期刊
ANTICANCER RESEARCH
卷 40, 期 1, 页码 271-280出版社
INT INST ANTICANCER RESEARCH
DOI: 10.21873/anticanres.13949
关键词
Head and neck squamous cell carcinoma; computed tomography; texture analysis; machine learning
类别
资金
- project Development of an integrated radiomic and phenotypic system for the diagnosis, prognosis and personalization of therapy of head and neck tumors. eMORFORAD technologic platform-Regione Campania
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 Naive 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.
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