Background

Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG)-PET.

Methods

The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used.

Results

In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. ¹⁸F-FDG-PET and morphologic features were less predictive.

Conclusion

Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions.

中文翻译：

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通过识别信息丰富的多参数PET / MRI特征，对乳房病变进行自动分割和分类。

背景

多参数正电子发射断层扫描/磁共振成像（mpPET / MRI）显示出对乳腺病变进行检测和分类的临床潜力。但是，需要更好地理解功能对计算机辅助分割和诊断（CAD）的贡献。我们为CAD系统提出了一种数据驱动的机器学习方法，该方法结合了动态对比度增强（DCE）-MRI，扩散加权成像（DWI）和¹⁸ F-氟脱氧葡萄糖（¹⁸ F-FDG）-PET。

方法

CAD将随机森林（RF）分类器与基于mpPET / MRI强度的特征相结合，用于病变分割和形状特征，动力学和时空纹理特征，用于病变分类。CAD管道检测并分割了可疑区域，并将病变分类为良性或恶性。使用了RF固有的特征选择方法以及最小冗余最大相关特征排序方法。

结果

在34例患者中，我们报告良性和恶性病变的检出率分别为10/12（83.3％）和22/22（100％），Dice相似度系数为0.665进行分割，并且分类性能低于接收器工作特性分析的曲线为0.978，灵敏度为0.946，特异性为0.936。DWI和FDG-PET提供的信息可改善DCE-MRI的分割效果，但不会提高分类性能。特征分级显示，动力学和时空纹理特征对病变分类的贡献最大。¹⁸ F-FDG-PET和形态特征的预测性较差。

结论

我们的CAD可以评估mpPET / MRI功能与分割和分类准确性的相关性。它可以作为一种新颖的计算工具，帮助探索不同的模式/特征及其对乳腺病变的检测和分类的作用。