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.

Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous-cell carcinoma using a radiomic approach / Romeo, V.; Cuocolo, R.; Ricciardi, C.; Ugga, L.; Cocozza, S.; Verde, F.; Stanzione, A.; Napolitano, V.; Russo, D.; Improta, G.; Elefante, A.; Staibano, S.; Brunetti, A.. - In: ANTICANCER RESEARCH. - ISSN 0250-7005. - 40:1(2020), pp. 271-280. [10.21873/anticanres.13949]

Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous-cell carcinoma using a radiomic approach

Romeo V.;Cuocolo R.;Ricciardi C.;Ugga L.;Verde F.;Stanzione A.;Russo D.;Improta G.;Elefante A.;Staibano S.;Brunetti A.;
2020

Abstract

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.
2020
Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous-cell carcinoma using a radiomic approach / Romeo, V.; Cuocolo, R.; Ricciardi, C.; Ugga, L.; Cocozza, S.; Verde, F.; Stanzione, A.; Napolitano, V.; Russo, D.; Improta, G.; Elefante, A.; Staibano, S.; Brunetti, A.. - In: ANTICANCER RESEARCH. - ISSN 0250-7005. - 40:1(2020), pp. 271-280. [10.21873/anticanres.13949]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/793863
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