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使用甲状腺软骨细分和放射性特征进行预测建模:可行性研究

Nivea Roy1, K Devaraja1, Prakashini Koteshwara2

  • 1Department of Head and Neck Surgery, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India.

Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India
|July 29, 2025
PubMed
概括
此摘要是机器生成的。

甲状腺软骨的放射性分析显示,对分类与年龄相关的变化充满希望. 这种技术可能有助于喉癌的分期,通过区分正常和瘤透的软骨.

关键词:
头部和部的情况.喉癌是一种喉癌.机器学习 机器学习无线电学 (Radiomics) 是一种无线电学.分段化 分段化 分段化 分段化甲状腺软骨的甲状腺软骨.

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科学领域:

  • 医疗成像医学成像
  • 放射学 放射学是一门学科.
  • 机器学习 机器学习

背景情况:

  • 喉癌的诊断和分期对于有效的治疗至关重要.
  • 甲状腺软骨侵袭意味着晚期癌症,但由于与年龄相关的变化,CT很难评估.
  • 机器学习 (ML) 为分析CT图像提供了一个潜在的解决方案.

研究的目的:

  • 研究CT图像上使用手工细分甲状腺软骨的放射性特征的可行性.
  • 探索ML模型在分类甲状腺软骨与年龄相关的变化的潜力.
  • 评估放射学在区分正常和可能瘤透的软骨方面的实用性.

主要方法:

  • 用3D切片器从39张CT图像中手动细分甲状腺软骨.
  • 使用Slicer Radiomics提取107个放射性特征.
  • 使用Jamovi和MATLAB进行的统计和ML分析.

主要成果:

  • 甲状腺软骨的成功手动细分.
  • 在放射性特征中识别显著的性别和年龄相关差异.
  • 机器学习模型在性别分类方面实现了100%的准确性,在年龄组分类方面达到85.71%.
  • 回归模型在变量转换时显示了更高的准确性.

结论:

  • 甲状腺软骨的放射性分析是一种可行的,有前途的方法来分类与年龄相关的变化.
  • 这种方法有可能通过帮助正常与瘤透软骨的差异化来改善喉癌的分期.