Department of Radiology, Seoul National University Hospital, South Korea.
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
Magn Reson Imaging. 2019 Nov;63:60-69. doi: 10.1016/j.mri.2019.08.026. Epub 2019 Aug 16.
Somatic mutations in TP53 and PIK3CA genes, the two most frequent genetic alternations in breast cancer, are associated with prognosis and therapeutic response. This study predicted the presence of TP53 and PIK3CA mutations in breast cancer by using texture and morphology analyses on breast MRI.
A total of 107 breast cancers (dataset A) from The Cancer Imaging Archive (TCIA) consisting of 40 TP53 mutation cancer and 67 cancers without TP53 mutation; 35 PIK3CA mutations cancer and 72 without PIK3CA mutation. 122 breast cancer (dataset B) from Seoul National University Hospital containing 54 TP53 mutation cancer and 68 without mutations were used in this study. At first, the tumor area was segmented by a region growing method. Subsequently, gray level co-occurrence matrix (GLCM) texture features were extracted after ranklet transform, and a series of features including compactness, margin, and ellipsoid fitting model were used to describe the morphological characteristics of tumors. Lastly, a logistic regression was used to identify the presence of TP53 and PIK3CA mutations. The classification performances were evaluated by accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Taking into account the trade-offs of sensitivity and specificity, the overall performances were evaluated by using receiver operating characteristic (ROC) curve analysis.
The GLCM texture feature based on ranklet transform is more capable of recognizing TP53 and PIK3CA mutations than morphological feature, especially for the TP53 mutation that achieves statistically significant. The area under the ROC curve (AUC) for TP53 mutation dataset A and dataset B achieved 0.78 and 0.81 respectively. For PIK3CA mutation, the AUC of ranklet texture feature was 0.70.
Texture analysis of segmented tumor on breast MRI based on ranklet transform is potential in recognizing the presence of TP53 mutation and PIK3CA mutation.
TP53 和 PIK3CA 基因的体细胞突变是乳腺癌中最常见的两种遗传改变，与预后和治疗反应相关。本研究通过对乳腺 MRI 的纹理和形态分析，预测乳腺癌中 TP53 和 PIK3CA 突变的存在。
本研究共纳入了来自癌症成像档案（TCIA）的 107 例乳腺癌患者（数据集 A），包括 40 例 TP53 突变型癌症和 67 例无 TP53 突变型癌症；35 例 PIK3CA 突变型癌症和 72 例无 PIK3CA 突变型癌症。还纳入了来自首尔国立大学医院的 122 例乳腺癌患者（数据集 B），其中 54 例为 TP53 突变型癌症，68 例为无突变型癌症。首先，采用区域生长法对肿瘤区域进行分割。然后，在秩勒变换后提取灰度共生矩阵（GLCM）纹理特征，并使用紧凑度、边缘和椭圆拟合模型等一系列特征来描述肿瘤的形态特征。最后，采用逻辑回归来识别 TP53 和 PIK3CA 突变的存在。通过准确性、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）来评估分类性能。考虑到敏感性和特异性的权衡，通过 ROC 曲线分析评估整体性能。
基于秩勒变换的 GLCM 纹理特征比形态特征更能识别 TP53 和 PIK3CA 突变，尤其是对 TP53 突变的识别能力具有统计学意义。数据集 A 和数据集 B 的 TP53 突变的 ROC 曲线下面积（AUC）分别为 0.78 和 0.81。对于 PIK3CA 突变，秩勒纹理特征的 AUC 为 0.70。
基于秩勒变换的乳腺 MRI 分割肿瘤的纹理分析有可能识别 TP53 突变和 PIK3CA 突变的存在。
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