Department of Imaging Physics, The University of Texas MD Anderson Cancer Center (UT MDACC), Houston, Texas.
UT MDACC UTHealth Graduate School of Biomedical Sciences, Houston, Texas.
Neuro Oncol. 2019 Mar 18;21(4):527-536. doi: 10.1093/neuonc/noz004.
Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard.
MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain.
Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, Ktrans). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R2 = 0.75). A less accurate predictive result (RMS error 5.4%, R2 = 0.50) was found using conventional imaging only.
Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients.
首次活检时对胶质瘤的采样不足是一个主要的临床问题，因为准确的分级决定了所有后续的治疗。我们提交了一种技术解决方案，通过使用磁共振成像（MRI）数据作为输入来估计肿瘤增殖标志物（Ki-67），从而减少采样不足的问题，该方法以立体定向组织病理学金标准为对照。
在一项前瞻性临床试验中，对未经治疗的胶质瘤患者进行解剖学、弥散、通透性和灌注序列的 MRI 检查。在每个患者接受手术切除之前，立即从每个患者中采集立体定向活检。对于每个活检，制定了影像学描述（23 个参数），并记录了 Ki-67 指数。构建机器学习模型以从影像学输入中估算 Ki-67，并进行交叉验证以确定估计值的误差。使用最佳模型生成整个大脑的 Ki-67 估计图。
从 23 例可评估患者中收集了 52 个图像引导活检。随机森林算法用 4 个影像学输入（T2 加权、各向异性分数、脑血流、Ktrans）对 Ki-67 进行最佳建模。它以 3.5%的均方根误差（RMS）（R2 = 0.75）预测 Ki-67 的表达水平。仅使用常规影像学时，预测结果的准确性较低（RMS 误差 5.4%，R2 = 0.50）。
可以使用临床影像学数据以临床有用的准确度预测 Ki-67。高级影像学（弥散、灌注和通透性）比单独使用常规影像学可提高预测准确性。Ki-67 预测结果以图形地图显示，可以用于指导胶质瘤患者的活检、切除和/或放疗。
