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Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach

Valeria RomeoDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, Italy [email protected]Renato CuocoloDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, ItalyCarlo RicciardiDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, ItalyLorenzo UggaDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, ItalySirio CocozzaDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, ItalyFrancesco VerdeDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, ItalyArnaldo StanzioneDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, ItalyVirginia NapolitanoDepartment of Advanced Biomedical Sciences, Pathology section, Head and Neck research group (GIPaTeC, SIAPEC), University of Naples "Federico II", Naples, ItalyDaniela RussoDepartment of Advanced Biomedical Sciences, Pathology section, Head and Neck research group (GIPaTeC, SIAPEC), University of Naples "Federico II", Naples, ItalyGiovanni ImprotaDepartment of Public Health, University of Naples "Federico II", Naples, ItalyAndrea ElefanteDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, ItalyStefania StaibanoDepartment of Advanced Biomedical Sciences, Pathology section, Head and Neck research group (GIPaTeC, SIAPEC), University of Naples "Federico II", Naples, ItalyArturo BrunettiDepartment of Advanced Biomedical Sciences, Diagnostic Imaging section, University of Naples "Federico II", Naples, Italy

2019en

ABI

Annotatsiya

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.

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DOI: 10.21873/anticanres.13949

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