口腔鳞状细胞癌隐匿性淋巴结转移识别的多变量预测模型的开发与验证
Development and validation of a multivariable prediction model for the identification of occult lymph node metastasis in oral squamous cell carcinoma.
作者信息
Mermod Maxime, Jourdan Eva-Francesca, Gupta Ruta, Bongiovanni Massimo, Tolstonog Genrich, Simon Christian, Clark Jonathan, Monnier Yan
机构信息
Department of Otolaryngology - Head and Neck Surgery, Head and Neck Tumor Laboratory, CHUV and University of Lausanne, Lausanne, Switzerland.
Consultant Statistician for the Head and Neck Tumor Laboratory, CHUV and University of Lausanne, Lausanne, Switzerland.
出版信息
Head Neck. 2020 Aug;42(8):1811-1820. doi: 10.1002/hed.26105. Epub 2020 Feb 14.
BACKGROUND
There have been few recent advances in the identification of occult lymph node metastases (OLNM) in oral squamous cell carcinoma (OSCC). This study aimed to develop, compare, and validate several machine learning models to predict OLNM in clinically N0 (cN0) OSCC.
METHODS
The biomarkers CD31 and PROX1 were combined with relevant histological parameters and evaluated on a training cohort (n = 56) using four different state-of-the-art machine learning models. Next, the optimized models were tested on an external validation cohort (n = 112) of early-stage (T1-2 N0) OSCC.
RESULTS
The random forest (RF) model gave the best overall performance (area under the curve = 0.89 [95% CI = 0.8, 0.98]) and accuracy (0.88 [95% CI = 0.8, 0.93]) while maintaining a negative predictive value >95%.
CONCLUSIONS
We provide a new clinical decision algorithm incorporating risk stratification by an RF model that could significantly improve the management of patients with early-stage OSCC.
背景
口腔鳞状细胞癌（OSCC）隐匿性淋巴结转移（OLNM）的识别近年来进展甚少。本研究旨在开发、比较和验证几种机器学习模型，以预测临床N0（cN0）OSCC中的OLNM。
方法
将生物标志物CD31和PROX1与相关组织学参数相结合，并使用四种不同的先进机器学习模型在一个训练队列（n = 56）上进行评估。接下来，在早期（T1-2 N0）OSCC的外部验证队列（n = 112）上测试优化后的模型。
结果
随机森林（RF）模型表现出最佳的整体性能（曲线下面积 = 0.89 [95% CI = 0.8, 0.98]）和准确性（0.88 [95% CI = 0.8, 0.93]），同时保持阴性预测值>95%。
结论
我们提供了一种新的临床决策算法，该算法纳入了基于RF模型的风险分层，可显著改善早期OSCC患者的管理。
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