Department of Otolaryngology - Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
Department of Otolaryngology - Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
Oral Oncol. 2019 May;92:20-25. doi: 10.1016/j.oraloncology.2019.03.011. Epub 2019 Mar 16.
To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (OCSCC) using machine learning. To compare algorithm performance to a model based on tumor depth of invasion (DOI).
Patients who underwent primary tumor extirpation and elective neck dissection from 2007 to 2013 for clinical T1-2N0 OCSCC were identified from the National Cancer Database (NCDB). Multiple machine learning algorithms were developed to predict pathologic nodal metastasis using clinicopathologic data from 782 patients.The algorithm was internally validated using test data from 654 patients in NCDB and was then externally validated using data from 71 patients treated at a single academic institution. Performance was measured using area under the receiver operating characteristic (ROC) curve (AUC). Machine learning and DOI model performance were compared using Delong's test for two correlated ROC curves.
The best classification performance was achieved with a decision forest algorithm (AUC = 0.840). When applied to the single-institution data, the predictive performance of machine learning exceeded that of the DOI model (AUC = 0.657, p = 0.007). Compared to the DOI model, machine learning reduced the number of neck dissections recommended while simultaneously improving sensitivity and specificity.
Machine learning improves prediction of pathologic nodal metastasis in patients with clinical T1-2N0 OCSCC compared to methods based on DOI. Improved predictive algorithms are needed to ensure that patients with occult nodal disease are adequately treated while avoiding the cost and morbidity of neck dissection in patients without pathologic nodal disease.
利用机器学习开发并验证一种预测临床淋巴结阴性口腔鳞状细胞癌（OCSCC）隐匿性淋巴结转移的算法。将算法性能与基于肿瘤侵袭深度（DOI）的模型进行比较。
从国家癌症数据库（NCDB）中确定了 2007 年至 2013 年间因临床 T1-2N0 OCSCC 而行原发肿瘤切除和选择性颈部清扫术的患者。使用来自 782 例患者的临床病理数据，开发了多种机器学习算法以预测病理淋巴结转移。该算法在 NCDB 中 654 例患者的测试数据中进行了内部验证，然后在单一学术机构治疗的 71 例患者的数据中进行了外部验证。使用接收者操作特征曲线（ROC）下的面积（AUC）来衡量性能。使用 DeLong 检验对两个相关 ROC 曲线比较机器学习和 DOI 模型的性能。
决策森林算法的分类性能最佳（AUC=0.840）。当应用于单机构数据时，机器学习的预测性能优于 DOI 模型（AUC=0.657，p=0.007）。与 DOI 模型相比，机器学习减少了推荐的颈部清扫术数量，同时提高了敏感性和特异性。
与基于 DOI 的方法相比，机器学习可提高对临床 T1-2N0 OCSCC 患者病理淋巴结转移的预测。需要改进预测算法，以确保隐匿性淋巴结疾病患者得到充分治疗，同时避免对无病理淋巴结疾病患者进行颈部清扫的成本和发病率。
