IRCCS SDN, Via Gianturco n 113, Naples, Italy.
Department of Pathology, Ospedale Moscati, Avellino, Italy.
Eur J Nucl Med Mol Imaging. 2018 Sep;45(10):1680-1693. doi: 10.1007/s00259-018-4010-7. Epub 2018 Apr 25.
The aim of this study was to determine if functional parameters extracted from the hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) correlate with the immunohistochemical markers of breast cancer (BC) lesions, to assess their ability to predict BC subtype.
This prospective study was approved by the institution's Ethics Committee, and all patients provided written informed consent. A total of 50 BC patients at diagnosis underwent PET/MRI before pharmacological and surgical treatment. For each primary lesion, the following data were extracted: morphological data including tumour-node-metastasis stage and lesion size; apparent diffusion coefficient (ADC); perfusion data including forward volume transfer constant (Ktrans), reverse efflux volume transfer constant (Kep) and extravascular extracellular space volume (Ve); and metabolic data including standardized uptake value (SUV), lean body mass (SUL), metabolic tumour volume and total lesion glycolysis. Immunohistochemical reports were used to determine receptor status (oestrogen, progesterone, and human epidermal growth factor receptor 2), cellular differentiation status (grade), and proliferation index (Ki67) of the tumour lesions. Correlation studies (Mann-Whitney U test and Spearman's test), receiver operating characteristic (ROC) curve analysis, and multivariate analysis were performed.
Association studies were performed to assess the correlations between imaging and histological prognostic markers of BC. Imaging biomarkers, which significantly correlated with biological markers, were selected to perform ROC curve analysis to determine their ability to discriminate among BC subtypes. SUV, SUV and SUL were able to discriminate between luminal A and luminal B subtypes (AUC = 0.799; AUC = 0.833; AUC = 0.813) and between luminal A and nonluminal subtypes (AUC = 0.926; AUC = 0.917; AUC = 0.945), and the lowest SUV and SUL values were associated with the luminal A subtype. Kep was able to discriminate between luminal A and luminal B subtypes (AUC = 0.779), and its highest values were associated with the luminal B subtype. Ktrans (AUC = 0.881) was able to discriminate between luminal A and nonluminal subtypes, and the highest perfusion values were associated with the nonluminal subtype. In addition, ADC (AUC = 0.877) was able to discriminate between luminal B and nonluminal subtypes, and the lowest ADC values were associated with the luminal B subtype. Multivariate analysis was performed to develop a prognostic model, and the best predictive model included Ktrans and SUV parameters.
Using multivariate analysis of both PET and MRI parameters, a prognostic model including Ktrans and SUV was able to predict the tumour subtype in 38 of 49 patients (77.6%, p < 0.001), with higher accuracy for the luminal B subtype (86.2%).
本研究旨在确定从正电子发射断层扫描/磁共振成像（PET/MRI）融合图像中提取的功能参数是否与乳腺癌（BC）病变的免疫组织化学标志物相关，以评估其预测 BC 亚型的能力。
这项前瞻性研究得到了机构伦理委员会的批准，所有患者均提供了书面知情同意书。共 50 例初诊 BC 患者在药物和手术治疗前接受了 PET/MRI 检查。对每个原发性病变，提取以下数据：形态学数据，包括肿瘤-淋巴结-转移分期和病变大小；表观扩散系数（ADC）；灌注数据，包括正向容积转移常数（Ktrans）、反向流出容积转移常数（Kep）和细胞外空间容积（Ve）；代谢数据，包括标准化摄取值（SUV）、瘦体质量（SUL）、代谢肿瘤体积和总肿瘤糖酵解。免疫组织化学报告用于确定肿瘤病变的受体状态（雌激素、孕激素和人表皮生长因子受体 2）、细胞分化状态（分级）和增殖指数（Ki67）。进行了相关性研究（Mann-Whitney U 检验和 Spearman 检验）、受试者工作特征（ROC）曲线分析和多变量分析。
进行关联研究以评估成像与 BC 生物学预后标志物之间的相关性。选择与生物学标志物显著相关的成像生物标志物进行 ROC 曲线分析，以确定其区分 BC 亚型的能力。SUV、SUV 和 SUL 能够区分 luminal A 和 luminal B 亚型（AUC=0.799；AUC=0.833；AUC=0.813）和 luminal A 和非 luminal 亚型（AUC=0.926；AUC=0.917；AUC=0.945），且最低的 SUV 和 SUL 值与 luminal A 亚型相关。Kep 能够区分 luminal A 和 luminal B 亚型（AUC=0.779），其最高值与 luminal B 亚型相关。Ktrans（AUC=0.881）能够区分 luminal A 和非 luminal 亚型，并且与非 luminal 亚型相关的灌注值最高。此外，ADC（AUC=0.877）能够区分 luminal B 和非 luminal 亚型，并且与 luminal B 亚型相关的 ADC 值最低。进行了多变量分析以建立预后模型，最佳预测模型包括 Ktrans 和 SUV 参数。
使用 PET 和 MRI 参数的多变量分析，包括 Ktrans 和 SUV 的预后模型能够在 49 例患者中的 38 例（77.6%，p<0.001）中预测肿瘤亚型，对 luminal B 亚型的准确性更高（86.2%）。
