Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning
Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning
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DOI：
10.1007/s00234-019-02266-1
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发表时间：
2019-12-01
期刊：
影响因子：
2.8
通讯作者：
Brunetti, Arturo
中科院分区：
文献类型：
--
作者：
Ugga, Lorenzo;Cuocolo, Renato;Brunetti, Arturo
目的垂体腺瘤是最常见的颅内肿瘤之一。他们可能表现出临床攻击行为、疾病复发和对多模式治疗的抵抗力。 ki-67 标记指数代表与垂体腺瘤侵袭性相关的增殖标记。我们研究的目的是评估机器学习分析垂体腺瘤术前 MRI 纹理衍生参数预测 ki-67 增殖指数类别的准确性。方法 纳入 89 例接受内镜鼻内手术切除垂体腺瘤且具有可用 ki-67 标记指数的患者。从 T2w MR 图像中，提取了 1128 个定量成像特征。为了选择信息最丰富的特征，采用了不同的监督特征选择方法。随后，采用 k 最近邻 (k-NN) 分类器来预测大腺瘤增殖指数的高低。使用训练测试方法进行算法验证。结果 在从特征选择中得出的 12 个子集中，表现最好的一个是由 Pearson 检验中 4 个最高相关参数构成的。这些都显示出非常好的（ICC >= 0.85）观察者间再现性。测试组中 k-NN 的总体准确度为正确分类患者的 91.67% (33/36)。结论 术前 T2 MRI 纹理衍生参数的机器学习分析已被证明对于预测垂体大腺瘤 ki-67 增殖指数类别是有效的。这可能有助于手术策略做出更准确的术前病变分类，并允许更有针对性和更具成本效益的随访和长期管理。
Purpose Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class. Methods A total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach. Results Of the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson's test. These all showed very good (ICC >= 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients. Conclusions Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.