İçeriğe geç
akaturk Akademik ölçüm

Makale detayı · 2020 · article

Identification of IDH and TERTp mutation status using 1 H-MRS in 112 hemispheric diffuse gliomas.

YÖKSİS OpenAlex
Yıl2020
Atıf41OpenAlex
Atıf34Semantic Scholar · 2 etkili
Yüzdelik%82,3
FWCI1,441,00 = dünya ortalaması
Scopus (SJR)Q1
WoS (JCR)Q1

Veri kaynağı ayrımı

  • YÖKSİSYÖKSİS makale kaydı
  • YÖKSİS dergi adıJournal of magnetic resonance imaging : JMRI
  • Katalog eşleşmesi (ISSN)Journal of Magnetic Resonance Imaging
  • OpenAlexOpenAlex zenginleştirmesi (özet, atıf, konular)
  • Semantic Scholaratıf sayısı (OpenAlex ile birleştirilmez)

Özet

OpenAlex İngilizce

BACKGROUND: There is a growing interest in noninvasively defining molecular subsets of hemispheric diffuse gliomas based on the isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutation status, which correspond to distinct tumor entities, and differ in demographics, natural history, treatment response, recurrence, and survival patterns. PURPOSE: H-MRS) at 3T can be used for noninvasive molecular classification of IDH and TERTp mutation-based subsets of gliomas. STUDY TYPE: Retrospective. SUBJECTS: In all, 112 hemispheric diffuse gliomas (70 males/42 females, mean age: 42.1 ± 13.9 years). FIELD STRENGTH/SEQUENCE: H-MRS (repetition time (TR) = 2000 msec, TE = 30 msec, number of signal averages = 192) and routine clinical brain tumor MR protocols were acquired at 3T. ASSESSMENT: H-MRS data were quantified using LCModel software. TERTp and IDH1 or IDH2 (IDH1/2) mutations in the tissue were determined by either minisequencing or Sanger sequencing. STATISTICAL TESTS: Metabolic differences between IDH mutant and IDH wildtype gliomas were assessed by a Mann-Whitney U-test. A Kruskal-Wallis test followed by a Tukey-Kramer test was used to analyze metabolic differences between IDH and TERTp mutational molecular subsets of gliomas. A Spearman rank correlation coefficient was used to assess the correlations of metabolite intensities with the Ki-67 index. Furthermore, machine learning was employed to classify the IDH and TERTp mutational status of gliomas, and the accuracy, sensitivity, and specificity values were estimated. RESULTS: H-MRS classified the presence of an IDH mutation with 88.39% accuracy, 76.92% sensitivity, and 94.52% specificity, and a TERTp mutation within primary IDH wildtype gliomas with 92.59% accuracy, 83.33% sensitivity, and 95.24% specificity. DATA CONCLUSION: H-MRS could be used to identify molecular subsets of hemispheric diffuse gliomas corresponding to IDH and TERTp mutations. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1799-1809.

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

41atıfOpenAlex · cited_by_count (önbellek / veritabanı)

Yerel katalogda bu makaleye atıf yapan 15 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).

  1. 2023 AdvancedMRTechniques for Preoperative Glioma Characterization: Part 2Atıf 64 · OpenAlex
  2. 2023 Advanced MR Techniques for Preoperative Glioma Characterization: Part 2Atıf 64 · OpenAlex
  3. 2024 Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomasAtıf 15 · OpenAlex
  4. 2023 Magnetic resonance spectroscopic correlates of progression free and overall survival in “glioblastoma, IDH-wildtype, WHO grade-4”Atıf 11 · OpenAlex
  5. 2022 Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learningAtıf 9 · OpenAlex
  6. 2022 Identification of metabolic correlates of mild cognitive impairment in Parkinsons disease using magnetic resonance spectroscopic imaging and machine learningAtıf 9 · OpenAlex
  7. 2022 Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learningAtıf 9 · OpenAlex
  8. 2022 Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learningAtıf 9 · OpenAlex
  9. 2025 From pixels to prognosis: Leveraging radiomics and machine learning to predict IDH1 genotype in gliomasAtıf 5 · OpenAlex
  10. 2025 From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomasAtıf 5 · OpenAlex

Yazarlar

4
  1. KORAY ÖZDUMAN ACIBADEM MEHMET ALİ AYDINLAR ÜNİVERSİTESİ 1
  2. ESİN ÖZTÜRK IŞIK BOĞAZİÇİ ÜNİVERSİTESİ 2
  3. ALP DİNÇER ACIBADEM MEHMET ALİ AYDINLAR ÜNİVERSİTESİ 3
  4. AYÇA ERŞEN DANYELİ ACIBADEM MEHMET ALİ AYDINLAR ÜNİVERSİTESİ 4