对爱尔兰皮肤恶性黑色素瘤中哨戒节阳性的预测性诺图的验证和临床实用性
N Christodoulides1, N Quirke2, R Leon3
1Department of Plastic and Reconstructive Surgery, St James' Hospital, Dublin, Ireland; Trinity St. James's Cancer Institute, Ireland; Royal College of Surgeons in Ireland (RCSI), Ireland.
在一个爱尔兰队列中,三种黑色素瘤诺基图得到了验证. 斯隆·凯特林纪念馆 (MSK) 模型表现最好,这表明在10%的风险值下,有可能减少哨兵淋巴结活检 (SLNB).
科学领域:
- 在瘤学瘤学.
- 皮肤病学 皮肤病学
- 手术病理学手术病理学
背景情况:
- 哨兵淋巴结活检 (SLNB) 对黑色素瘤预后至关重要.
- 预测性名录 (MSK,MIA,LifeMath) 旨在识别需要SLNB的患者.
- 需要在爱尔兰队列中进行验证.
研究的目的:
- 在爱尔兰黑色素瘤队列中比较和验证MSK,MIA和LifeMath的SLNB阳性的预测准确性.
- 评估SLNB程序的临床实用性和潜在减少.
主要方法:
- 从215名黑色素瘤患者的临床和病理数据的回顾性分析.
- 使用三个名ograms计算预测的SLNB阳性.
- 使用歧视,校准,灵敏度,特异性,NPV和决策曲线分析 (DCA) 评估模型性能.
主要成果:
- 16%的患者有积极的SLNB.
- 所有的nomograms都表现出良好的预测能力;MSK表现最好.
- 在10%的风险值时,名图显示出临床益处和减少不必要的活检的潜力,而不是在5%的值时.
结论:
- 在爱尔兰队伍中,MSK,MIA和LifeMath的编程表得到了良好的校准.
- 在MSK nomogram表现出最强的预测性能.
- 使用这些名录,特别是在10%的风险值,可以帮助临床决策,减少不必要的SLNB手术和相关的发病率.
更多相关视频
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
相关概念视频
Reliability and Validity
Predicting Molecular Geometry
Dosage Regimen Designs: Nomograms and Tabulations
Positive Regulator Molecules
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Data Validation
Key parameters for method validation include:
