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Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma

机译:18F-FDG PET / CT放射学特征区分乳腺淋巴瘤和乳腺癌的能力

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摘要

Purpose. To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in differentiating between breast lymphoma and breast carcinoma. Methods. A total of 67 breast nodules from 44 patients who underwent 18F-FDG PET/CT pretreatment were retrospectively analyzed. Radiomic parameters and SUV metrics were extracted using the LIFEx package on PET and CT images. All texture parameters were divided into six groups: histogram (HISTO), SHAPE, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone-length matrix (GLZLM). Receiver operating characteristics (ROC) curves were generated to evaluate the discriminative ability of each parameter, and the optimal parameter in each group was selected to generate a new predictive variable by using binary logistic regression. PET predictive variable, CT predictive variable, the combination of PET and CT predictive variables, and SUVmax were compared in terms of areas under the curve (AUCs), sensitivity, specificity, and accuracy. Results. Except for SUVmin (p=0.971), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (p ≤ 0.001), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (P < 0.001); SUVstd, 2.04 versus 0.88 g/ml (P=0.001); SUVmax, 10.69 versus 4.76 g/ml (P=0.001); SUVpeak, 9.15 versus 2.78 g/ml (P < 0.001); TLG, 42.24 versus 9.90 (P < 0.001). In the ROC curves analysis based on radiomic features and SUVmax, the AUC for SUVmax was 0.747, for CT texture parameters was 0.729, for PET texture parameters was 0.751, and for the combination of CT and PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the differentiation between breast lymphoma and carcinoma. The combination of SUVmax and PET/CT texture analysis may be promising to provide an effectively discriminant modality for the differential diagnosis of breast lymphoma and carcinoma, even for the differentiation of subtypes of lymphoma.
机译:目的。基于 18 F-FDG PET / CT的能力,探讨SUV指标和放射学特征在区分乳腺癌和淋巴瘤中的价值。方法。回顾性分析了44例行 18 F-FDG PET / CT预处理的患者的67个乳腺结节。使用LIFEx软件包在PET和CT图像上提取了放射性参数和SUV度量。所有纹理参数均分为六组:直方图(HISTO),SHAPE,灰度共现矩阵(GLCM),灰度游程矩阵(GLRLM),邻域灰度不同矩阵(NGLDM)和灰度级区域长度矩阵(GLZLM)。生成接收器工作特性(ROC)曲线以评估每个参数的判别能力,并使用二元logistic回归选择每个组中的最佳参数以生成新的预测变量。比较了PET预测变量,CT预测变量,PET和CT预测变量的组合以及SUVmax曲线下面积(AUC),敏感性,特异性和准确性。结果。除SUVmin(p = 0.971)外,淋巴瘤的FDG摄取量均值显着高于癌组织(p≤0.001),其中位数为SUVmean,分别为4.75和2.38μg/ ml(P <0.001); SUVstd,2.04对0.88μg/ ml(P = 0.001); SUVmax,10.69对4.76μg/ ml(P = 0.001); SUVpeak,9.15对2.78μg/ ml(P <0.001); TLG:42.24对9.90(P <0.001)。在基于放射学特征和SUVmax的ROC曲线分析中,SUVmax的AUC为0.747,CT纹理参数为0.729,PET纹理参数为0.751,CT和PET纹理参数的组合为0.771。结论。 18 FDG PET / CT图像中的SUV指标显示出在乳腺癌和淋巴瘤之间进行区分的潜在能力。 SUVmax和PET / CT纹理分析的结合可能有望为乳腺淋巴瘤和癌的鉴别诊断,甚至为淋巴瘤亚型的鉴别提供有效的判别方法。

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