Deep learning for the automatic detection and segmentation of parotid gland tumors on MRI.
Oral Oncol
; 152: 106796, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38615586
ABSTRACT
OBJECTIVES:
Parotid gland tumors (PGTs) often occur as incidental findings on magnetic resonance images (MRI) that may be overlooked. This study aimed to construct and validate a deep learning model to automatically identify parotid glands (PGs) with a PGT from normal PGs, and in those with a PGT to segment the tumor. MATERIALS ANDMETHODS:
The nnUNet combined with a PG-specific post-processing procedure was used to develop the deep learning model trained on T1-weighed images (T1WI) in 311 patients (180 PGs with tumors and 442 normal PGs) and fat-suppressed (FS)-T2WI in 257 patients (125 PGs with tumors and 389 normal PGs), for detecting and segmenting PGTs with five-fold cross-validation. Additional validation set separated by time, comprising T1WI in 34 and FS-T2WI in 41 patients, was used to validate the model performance. RESULTS ANDCONCLUSION:
To identify PGs with tumors from normal PGs, using combined T1WI and FS-T2WI, the deep learning model achieved an accuracy, sensitivity and specificity of 98.2% (497/506), 100% (119/119) and 97.7% (378/387), respectively, in the cross-validation set and 98.5% (67/68), 100% (20/20) and 97.9% (47/48), respectively, in the validation set. For patients with PGTs, automatic segmentation of PGTs on T1WI and FS-T2WI achieved mean dice coefficients of 86.1% and 84.2%, respectively, in the cross-validation set, and of 85.9% and 81.0%, respectively, in the validation set. The proposed deep learning model may assist the detection and segmentation of PGTs and, by acting as a second pair of eyes, ensure that incidentally detected PGTs on MRI are not missed.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
/
Tipos_de_cancer
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Boca
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Parotídeas
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Imageamento por Ressonância Magnética
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Aprendizado Profundo
Limite:
Adolescent
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Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Oral Oncol
Assunto da revista:
NEOPLASIAS
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China