A nomogram for predicting the risk of neck node metastasis in oral cavity carcinoma using acoustic radiation force impulse imaging (ARFI).


Journal

Oral oncology
ISSN: 1879-0593
Titre abrégé: Oral Oncol
Pays: England
ID NLM: 9709118

Informations de publication

Date de publication:
07 2021
Historique:
received: 17 12 2020
revised: 10 04 2021
accepted: 12 04 2021
pubmed: 2 5 2021
medline: 1 2 2022
entrez: 1 5 2021
Statut: ppublish

Résumé

The study was conducted to assess the accuracy power of Acoustic radiation force impulse imaging (ARFI) and generate a nomogram using ultrasound and ARFI to predict malignant cervical lymph nodes in head and neck squamous cell carcinoma. 374 cervical lymph nodes from 67 patients were evaluated. The B-mode ultrasonography and the elastography findings were compared with the final histopathological diagnosis. Radiological variables were used to construct nomogram and clinical utility of the nomogram was cross-validated. In univariate analysis, status of the hilum, Long Axis Diameter, Short axis diameter, colour virtual touch imaging grade (VTI) and shear wave velocity were significant in predicting metastasis in the cervical lymph nodes. In multivariable analysis, it was found that predominance of red over yellow area on colour VTI was significantly associated with lymph node metastasis. A multiple logistic regression performed to ascertain the effects of on the likelihood that patients had lymph node metastasis on histopathology was statistically significant, χ2(10) = 44.96, p < 0.001. The model was able to correctly classify 93.28% of cases and the concordance index (c-index) was estimated to be 0.8773. A nomogram was thus established to predict metastasis in cervical lymph nodes. ARFI increases the diagnostic accuracy of conventional USG in predicting metastatic lymph nodes in HNSCC. Adding the constructed nomogram to the conventional diagnostic pathway can provide an alternative option to frozen section and FNAC.

Sections du résumé

BACKGROUND
The study was conducted to assess the accuracy power of Acoustic radiation force impulse imaging (ARFI) and generate a nomogram using ultrasound and ARFI to predict malignant cervical lymph nodes in head and neck squamous cell carcinoma.
MATERIAL AND METHODS
374 cervical lymph nodes from 67 patients were evaluated. The B-mode ultrasonography and the elastography findings were compared with the final histopathological diagnosis. Radiological variables were used to construct nomogram and clinical utility of the nomogram was cross-validated.
RESULTS
In univariate analysis, status of the hilum, Long Axis Diameter, Short axis diameter, colour virtual touch imaging grade (VTI) and shear wave velocity were significant in predicting metastasis in the cervical lymph nodes. In multivariable analysis, it was found that predominance of red over yellow area on colour VTI was significantly associated with lymph node metastasis. A multiple logistic regression performed to ascertain the effects of on the likelihood that patients had lymph node metastasis on histopathology was statistically significant, χ2(10) = 44.96, p < 0.001. The model was able to correctly classify 93.28% of cases and the concordance index (c-index) was estimated to be 0.8773. A nomogram was thus established to predict metastasis in cervical lymph nodes.
CONCLUSIONS
ARFI increases the diagnostic accuracy of conventional USG in predicting metastatic lymph nodes in HNSCC. Adding the constructed nomogram to the conventional diagnostic pathway can provide an alternative option to frozen section and FNAC.

Identifiants

pubmed: 33932875
pii: S1368-8375(21)00134-2
doi: 10.1016/j.oraloncology.2021.105311
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105311

Informations de copyright

Copyright © 2021 Elsevier Ltd. All rights reserved.

Auteurs

Neetesh Kumar Sinha (NK)

Department of Surgical Oncology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Pavneet Singh Kohli (PS)

Department of Surgical Oncology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Krishnan Nagarajan (K)

Additional Professor and Head, Department of Radiodiagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Debasis Gochhait (D)

Department of Pathology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Sachit Ganapathy (S)

Department of Biostatistics, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Elango Swamiappan (E)

Department of Radiodiagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Sivaraman Ganesan (S)

Department of ENT, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.

Prasanth Penumadu (P)

Department of Surgical Oncology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India. Electronic address: drpenumadu@gmail.com.

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