Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma

In this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion o...

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Veröffentlicht in:	PloS one 2020-05, Vol.15 (5), p.e0232639
Hauptverfasser:	Keek, Simon, Sanduleanu, Sebastian, Wesseling, Frederik, de Roest, Reinout, van den Brekel, Michiel, van der Heijden, Martijn, Vens, Conchita, Giuseppina, Calareso, Licitra, Lisa, Scheckenbach, Kathrin, Vergeer, Marije, Leemans, C. René, Brakenhoff, Ruud H, Nauta, Irene, Cavalieri, Stefano, Woodruff, Henry C, Poli, Tito, Leijenaar, Ralph, Hoebers, Frank, Lambin, Philippe, Hsieh, Jason Chia-Hsun
Format:	Artikel
Sprache:	eng
Schlagworte:	Big data Cancer Cancer metastasis Cancer recurrence Carcinoma CAT scans Cetuximab Chemotherapy Development and progression Diagnosis Head and neck cancer Machine learning Methods Operating systems (Software) Patient outcomes Pharyngeal cancer Radiotherapy Risk factors Squamous cell carcinoma Time Tomography Tumors
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