Universitat Internacional Valenciana, Valencia, Spain,
MAGMA. 2011 Feb;24(1):35-42. doi: 10.1007/s10334-010-0241-8. Epub 2011 Jan 20.
This study demonstrates that 3T SV-MRS data can be used with the currently available automatic brain tumour diagnostic classifiers which were trained on databases of 1.5T spectra. This will allow the existing large databases of 1.5T MRS data to be used for diagnostic classification of 3T spectra, and perhaps also the combination of 1.5T and 3T databases.
Brain tumour classifiers trained with 154 1.5T spectra to discriminate among high grade malignant tumours and common grade II glial tumours were evaluated with a subsequently-acquired set of 155 1.5T and 37 3T spectra. A similarity study between spectra and main brain tumour metabolite ratios for both field strengths (1.5T and 3T) was also performed.
Our results showed that classifiers trained with 1.5T samples had similar accuracy for both test datasets (0.87 ± 0.03 for 1.5T and 0.88 ± 0.03 for 3.0T). Moreover, non-significant differences were observed with most metabolite ratios and spectral patterns.
These results encourage the use of existing classifiers based on 1.5T datasets for diagnosis with 3T (1)H SV-MRS. The large 1.5T databases compiled throughout many years and the prediction models based on 1.5T acquisitions can therefore continue to be used with data from the new 3T instruments.
本研究表明,3T SV-MRS 数据可与目前基于 1.5T 谱图数据库的自动脑肿瘤诊断分类器一起使用。这将允许使用现有的大型 1.5T MRS 数据数据库对 3T 谱图进行诊断分类,也许还可以结合 1.5T 和 3T 数据库。
使用 154 例 1.5T 谱图训练的脑肿瘤分类器,以区分高级别恶性肿瘤和常见的 II 级神经胶质瘤,然后用随后获得的 155 例 1.5T 和 37 例 3T 谱图进行评估。还对两种场强(1.5T 和 3T)的谱图和主要脑肿瘤代谢物比值进行了相似性研究。
我们的结果表明,使用 1.5T 样本训练的分类器在两个测试数据集上具有相似的准确性(1.5T 为 0.87 ± 0.03,3.0T 为 0.88 ± 0.03)。此外,大多数代谢物比值和光谱模式的差异无统计学意义。
这些结果鼓励使用基于 1.5T 数据集的现有分类器进行 3T(1)H SV-MRS 诊断。因此,可以继续使用多年来编译的大型 1.5T 数据库和基于 1.5T 采集的预测模型,同时使用新的 3T 仪器的数据。