Araz Mine, Soydal Çiğdem, Gündüz Pınar, Kırmızı Ayça, Bakırarar Batuhan, Dizbay Sak Serpil, Özkan Elgin
Ankara University Faculty of Medicine, Department of Nuclear Medicine, Ankara, Turkey.
Ankara University Faculty of Medicine, Department of Pathology, Ankara, Turkey.
Mol Imaging Radionucl Ther. 2022 Feb 2;31(1):49-56. doi: 10.4274/mirt.galenos.2022.59140.
This study aimed to investigate the role of preoperative 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features and metabolic parameters of primary breast tumors in predicting hormone receptor (HR) positivity.
A total of 153 patients with breast carcinoma who underwent preoperative F-FDG PET/CT were included. All PET/CT images were retrospectively reevaluated. Radiomics features of primary breast lesions reflecting tumor heterogeneity as well as standardized uptake value (SUV) metrics (SUV, SUV, SUV, and SUV) and volumetric parameters such as metabolic tumor volume and total lesion glycolysis (TLG) were extracted by commercial texture analysis software package (LIFEx; https://www.lifexsoft.org/ index.php). WEKA and SPSS were used for statistical analysis. Binary logistic regression analysis was used to determine texture features predicting HR positivity. Accuracy, F-measure, precision, recall, and precision-recall curve area were used as data-mining performance criteria of texture features to predict HR positivity.
None of the radiomics parameters were significant in predicting HR status. Only SUV metrics and TLG were statistically important. Mean ± standard deviations for SUV, SUV, and SUV for the HR-negative group were significantly higher than those in the HR-positive group (6.73±4.36 vs. 5.20±3.32, p=0.027; 11.55±7.42 vs. 8.63±5.23, p=0.006; and 8.37±6.81 vs. 5.72±4.86; p=0.012). Cut-off values of SUV, SUV, and SUV for the prediction of HR positivity were 4.93, 8.35, and 6.02, respectively. Among data-mining methods, logistic regression showed the best performance with accuracy of 0.762.
In addition to the relatively limited number of patients in this study, radiomics parameters cannot predict the HR status of primary breast cancer. SUV levels of the HR-negative group were significantly higher than those of the HR-positive group. To clarify the role of metabolic and radiomics parameters in predicting HR status in breast cancer, further studies involving a larger study population are needed.
本研究旨在探讨原发性乳腺肿瘤的术前18氟-氟脱氧葡萄糖（18F-FDG）正电子发射断层扫描/计算机断层扫描（PET/CT）影像组学特征和代谢参数在预测激素受体（HR）阳性方面的作用。
纳入153例术前行F-FDG PET/CT检查的乳腺癌患者。所有PET/CT图像均进行回顾性重新评估。使用商业纹理分析软件包（LIFEx；https://www.lifexsoft.org/index.php）提取反映肿瘤异质性的原发性乳腺病变的影像组学特征以及标准化摄取值（SUV）指标（SUV最大值、SUV平均值、SUV最小值和SUV峰值）和代谢肿瘤体积、总病变糖酵解（TLG）等体积参数。使用WEKA和SPSS进行统计分析。采用二元逻辑回归分析确定预测HR阳性的纹理特征。准确性、F值、精确率、召回率和精确率-召回率曲线面积用作纹理特征预测HR阳性的数据挖掘性能标准。
影像组学参数在预测HR状态方面均无显著性。只有SUV指标和TLG具有统计学意义。HR阴性组的SUV最大值、SUV平均值和SUV峰值的均值±标准差显著高于HR阳性组（6.73±4.36 vs. 5.20±3.32，p = 0.027；11.55±7.42 vs. 8.63±5.23，p = 0.006；8.37±6.81 vs. 5.72±4.86；p = 0.012）。预测HR阳性的SUV最大值、SUV平均值和SUV峰值的截断值分别为4.93、8.35和6.02。在数据挖掘方法中，逻辑回归表现最佳，准确性为0.762。
除本研究患者数量相对有限外，影像组学参数无法预测原发性乳腺癌的HR状态。HR阴性组的SUV水平显著高于HR阳性组。为阐明代谢和影像组学参数在预测乳腺癌HR状态中的作用，需要开展涉及更大研究人群的进一步研究。
