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Published on: January 14, 2014
IDH Mutation Classification in Nonenhancing Gliomas: A Comparison of Habitat and Whole-Tumor Transfer Learning
Yu Han1, Yuyao Wang1, Wuxun Cui1
1Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, People's Republic of China.
Background:
Isocitrate dehydrogenase (IDH) mutation status is an important biomarker for the diagnosis and management of nonenhancing gliomas, underscoring the need for noninvasive preoperative classification.
Purpose:
To compare the value of habitat-based and whole-tumor strategies in classifying IDH mutation status in nonenhancing gliomas via transfer learning on structural magnetic resonance imaging and subtraction images.
Study Type:
Retrospective.
Population:
Two-hundred and eighty-four patients with nonenhancing gliomas, divided into a training set (n = 198; 44 ± 12 years; 83 females) and a testing set (n = 86; 46 ± 11 years; 35 females).
Field Strength/Sequence:
3T, fluid-attenuated inversion recovery (FLAIR), fast spin-echo (FSE) T2-weighted imaging (T2WI), FSE T1-weighted imaging (T1WI), contrast-enhanced FSE T1-weighted imaging (T1CE).
Assessment:
Based on FLAIR, T2WI, T1WI, T1CE, and subtraction images, two regions of interest input strategies were applied to construct transfer learning models, including whole-tumor strategy and habitat-based strategy. Model performance was evaluated using the area under curves (AUC) and accuracy (ACC). Finally, the optimal model was combined with clinical variables to develop integrative models.
Statistical Tests:
Continuous variables were analyzed by Student's t test or Wilcoxon rank-sum test; categorical variables by χ 2 test or Fisher's exact test. Two-sided p < 0.05 was statistically significant.
Results:
In the whole-tumor strategy, the subtraction model demonstrated significantly superior performance, achieving training and testing set AUC/ACC of 0.850/0.813 and 0.890/0.884. The habitat-based strategy significantly outperformed the whole-tumor strategy, with the T2WI model demonstrating optimal efficacy (training set, AUC/ACC = 0.898/0.899; testing set, AUC/ACC = 0.870/0.849). The integrative model (habitat-based T2WI + Age + Location) achieved the highest classification performance, with AUCs of 0.923 and 0.947 in the training and testing sets, respectively.
Data Conclusion:
The habitat-based strategy outperforms the whole-tumor approach, with the habitat-based T2WI model achieving optimal classification performance. Integrating age and tumor location into this model can further boost its classification capability.
Technical Efficacy:
Stage 2.
