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.

中文翻译：

---

使用基于MRI的放射线学和机器学习预测垂体大腺瘤的高增殖指数。

目的垂体腺瘤是最常见的颅内肿瘤之一。它们可能表现出具有临床攻击性的行为，伴有复发性疾病和对多式联运疗法的抵抗力。ki-67标记指数代表与垂体腺瘤侵袭性相关的增殖标志物。我们研究的目的是评估机器学习分析脑垂体腺瘤术前MRI纹理衍生参数对ki-67增殖指数类别的预测的准确性。方法纳入89例行鼻内镜下垂体腺瘤摘除术并具有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。为了选择最有用的特征，采用了不同的监督特征选择方法。后来，使用k近邻（k-NN）分类器预测宏观腺瘤的高或低增殖指数。使用火车测试方法进行算法验证。结果在特征选择中得出的12个子集中，表现最好的一个子集由Pearson检验中的4个最高相关参数构成。这些都显示了非常好的（ICC≥0.85）观察者间可重复性。在正确分类的患者中，测试组的k-NN总体准确性为91.67％（33/36）。结论术前T2 MRI对纹理衍生参数的机器学习分析已被证明可有效地预测垂体腺瘤ki-67增殖指数。