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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.
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]
Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Deep learning-based quantification of T2-FLAIR mismatch sign: extending IDH mutation prediction in adult-type diffuse lower-grade glioma.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22European+Radiology%22&quot;&gt;European Radiology&lt;/searchLink&gt;. Sep2025, Vol. 35 Issue 9, p5193-5202. 10p.
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  Data: 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 &#177; 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 &#177; 0.38, p = 0.004) than the IDHmut-Codel (0.24 &#177; 0.39) and IDHwt groups (0.07 &#177; 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 &lt; 0.001), frontal lobe location (p = 0.007), cortical involvement (p &lt; 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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  Data: &lt;i&gt;Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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