基于放射组学的方法预测口咽和口腔鳞状细胞癌的肿瘤分级和淋巴结状态。
Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach.
机构信息
Department of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, Italy
Department of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, Italy.
出版信息
Anticancer Res. 2020 Jan;40(1):271-280. doi: 10.21873/anticanres.13949.
BACKGROUND/AIM: To investigate whether a radiomic machine learning (ML) approach employing texture-analysis (TA) features extracted from primary tumor lesions (PTLs) is able to predict tumor grade (TG) and nodal status (NS) in patients with oropharyngeal (OP) and oral cavity (OC) squamous-cell carcinoma (SCC).
PATIENTS AND METHODS
Contrast-enhanced CT images of 40 patients with OP and OC SCC were post-processed to extract TA features from PTLs. A feature selection method and different ML algorithms were applied to find the most accurate subset of features to predict TG and NS.
RESULTS
For the prediction of TG, the best accuracy (92.9%) was achieved by Naïve Bayes (NB), bagging of NB and K Nearest Neighbor (KNN). For the prediction of NS, J48, NB, bagging of NB and boosting of J48 overcame the accuracy of 90%.
CONCLUSION
A radiomic ML approach applied to PTLs is able to predict TG and NS in patients with OC and OP SCC.
背景/目的：研究基于纹理分析（TA）特征的放射组学机器学习（ML）方法是否能够预测口咽（OP）和口腔（OC）鳞状细胞癌（SCC）患者的肿瘤分级（TG）和淋巴结状态（NS）。
患者与方法
对 40 例 OP 和 OC SCC 患者的增强 CT 图像进行后处理，以从原发肿瘤病灶（PTL）中提取 TA 特征。应用特征选择方法和不同的 ML 算法，找到预测 TG 和 NS 的最准确特征子集。
结果
对于 TG 的预测，朴素贝叶斯（NB）、NB 集成和 K 最近邻（KNN）的最佳准确率（92.9%）。对于 NS 的预测，J48、NB、NB 集成和 J48 提升均超过 90%的准确率。
结论
应用于 PTL 的放射组学 ML 方法能够预测 OC 和 OP SCC 患者的 TG 和 NS。
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