Department of Radiology, Fortis Memorial Research Institute, Sector 44, Gurgaon, Haryana, 122002, India.
Centre for Biomedical Engineering, IIT Delhi, New Delhi, India.
Neuroradiology. 2021 Aug;63(8):1227-1239. doi: 10.1007/s00234-021-02636-8. Epub 2021 Jan 19.
This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation.
Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features.
Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma.
We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.
本回顾性研究在 3T MRI 上进行，旨在确定少突胶质细胞瘤和星形细胞瘤的独特常规磁共振成像和 T1 加权 DCE-MRI 特征，并探讨机器学习算法在其鉴别中的应用。
根据 2016 年 WHO 分类，对 81 例未经治疗的患者进行组织学确认，将其分为两组：少突胶质细胞瘤（n=16；2 级，n=25；3 级）和星形细胞瘤（n=10；2 级，n=30；3 级）。使用 Z 检验评估肿瘤形态特征的差异。使用内部构建的 MATLAB 程序分析 T1 加权 DCE-MRI 数据。使用独立学生 t 检验评估相对脑血流量、相对脑血容量校正值、从血浆到细胞外细胞外空间的容积转移率和细胞外细胞外空间容积的 90 百分位数均值。基于具有统计学意义的特征，构建支持向量机（SVM）分类器，以区分 II 级、III 级和 II+III 级的两组。
Z 检验仅在常规 MR 特征中表明钙化可用于区分 III 级和 II+III 级的少突胶质细胞瘤和星形细胞瘤。两组及其亚型之间的灌注参数无统计学差异。基于钙化训练的 SVM 也能提供区分少突胶质细胞瘤和星形细胞瘤的中等准确性。
我们得出的结论是，除钙化外，常规磁共振特征和定量 T1 加权 DCE-MRI 参数均无法区分少突胶质细胞瘤和星形细胞瘤。SVM 不能进一步帮助其区分。该研究还表明，T2-FLAIR 不匹配超过 50%可能被认为是 IDH 突变星形细胞瘤鉴别诊断的更具决定性的标志。
