MRI-based assessment of the mylohyoid muscle in oral squamous cell carcinoma, a 7-point scoring method.

Head and neck squamous cell carcinoma Multiparametric MRI Otolaryngology Scoring methods Surgical oncology

Journal

European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774

Informations de publication

Date de publication:
29 Aug 2024
Historique:
received: 22 11 2023
accepted: 26 07 2024
revised: 04 07 2024
medline: 31 8 2024
pubmed: 31 8 2024
entrez: 29 8 2024
Statut: aheadofprint

Résumé

To investigate preoperative MRI evaluation of the features of the mylohyoid muscle (MM) predictive of its infiltration in oral squamous cell carcinoma (OSCC) treatment planning, defining the most appropriate sequences to study its deep extension into the floor of the mouth (FOM). We applied a 7-point score to retrospectively evaluate preoperative imaging of patients who underwent surgery for OSCC over 11 years. The results were compared with histopathological findings using Spearman's rank coefficient. Receiver operating characteristic curves were employed to assess the score's ability to predict MM infiltration, determining optimal thresholds for sensitivity, specificity, and predictive values. The Mann-Whitney U-test confirmed that infiltration judgments did not overlap around this threshold. Cohen's K statistical coefficient was used to evaluate the interobserver agreement. Fifty-two patients (mean age 66.4 ± 11.9 years, 36 men) were evaluated. Histopathological examination found MM infiltration in 21% of cases (n = 11), with 90% classified in the highest Score categories. A score > 4 proved to be the best cut-off for predicting the risk of MM infiltration, with a sensitivity of 91% (CI: 0.57-0.99), specificity 61% (CI: 0.45-0.76), PPV 38% (CI: 0.21-0.59), and NPV 96% (CI: 0.78-0.99). At the subsequent single-sequence assessment, the TSE-T2wi had the highest diagnostic accuracy, with sensitivity 90% (CI: 0.57-0.99), specificity 70% (CI: 0.53-0.82), PPV 45% (CI: 0.25-0.67), and NPV 96% (CI: 0.80-0.99). The 7-point score is a promising predictor of safe surgical margins for MM in OSCC treatment, with the particular benefit of T2-weighted sequences. Our scoring system for tumor infiltration of MM, which is easy to use even for less experienced radiologists, allows for uniformity in radiological language, thereby ensuring crucial preoperative information for the surgeon. The relationship of the MM to an oral lesion may impact surgical planning. As the score increases, there is a greater incidence of infiltration in the MM. Our score system improves radiologists' reporting for MM involvement by tumor.

Identifiants

pubmed: 39210160
doi: 10.1007/s00330-024-11016-8
pii: 10.1007/s00330-024-11016-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2024. The Author(s).

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Auteurs

E Radin (E)

Department of Radiology, University of Trieste, Trieste, Italy. erik.radin205@gmail.com.

A V Marcuzzo (AV)

ENT Clinic, Head and Neck Department, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, Trieste, Italy.

J de Groodt (J)

Department of Radiology, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, Trieste, Italy.

F Degrassi (F)

Department of Radiology, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, Trieste, Italy.

L Calderan (L)

Department of Radiology, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, Trieste, Italy.

V Ramella (V)

Department of Plastic, Reconstructive and Aesthetic Surgery, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, University of Trieste, Trieste, Italy.

G Tirelli (G)

ENT Clinic, Head and Neck Department, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, University of Trieste, Trieste, Italy.

M Ukmar (M)

Department of Radiology, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, University of Trieste, Trieste, Italy.

M A Cova (MA)

Department of Radiology, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, University of Trieste, Trieste, Italy.

Classifications MeSH