Scholarlink

<p>Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. The aim of this study is to share our experience using DNA methylation classification in daily routine practice, illustrated through clinical cases. We employed a classification system to evaluate discrepancies between histo-molecular and DNA methylation diagnoses, with a specific focus on adult versus pediatric CNS tumors. In our study, we observed that 40% of cases fell into Class I, 47% into Class II, and 13% into Class III among the “matched cases” (≥ 0.84). In other words, DNA methylation classification confirmed morphological diagnoses in 63% of adult and 23% of pediatric cases. Refinement of diagnosis was particularly evident in the pediatric population (65% vs. 21% for the adult population, <i>p</i> = 0.006). Additionally, we discussed cases classified with low calibrated scores. In conclusion, our study confirms that DNA methylation classification provides significant added-value for CNS tumors diagnosis, particularly in pediatric cases.</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

Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月22日

查看全文
  • Laetitia Lebrun,
  • Nathalie Gilis,
  • Manon Dausort,
  • Chloé Gillard,
  • Stefan Rusu,
  • Karim Slimani,
  • Olivier De Witte,
  • Fabienne Escande,
  • Florence Lefranc,
  • Nicky D’Haene,
  • Claude Alain Maurage,
  • Isabelle Salmon

摘要

Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. The aim of this study is to share our experience using DNA methylation classification in daily routine practice, illustrated through clinical cases. We employed a classification system to evaluate discrepancies between histo-molecular and DNA methylation diagnoses, with a specific focus on adult versus pediatric CNS tumors. In our study, we observed that 40% of cases fell into Class I, 47% into Class II, and 13% into Class III among the “matched cases” (≥ 0.84). In other words, DNA methylation classification confirmed morphological diagnoses in 63% of adult and 23% of pediatric cases. Refinement of diagnosis was particularly evident in the pediatric population (65% vs. 21% for the adult population, p = 0.006). Additionally, we discussed cases classified with low calibrated scores. In conclusion, our study confirms that DNA methylation classification provides significant added-value for CNS tumors diagnosis, particularly in pediatric cases.