在这项前瞻性研究中，2019年8月至2020年6月期间，31例乳腺癌女性患者接受了对比增强DLCT检查。所有ALN均经术后组织学证实。计算动脉期和延迟期的光谱定量参数，包括（每千电子伏特的亨氏单位）、nIC（归一化碘浓度）和Z（Z有效价值），并使用McNemar检验对比转移和非转移ALN之间的差异。使用受试者工作特征曲线分析区分转移和非转移ALN的性能。
在DLCT图像上，手术标记和术前标记之间总共成功匹配了132个ALN（52个转移和80个非转移）。转移ALN中动脉期和延迟期的所有光谱定量参数（、nIC和Z）均高于非转移SLN（所有P<0.001）。逻辑回归分析表明，延迟期的是基于每个淋巴结检测转移ALN的最佳单一参数，曲线下面积为0.93，准确率为86.4%（114/132），灵敏度为92.3%（48/52），特异性为87.5%（70/80）。
对比增强DLCT得出的光谱定量参数，如，可用于乳腺癌ALN转移的术前检测。
