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术前识别原发性中枢神经系统淋巴瘤的正常化时间-强度曲线:一种新的 DSC-PWI 分析方法的初步研究。

Presurgical Identification of Primary Central Nervous System Lymphoma with Normalized Time-Intensity Curve: A Pilot Study of a New Method to Analyze DSC-PWI.

机构信息

Radiology Department (A.P.-E., P.N.-B., M.C., C.M.), Institut de Diagnòstic per la Imatge, Hospital Universitari de Bellvitge. L'Hospitalet de Llobregat, Barcelona, Spain

Neurooncology Unit (A.P.-E., N.V., G.P., J.B., C.M.), Insitut Català d'Oncologia, Institut d'Investigació Biomèdica de Bellvitge, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain.

出版信息

AJNR Am J Neuroradiol. 2020 Oct;41(10):1816-1824. doi: 10.3174/ajnr.A6761. Epub 2020 Sep 17.

Abstract

BACKGROUND AND PURPOSE

DSC-PWI has demonstrated promising results in the presurgical diagnosis of brain tumors. While most studies analyze specific parameters derived from time-intensity curves, very few have directly analyzed the whole curves. The aims of this study were the following: 1) to design a new method of postprocessing time-intensity curves, which renders normalized curves, and 2) to test its feasibility and performance on the diagnosis of primary central nervous system lymphoma.

MATERIALS AND METHODS

Diagnostic MR imaging of patients with histologically confirmed primary central nervous system lymphoma were retrospectively reviewed. Correlative cases of glioblastoma, anaplastic astrocytoma, metastasis, and meningioma, matched by date and number, were retrieved for comparison. Time-intensity curves of enhancing tumor and normal-appearing white matter were obtained for each case. Enhancing tumor curves were normalized relative to normal-appearing white matter. We performed pair-wise comparisons for primary central nervous system lymphoma against the other tumor type. The best discriminatory time points of the curves were obtained through a stepwise selection. Logistic binary regression was applied to obtain prediction models. The generated algorithms were applied in a test subset.

RESULTS

A total of 233 patients were included in the study: 47 primary central nervous system lymphomas, 48 glioblastomas, 39 anaplastic astrocytomas, 49 metastases, and 50 meningiomas. The classifiers satisfactorily performed all bilateral comparisons in the test subset (primary central nervous system lymphoma versus glioblastoma, area under the curve = 0.96 and accuracy = 93%; versus anaplastic astrocytoma, 0.83 and 71%; versus metastases, 0.95 and 93%; versus meningioma, 0.93 and 96%).

CONCLUSIONS

The proposed method for DSC-PWI time-intensity curve normalization renders comparable curves beyond technical and patient variability. Normalized time-intensity curves performed satisfactorily for the presurgical identification of primary central nervous system lymphoma.

摘要

背景与目的

DSC-PWI 在脑肿瘤的术前诊断中显示出了有前景的结果。虽然大多数研究分析了源自时间-强度曲线的特定参数,但很少有研究直接分析整个曲线。本研究的目的如下:1)设计一种新的时间-强度曲线后处理方法,使曲线归一化,2)检验其在原发性中枢神经系统淋巴瘤诊断中的可行性和性能。

材料与方法

回顾性分析经组织学证实的原发性中枢神经系统淋巴瘤患者的诊断性磁共振成像。为了进行比较,检索了日期和数量相匹配的胶质母细胞瘤、间变性星形细胞瘤、转移瘤和脑膜瘤的相关病例。为每个病例获取增强肿瘤和正常白质的时间-强度曲线。将增强肿瘤曲线相对于正常白质进行归一化。我们对原发性中枢神经系统淋巴瘤与其他肿瘤类型进行了两两比较。通过逐步选择获得曲线的最佳鉴别时间点。应用逻辑二进制回归获得预测模型。在测试子集上应用生成的算法。

结果

本研究共纳入 233 例患者:47 例原发性中枢神经系统淋巴瘤、48 例胶质母细胞瘤、39 例间变性星形细胞瘤、49 例转移瘤和 50 例脑膜瘤。分类器在测试子集的所有双边比较中表现良好(原发性中枢神经系统淋巴瘤与胶质母细胞瘤,曲线下面积=0.96,准确率=93%;与间变性星形细胞瘤,0.83,71%;与转移瘤,0.95,93%;与脑膜瘤,0.93,96%)。

结论

提出的 DSC-PWI 时间-强度曲线归一化方法可在技术和患者变异性之外生成可比较的曲线。归一化的时间-强度曲线在原发性中枢神经系统淋巴瘤的术前识别中表现良好。

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