Scholarlink

Objectives <p>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).</p> Materials and methods <p>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.</p> Results <p>The IDHmut-Noncodel group had a higher qT2FM (0.37 ± 0.38, <i>p</i> = 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, <i>p</i> = 0.01). Independent predictors of IDH mutation were younger age (<i>p</i> &lt; 0.001), frontal lobe location (<i>p</i> = 0.007), cortical involvement (<i>p</i> &lt; 0.001), and higher qT2FM (<i>p</i> = 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.</p> Conclusion <p>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.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Does 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</i>?</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>Quantifying 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</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Integrating the qT2FM into clinical protocols enhances diagnostic precision and guides treatment strategies, underscoring the role of advanced imaging in neuro-oncology</i>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning-based quantification of T2-FLAIR mismatch sign: extending IDH mutation prediction in adult-type diffuse lower-grade glioma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月7日

查看全文
  • Young Hun Jeon,
  • Kyu Sung Choi,
  • Kyung Hoon Lee,
  • Seong Yun Jeong,
  • Ji Ye Lee,
  • Taehyuk Ham,
  • Inpyeong Hwang,
  • Roh-Eul Yoo,
  • Koung Mi Kang,
  • Tae Jin Yun,
  • Seung Hong Choi,
  • Ji-hoon Kim,
  • Chul-Ho Sohn

摘要

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

Question Does 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?

Findings Quantifying 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 relevance Integrating the qT2FM into clinical protocols enhances diagnostic precision and guides treatment strategies, underscoring the role of advanced imaging in neuro-oncology.