Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/22239
Title: Do we have a robust method for preoperative tumour depth assessment for oral cavity tumours with clinically negative necks?
Authors: Cocker, H
Francies, O
Adams, A
Sassoon, I
Schilling, C
Keywords: neoplasms;mouth;squamous cell carcinoma of head and neck;neoplasm staging;neoplasm invasiveness
Issue Date: 25-Dec-2020
Publisher: Elsevier
Citation: International Journal of Oral and Maxillofacial Surgery, 2020
Abstract: © 2020 International Association of Oral and Maxillofacial Surgeons Tumour depth is an important prognostic factor in head and neck cancer and has recently been included in the eighth edition of the Union for International Cancer Control TNM classification of malignant tumours for oral squamous cell carcinoma (OSCC). It is important to appraise the accuracy of depth assessments; however, there is little current evidence in the literature. Accurate depth assessment is particularly pertinent in cT1–T2N0 OSCC where it may influence neck management. A retrospective study was performed at two tertiary referral centres, in which surgically treated patients with cT1–T4N0 OSCC were audited. Preoperative tumour depth assessments from multimodality radiological staging scans were compared with the final histopathological depth. The predictive accuracy of intraoral ultrasound (IOUS), computed tomography (CT), and magnetic resonance imaging (MRI) for tumour depth was evaluated. Accuracy to within 3 mm of the histopathological depth was seen in 56.7% of MRI scans and 57.1% of CT scans. IOUS appeared to have superior prediction, with 78.2% of measurements within 3 mm. Over one third of CT and MRI imaging failed to detect a lesion; IOUS scans detected the lesions in all of these case. In conclusion, the reliability of preoperative imaging assessment of tumour depth should be considered when recommending treatment.
URI: http://bura.brunel.ac.uk/handle/2438/22239
DOI: http://dx.doi.org/10.1016/j.ijom.2020.11.002
ISSN: 0901-5027
Appears in Collections:Dept of Computer Science Embargoed Research Papers

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