个性化动态预测模型,用于患者报告的声音质量在早期的喉癌
Maarten C Dorr1, Eleni-Rosalina Andrinopoulou2, Aniel Sewnaik1
1Department of Otorhinolaryngology and Head and Neck Surgery, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands.
概括
一个新的动态模型预测了患者报告的早期喉癌 (ESGC) 患者的语音质量. 这个工具有助于明智的决策和个性化咨询,以获得更好的声音结果.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 在瘤学瘤学.
- 语音和语言病理学 语言病理学
背景情况:
- 早期的喉癌 (ESGC) 需要治疗,平衡疾病控制与语音保存.
- 准确预测语音质量对于患者咨询和知情决策至关重要.
- 目前的方法缺乏个性化,随着时间的推移对语音质量的动态预测.
研究的目的:
- 开发一个个性化的动态预测模型,用于ESGC患者患者报告的语音质量.
- 提供实时语音质量预测,从诊断到随访.
- 加强患者咨询和治疗决策.
主要方法:
- 在第三级癌症中心对294名ESGC患者进行了纵向队列研究.
- 预期收集语音残疾指数 (VHI) 数据.
- 使用预后因素的混合和联合模型的应用:治疗,年龄,性别,并发症,绩效得分,吸烟,T阶段和前 commissure 参与.
主要成果:
- 队列 (N=294) 的平均年龄为67岁,男性为81.3%.
- 治疗包括横口CO2激光微手术 (57.8%),单个声带辐射 (24.5%),以及局部放射治疗 (17.5%).
- 随着更多的纵向测量,模型性能得到改善;额外的临床/人口统计变量没有提高预测准确性.
结论:
- 成功开发了ESGC中患者报告的语音质量的动态,个性化的预测模型.
- 该模型为患者和临床医生提供数据,以便共享决策.
- 该工具促进了针对治疗后语音结果的个性化咨询.
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