基于放射组学的机器学习分析和多参数扩散加权磁共振对乳腺病变的特征描述。
Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR.
机构信息
Department of Radiology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Diagnostic Imaging, Alpert Medical School of Brown University, Providence, USA.
出版信息
J Transl Med. 2021 Oct 24;19(1):443. doi: 10.1186/s12967-021-03117-5.
BACKGROUND
This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions.
METHODS
This retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADC, mADC), BE (mD, mD, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance.
RESULTS
RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04).
CONCLUSIONS
The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.
背景
本研究旨在评估基于放射组学的机器学习分析在多参数 DWI 中的应用，并比较放射组学特征和平均扩散指标在乳腺病变特征中的诊断性能。
方法
本回顾性研究纳入了 2018 年 2 月至 2018 年 11 月的 542 个病灶。从单指数（ME）、双指数（BE）、拉伸指数（SE）和扩散峰度成像（DKI）中计算了 100 个放射组学特征。通过比较包括随机森林（RF）、主成分分析（PCA）、L1 正则化（L1R）和支持向量机（SVM）在内的四种分类器，进行基于放射组学的分析。这四种分类器通过十折交叉验证在包含 271 名患者的训练集中进行训练，并在包含 271 名患者的独立测试集中进行测试。还计算了 ME（mADC、mADC）、BE（mD、mD、mf）、SE（mDDC、mα）和 DKI（mK、mD）的平均扩散指标的诊断性能，以进行比较。使用接收者操作特征曲线（AUC）下的面积来比较诊断性能。
结果
RF 的 AUC 高于 L1R、PCA 和 SVM。用于鉴别乳腺病变的放射组学特征的 AUC 范围为 0.80（BE_D*）至 0.85（BE_D）。平均扩散指标的 AUC 范围为 0.54（BE_mf）至 0.79（ME_mADC）。在良性和恶性乳腺病变鉴别中，所有扩散指标的平均值与 AUC 之间的 AUC 存在显著差异（均 P<0.001）。在所计算的放射组学特征中，最重要的序列是 BE_D（AUC：0.85），最重要的特征是 FO-10 百分位数（特征重要性：0.04）。
结论
基于 RF 的多参数 DWI 放射组学分析比平均扩散指标更能区分良性和恶性乳腺病变。
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