Deep learning-based quantification of T2-FLAIR mismatch sign: extending IDH mutation prediction in adult-type diffuse lower-grade glioma.
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| Title: | Deep learning-based quantification of T2-FLAIR mismatch sign: extending IDH mutation prediction in adult-type diffuse lower-grade glioma. |
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| Authors: | Jeon, Young Hun1 (AUTHOR), Choi, Kyu Sung1,2 (AUTHOR) kyuchoi86@gmail.com, Lee, Kyung Hoon3 (AUTHOR), Jeong, Seong Yun1 (AUTHOR), Lee, Ji Ye1,2 (AUTHOR), Ham, Taehyuk1 (AUTHOR), Hwang, Inpyeong1,2 (AUTHOR), Yoo, Roh-Eul1,2 (AUTHOR), Kang, Koung Mi1,2 (AUTHOR), Yun, Tae Jin1,2 (AUTHOR), Choi, Seung Hong1,2 (AUTHOR), Kim, Ji-hoon1,2 (AUTHOR), Sohn, Chul-Ho1,2 (AUTHOR) |
| Source: | European Radiology. Sep2025, Vol. 35 Issue 9, p5193-5202. 10p. |
| Subjects: | Deep learning, Genetic mutation, Neurosurgery, Tumor classification, Computer-assisted image analysis (Medicine), Prognosis, Astrocytomas |
| Abstract: | Objectives: To investigate the predictive value of the quantitative T2-FLAIR mismatch ratio (qT2FM) with fully automated tumor segmentation in adult-type diffuse lower-grade gliomas (LGGs). Materials and methods: This retrospective study included 218 consecutive patients (mean age, 47 years ± 15 [SD]; 125 males) diagnosed with adult-type diffuse LGG. The cohort was classified into IDH wild-type (IDHwt), IDH-mutant with 1p/19q-codeletion (IDHmut-Codel), and IDH-mutant without 1p/19q-codeletion (IDHmut-Noncodel) subtypes. Tumor masks were obtained using deep learning-based segmentation, and qT2FM was calculated from the differences in signal intensity ratios on T2 and FLAIR images. Multivariable logistic regression identified predictors for identifying IDHmut-Noncodel and IDH mutation status. Point-biserial correlations were analyzed between qualitative and quantitative T2FM, and median apparent diffusion coefficient (ADC) value. Diagnostic performance was evaluated with a receiver operating characteristic curve. Results: The IDHmut-Noncodel group had a higher qT2FM (0.37 ± 0.38, p = 0.004) than the IDHmut-Codel (0.24 ± 0.39) and IDHwt groups (0.07 ± 0.62). The qT2FM was the only independent imaging predictor for identifying IDHmut-Noncodel (OR = 3.43, 95% CI: 1.30–9.05, p = 0.01). Independent predictors of IDH mutation were younger age (p < 0.001), frontal lobe location (p = 0.007), cortical involvement (p < 0.001), and higher qT2FM (p = 0.034). The qT2FM significantly correlated with visual T2FM (vT2FM) and median ADC value. Adding qT2FM to vT2FM improved performance in identifying IDHmut-Noncodel (AUC 0.77, 95% CI: 0.70–0.82) and IDH mutation status (AUC 0.77, 95% CI: 0.71–0.83) than each parameter alone. Conclusion: The qT2FM ratio, derived from deep learning-based tumor segmentation, is a valuable predictor for identifying IDH mutation status and the IDHmut-Noncodel subtype in patients with adult-type diffuse LGG. Key Points: QuestionDoes deep-learning-based quantification of the T2-FLAIR mismatch sign provide accurate prediction of IDH-mutant, 1p/19q non-codeleted astrocytomas and enhance identification of IDH mutation status? FindingsQuantifying the T2-FLAIR mismatch sign with a fully automated segmentation tool achieved high accuracy in identifying IDH-mutant, 1p/19q non-codeleted astrocytomas, and enhanced IDH status prediction. Clinical relevanceIntegrating the qT2FM into clinical protocols enhances diagnostic precision and guides treatment strategies, underscoring the role of advanced imaging in neuro-oncology. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Objectives: To investigate the predictive value of the quantitative T2-FLAIR mismatch ratio (qT2FM) with fully automated tumor segmentation in adult-type diffuse lower-grade gliomas (LGGs). Materials and methods: This retrospective study included 218 consecutive patients (mean age, 47 years ± 15 [SD]; 125 males) diagnosed with adult-type diffuse LGG. The cohort was classified into IDH wild-type (IDHwt), IDH-mutant with 1p/19q-codeletion (IDHmut-Codel), and IDH-mutant without 1p/19q-codeletion (IDHmut-Noncodel) subtypes. Tumor masks were obtained using deep learning-based segmentation, and qT2FM was calculated from the differences in signal intensity ratios on T2 and FLAIR images. Multivariable logistic regression identified predictors for identifying IDHmut-Noncodel and IDH mutation status. Point-biserial correlations were analyzed between qualitative and quantitative T2FM, and median apparent diffusion coefficient (ADC) value. Diagnostic performance was evaluated with a receiver operating characteristic curve. Results: The IDHmut-Noncodel group had a higher qT2FM (0.37 ± 0.38, p = 0.004) than the IDHmut-Codel (0.24 ± 0.39) and IDHwt groups (0.07 ± 0.62). The qT2FM was the only independent imaging predictor for identifying IDHmut-Noncodel (OR = 3.43, 95% CI: 1.30–9.05, p = 0.01). Independent predictors of IDH mutation were younger age (p < 0.001), frontal lobe location (p = 0.007), cortical involvement (p < 0.001), and higher qT2FM (p = 0.034). The qT2FM significantly correlated with visual T2FM (vT2FM) and median ADC value. Adding qT2FM to vT2FM improved performance in identifying IDHmut-Noncodel (AUC 0.77, 95% CI: 0.70–0.82) and IDH mutation status (AUC 0.77, 95% CI: 0.71–0.83) than each parameter alone. Conclusion: The qT2FM ratio, derived from deep learning-based tumor segmentation, is a valuable predictor for identifying IDH mutation status and the IDHmut-Noncodel subtype in patients with adult-type diffuse LGG. Key Points: QuestionDoes deep-learning-based quantification of the T2-FLAIR mismatch sign provide accurate prediction of IDH-mutant, 1p/19q non-codeleted astrocytomas and enhance identification of IDH mutation status? FindingsQuantifying the T2-FLAIR mismatch sign with a fully automated segmentation tool achieved high accuracy in identifying IDH-mutant, 1p/19q non-codeleted astrocytomas, and enhanced IDH status prediction. Clinical relevanceIntegrating the qT2FM into clinical protocols enhances diagnostic precision and guides treatment strategies, underscoring the role of advanced imaging in neuro-oncology. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 09387994 |
| DOI: | 10.1007/s00330-025-11475-7 |
