Liu Jun, Wang Lanlan, Ai Zhaodong, Jian Lian, Yang Ming, Liu Siye, Yu Xiaoping
Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.
Quant Imaging Med Surg. 2024 Dec 5;14(12):8672-8685. doi: 10.21037/qims-24-598. Epub 2024 Nov 11.
The identification of the molecular subtypes of breast cancer is critical to determining appropriate treatment strategies and assessing prognosis. This study aimed to evaluate the ability of dual-layer spectral detector computed tomography (DLCT) metrics to differentiate luminal from nonluminal invasive breast cancer.
A total of 220 patients with invasive breast cancer who underwent routine DLCT examination were included in the study. The molecular subtypes of breast cancer were identified through immunohistochemical staining of biopsies or postoperative pathological specimens. DLCT quantitative parameters were compared between the luminal and nonluminal types of breast cancer. The diagnostic efficacy of these parameters was determined via receiver operating characteristic (ROC) curves. Univariate and multivariate regression analyses were conducted to identify independent predictors that could differentiate nonluminal from luminal breast cancer. A nomogram prediction model was established based on multivariate regression analysis. The performance of the nomogram model was assessed with ROC curve and calibration curve analyses.
Among the DLCT quantitative values, eight were significantly lower in the luminal type than in the nonluminal type of breast cancer (P<0.001-0.011). The area under the curve (AUC) values for these significant DLCT quantitative parameters ranged from 0.604 to 0.694. Multivariate logistic regression analysis identified CT-reported lymph node metastasis status [hazard ratio (HR) =4.214; P<0.001], the Hounsfield unit (HU) value of the virtual monoenergetic image at 40 keV (HU) (HR =2.628; P=0.012), and the normalized iodine concentration (nIC) (HR =2.182; P=0.041) as independent predictors of the nonluminal type, with an AUC of 0.754 [95% confidence interval (CI): 0.688-0.820]. The nomogram based on multivariate logistic regression analysis exhibited good discrimination and calibration (Hosmer-Lemeshow test; P=0.835). An average AUC value of 0.75 was obtained for the internal validation data.
DLCT quantitative parameters are valuable noninvasive indexes for differentiating between the luminal and nonluminal types of invasive breast cancer. Furthermore, the nomogram constructed in this study could guide individualized predictions of molecular subtypes in patients with invasive breast cancer.
乳腺癌分子亚型的识别对于确定合适的治疗策略和评估预后至关重要。本研究旨在评估双层光谱探测器计算机断层扫描（DLCT）指标区分管腔型与非管腔型浸润性乳腺癌的能力。
本研究纳入了220例行常规DLCT检查的浸润性乳腺癌患者。通过活检或术后病理标本的免疫组织化学染色确定乳腺癌的分子亚型。比较管腔型和非管腔型乳腺癌的DLCT定量参数。通过受试者操作特征（ROC）曲线确定这些参数的诊断效能。进行单因素和多因素回归分析，以识别可区分非管腔型和管腔型乳腺癌的独立预测因素。基于多因素回归分析建立列线图预测模型。通过ROC曲线和校准曲线分析评估列线图模型的性能。
在DLCT定量值中，管腔型乳腺癌的8个值显著低于非管腔型（P<0.001-0.011）。这些显著的DLCT定量参数的曲线下面积（AUC）值范围为0.604至0.694。多因素逻辑回归分析确定CT报告的淋巴结转移状态[风险比（HR）=4.214；P<0.001]、40keV时虚拟单能图像的Hounsfield单位（HU）值（HR =2.628；P=0.012）和归一化碘浓度（nIC）（HR =2.182；P=0.041）为非管腔型的独立预测因素，AUC为0.754[95%置信区间（CI）：0.688-0.820]。基于多因素逻辑回归分析的列线图显示出良好的区分度和校准度（Hosmer-Lemeshow检验；P=0.835）。内部验证数据的平均AUC值为0.75。
DLCT定量参数是区分管腔型和非管腔型浸润性乳腺癌的有价值的非侵入性指标。此外，本研究构建的列线图可指导浸润性乳腺癌患者分子亚型的个体化预测。
