基于放射组学分析的亚厘米乳腺病变特征描述。
Characterization of Sub-1 cm Breast Lesions Using Radiomics Analysis.
机构信息
Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
出版信息
J Magn Reson Imaging. 2019 Nov;50(5):1468-1477. doi: 10.1002/jmri.26732. Epub 2019 Mar 27.
BACKGROUND
Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail.
PURPOSE
To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps.
STUDY TYPE
Retrospective, single center.
POPULATION
In all, 149 patients, with a total of 165 lesions scored as BI-RADS 4 or 5 on MRI, with an enhancing volume of <0.52 cm .
FIELD STRENGTH/SEQUENCE: Higher spatial resolution T -weighted dynamic contrast-enhanced imaging with a temporal resolution of ~90 seconds performed at 3.0T.
ASSESSMENT
Parameter maps reflecting initial enhancement, overall enhancement, area under the enhancement curve, and washout were generated. Heterogeneity measures based on first-order statistics, gray level co-occurrence matrices, run length matrices, size zone matrices, and neighborhood gray tone difference matrices were calculated. Data were split into a training dataset (75% of cases) and a test dataset (25% of cases).
STATISTICAL TESTS
Comparison of medians was assessed using the nonparametric Mann-Whitney U-test. The Spearman rank correlation coefficient was utilized to determine significant correlations between individual features. Finally, a support vector machine was employed to build multiparametric predictive models.
RESULTS
Univariate analysis revealed significant differences between benign and malignant lesions for 58/133 calculated features (P < 0.05). Support vector machine analysis resulted in areas under the curve (AUCs) ranging from 0.75-0.81. High negative (>89%) and positive predictive values (>83%) were found for all models.
DATA CONCLUSION
Radiomics analysis of small contrast-enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort.
LEVEL OF EVIDENCE
4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1468-1477.
背景
由于缺乏形态学和动力学细节，小的乳腺病变很难通过肉眼进行分类。
目的
利用无模型参数图评估放射组学分析在区分小的良性和恶性病变中的效果。
研究类型
回顾性、单中心研究。
人群
共有 149 名患者，共 165 个病变在 MRI 上评分为 BI-RADS 4 或 5，增强体积<0.52cm³。
磁场强度/序列：在 3.0T 上进行高空间分辨率 T1 加权动态对比增强成像，时间分辨率约为 90 秒。
评估
生成反映初始增强、总体增强、增强曲线下面积和洗脱的参数图。计算基于一阶统计、灰度共生矩阵、运行长度矩阵、大小区矩阵和邻域灰度差矩阵的异质性度量。数据分为训练数据集（75%的病例）和测试数据集（25%的病例）。
统计学检验
采用非参数曼-惠特尼 U 检验比较中位数。采用 Spearman 秩相关系数确定各特征之间的显著相关性。最后，采用支持向量机构建多参数预测模型。
结果
单变量分析显示，良性和恶性病变之间有 58/133 个计算特征存在显著差异（P<0.05）。支持向量机分析得出的曲线下面积（AUC）范围为 0.75-0.81。所有模型的阴性（>89%）和阳性预测值（>83%）均较高。
数据结论
小的增强型乳腺病变的放射组学分析具有一定价值。与本患者队列中基于初始增强确定的特征相比，增强曲线后期时间点计算的纹理特征提供的附加价值有限。
证据水平
4 级 技术效果：阶段 2 J. Magn. Reson. Imaging 2019;50:1468-1477.
Zotero 插件