Scholarlink

<p>Intradural extramedullary (IDEM) spinal tumors are commonly managed with laminectomy or laminoplasty. Recent evidence demonstrates that laminoplasty preserves posterior elements, reduces postoperative deformity, and minimizes complications. However, there is no standardized framework guiding the choice of approach. We propose a structured preoperative decision model integrating tumor, patient, and surgical factors into a composite biomechanical risk score. Patients with higher scores may benefit from modified laminectomy or fusion, while low-risk patients are ideal candidates for laminoplasty. Additionally, machine-learning models can augment decision-making by predicting individualized deformity risk using imaging and clinical data. This model aims to reduce variability in surgical planning and optimize patient outcomes.</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

Clinical machine-learning model for laminoplasty vs. laminectomy in IDEM tumors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月24日

查看全文
  • Talha Khan,
  • Ayesha Tariq

摘要

Intradural extramedullary (IDEM) spinal tumors are commonly managed with laminectomy or laminoplasty. Recent evidence demonstrates that laminoplasty preserves posterior elements, reduces postoperative deformity, and minimizes complications. However, there is no standardized framework guiding the choice of approach. We propose a structured preoperative decision model integrating tumor, patient, and surgical factors into a composite biomechanical risk score. Patients with higher scores may benefit from modified laminectomy or fusion, while low-risk patients are ideal candidates for laminoplasty. Additionally, machine-learning models can augment decision-making by predicting individualized deformity risk using imaging and clinical data. This model aims to reduce variability in surgical planning and optimize patient outcomes.