Soussan Michael, Orlhac Fanny, Boubaya Marouane, Zelek Laurent, Ziol Marianne, Eder Véronique, Buvat Irène
Paris 13 University, Sorbonne Paris Cité, Bobigny, France; Department of Nuclear Medicine, AP-HP, Avicenne University Hospital, Bobigny, France; IMNC - UMR 8165 CNRS - Paris 7 and Paris 11 Universities, Orsay, France.
IMNC - UMR 8165 CNRS - Paris 7 and Paris 11 Universities, Orsay, France.
PLoS One. 2014 Apr 10;9(4):e94017. doi: 10.1371/journal.pone.0094017. eCollection 2014.
There is currently little support to understand which pathological factors led to differences in tumor texture as measured from FDG PET/CT images. We studied whether tumor heterogeneity measured using texture analysis in FDG-PET/CT images is correlated with pathological prognostic factors in invasive breast cancer.
Fifty-four patients with locally advanced breast cancer who had an initial FDG-PET/CT were retrospectively included. In addition to SUVmax, three robust textural indices extracted from 3D matrices: High-Gray-level Run Emphasis (HGRE), Entropy and Homogeneity were studied. Univariate and multivariate logistic regression was used to identify PET parameters associated with poor prognosis pathological factors: hormone receptor negativity, presence of HER-2 and triple negative phenotype. Receiver operating characteristic (ROC) curves and the (AUC) analysis, and reclassification measures, were performed in order to evaluate the performance of combining texture analysis and SUVmax for characterizing breast tumors.
Tumor heterogeneity, measured with HGRE, was higher in negative estrogen receptor (p = 0.039) and negative progesterone receptor tumors (p = 0.036), and in Scarff-Bloom-Richardson grade 3 tumors (p = 0.047). None of the PET indices could identify HER-2 positive tumors. Only SUVmax was positively correlated with Ki-67 (p<0.0004). Triple negative breast cancer (TNBC) exhibited higher SUVmax (Odd Ratio = 1.22, 95%CI [1.06-1.39],p = 0.004), lower Homogeneity (OR = 3.57[0.98-12.5],p = 0.05) and higher HGRE (OR = 8.06[1.88-34.51],p = 0.005) than non-TNBC. Multivariate analysis showed that HGRE remained associated with TNBC (OR = 5.27[1.12-1.38],p = 0.03) after adjustment for SUVmax. Combining SUVmax and HGRE yielded in higher area under the ROC curves (AUC) than SUVmax for identifying TNBC: AUC = 0.83 and 0.77, respectively. Probability of correct classification also increased in 77% (10/13) of TNBC and 71% (29/41) of non-TNBC (p = 0.003), when combining SUVmax and HGRE.
Tumor heterogeneity measured on FDG-PET/CT was higher in invasive breast cancer with poor prognosis pathological factors. Texture analysis might be used, in addition to SUVmax, as a new tool to assess invasive breast cancer aggressiveness.
目前，对于哪些病理因素导致了从FDG PET/CT图像测量的肿瘤纹理差异，几乎没有相关研究支持。我们研究了在FDG-PET/CT图像中使用纹理分析测量的肿瘤异质性是否与浸润性乳腺癌的病理预后因素相关。
回顾性纳入54例初诊时进行FDG-PET/CT检查的局部晚期乳腺癌患者。除SUVmax外，还研究了从3D矩阵中提取的三个稳健纹理指标：高灰度级游程强调（HGRE）、熵和均匀性。采用单因素和多因素逻辑回归来确定与预后不良病理因素相关的PET参数：激素受体阴性、HER-2的存在和三阴性表型。进行受试者操作特征（ROC）曲线和曲线下面积（AUC）分析以及重新分类测量，以评估结合纹理分析和SUVmax对乳腺肿瘤进行特征化的性能。
用HGRE测量的肿瘤异质性在雌激素受体阴性（p = 0.039）和孕激素受体阴性肿瘤（p = 0.036）以及斯卡夫-布卢姆-理查森3级肿瘤（p = 0.047）中更高。没有一个PET指标能够识别HER-2阳性肿瘤。只有SUVmax与Ki-67呈正相关（p<0.0004）。三阴性乳腺癌（TNBC）的SUVmax更高（优势比=1.22，95%置信区间[1.06 - 1.39]，p = 0.004），均匀性更低（OR = 3.57[0.98 - 12.5]，p = 0.05），HGRE更高（OR = 8.06[1.88 - 34.51]，p = 0.005），高于非TNBC。多因素分析显示，在调整SUVmax后，HGRE仍与TNBC相关（OR = 5.27[1.12 - 1.38]，p = 0.03）。结合SUVmax和HGRE在识别TNBC时，ROC曲线下面积（AUC）高于单独使用SUVmax：分别为AUC = 0.83和0.77。当结合SUVmax和HGRE时，TNBC的正确分类概率也增加了77%（10/13），非TNBC增加了71%（29/41）（p = 0.003）。
在具有预后不良病理因素的浸润性乳腺癌中，FDG-PET/CT测量的肿瘤异质性更高。除SUVmax外，纹理分析可能用作评估浸润性乳腺癌侵袭性的新工具。
