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 χ2 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.
Level of evidence: 3:
Technical efficacy: Stage 2.