Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma.
Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma.
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DOI：
10.1002/jmri.22432
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发表时间：
2011-02
影响因子：
4.4
通讯作者：
Wong, Stephen T.
中科院分区：
文献类型：
--
作者：
Hu, Xintao;Wong, Kelvin K.;Young, Geoffrey S.;Guo, Lei;Wong, Stephen T.
To automatically differentiate radiation necrosis from recurrent tumor at high spatial resolution using multi-parametric MRI features. MRI data retrieved from 31 patients (15 recurrent tumor and 16 radiation necrosis) who underwent chemoradiation therapy after surgical resection included post-Gd T1, T2, FLAIR, PD, ADC and PWI derived rCBV, rCBF and MTT maps. After alignment to post contrast T1WI, an 8-dimensional feature vector was constructed. An OC-SVMs classifier was trained using a radiation necrosis training set. Classifier parameters were optimized based on the area under ROC curve. The classifier was then tested on the full dataset. The sensitivity and specificity of optimized classifier for pseudoprogression was 89.91% and 93.72% respectively. The area under ROC curve was 0.9439. The distribution of voxels classified as radiation necrosis was supported by the clinical interpretation of followup scans for both non-progressing and progressing test cases. The ADC map derived from DWI and rCBV, rCBF derived from PWI were found to make a greater contribution to the discrimination than the conventional images. Machine learning using multi-parametric MRI features may be a promising approach to identify the distribution of radiation necrosis tissue in resected GBM patients undergoing chemoradiation.
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影响因子：
4.8
作者：
New, P
通讯作者：
New, P
DOI：
10.1016/s0360-3016(98)00266-1
发表时间：
1999-01-01
影响因子：
7
作者：
Lee, SW;Fraass, BA;Sandler, HM
通讯作者：
Sandler, HM
影响因子：
--
作者：
Cherkassky, V
通讯作者：
Cherkassky, V
影响因子：
8
作者：
Bradley, AP
通讯作者：
Bradley, AP
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V