Operative tactics in floor of mouth and tongue cancer resection - the importance of imaging and planning.


Journal

The British journal of oral & maxillofacial surgery
ISSN: 1532-1940
Titre abrégé: Br J Oral Maxillofac Surg
Pays: Scotland
ID NLM: 8405235

Informations de publication

Date de publication:
01 2021
Historique:
received: 04 08 2020
accepted: 06 08 2020
pubmed: 5 11 2020
medline: 24 4 2021
entrez: 4 11 2020
Statut: ppublish

Résumé

Complete tumour resection (R0 margin) is an axiom of surgical oncology. Oral cancer ablation is challenging, due to anatomical, functional, and aesthetic considerations. R0 margin is strongly linked to better survival outcomes with great variation in the R0 % across units. This is commonly attributed to disease biology. Without disputing the importance of biological characteristics, we contend that image-based anatomical surgical planning has an important role to play in achieving complete resection. Here, we present our approach utilising cross-sectional imaging, anatomical characteristics and spatial awareness in planning resections for floor of mouth (FOM) and oral tongue cancers. We highlight the challenge of controlling the deep tumour margin lingual to mandible due to anterior vector constraints and emphasise the importance of resecting the genial muscles in a planned fashion and that any rim resection should be obliquely sagittal. In resecting lateral FOM tumours, assessing extension to the parapharyngeal fat is crucial; and mandibular rim resection at a sagittal plane below the mylohyoid line is often required. Assessing the proximity of the contralateral neurovascular pedicle, pre-epiglottic space and hyoid bone are crucial parameters to determine the extent of tongue tumour resection. Our cohort included 173 patients with FOM SCC and 299 patients with tongue SCC. Six patients (3.5%) from the FOM group and eight patients (3%) from the tongue group had involved (R1) margins following surgery. This was associated with local relapse (p<0.05). In conclusion, we demonstrate that image-based planning can aid achieving R0 resections and reduce disease relapse.

Identifiants

pubmed: 33143945
pii: S0266-4356(20)30565-9
doi: 10.1016/j.bjoms.2020.08.118
pii:
doi:

Types de publication

Editorial

Langues

eng

Sous-ensembles de citation

IM

Pagination

5-15

Informations de copyright

Copyright © 2020 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Auteurs

J McMahon (J)

Dept of Maxillofacial Surgery, Queen Elizabeth University Hospital, 1345 Govan Rd, Glasgow, G51 4TF. Electronic address: jeremymcmahon@nhs.net.

P Steele (P)

Dept of Maxillofacial Surgery, Queen Elizabeth University Hospital, 1345 Govan Rd, Glasgow, G51 4TF. Electronic address: petersteele@nhs.net.

P Kyzas (P)

Dept of OMFS/H&N Surgery, Royal Blackburn Teaching Hospital, East Lancashire Hospitals Trust, BB2 3HH. Electronic address: kyzasp@icloud.com.

C Pollard (C)

Dept of Neuroradiology, Institute of Neurosciences, Queen Elizabeth University Hospital, 1345 Govan Road, Glasgow G51 4TF. Electronic address: christopher.pollard@nhs.net.

R Jampana (R)

Dept of Neuroradiology, Institute of Neurosciences, Queen Elizabeth University Hospital, 1345 Govan Road, Glasgow G51 4TF. Electronic address: ravijampana@nhs.net.

C MacIver (C)

Maxillofacial/Head and Neck Unit, SSMC Hospital in partnership with Mayo Clinic, Abu Dhabi. Electronic address: cmaciver@seha.ae.

S Subramaniam (S)

Dept of Maxillofacial Surgery, Queen Elizabeth University Hospital, 1345 Govan Rd, Glasgow, G51 4TF. Electronic address: Shiva.Subramaniam@ggc.scot.nhs.uk.

J Devine (J)

Maxillofacial/Head and Neck Unit, SSMC Hospital in partnership with Mayo Clinic, Abu Dhabi. Electronic address: john.devine20@btinternet.com.

C Wales (C)

Dept of Maxillofacial Surgery, Queen Elizabeth University Hospital, 1345 Govan Rd, Glasgow, G51 4TF. Electronic address: craig.wales@nhs.net.

J McCaul (J)

Dept of Maxillofacial Surgery, Queen Elizabeth University Hospital, 1345 Govan Rd, Glasgow, G51 4TF. Electronic address: jim.mccaul@mac.com.

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Classifications MeSH