Adding automated decision-tree models to multiparametric MRI for parotid tumours improves clinical performance.
Eur J Radiol
; 166: 110999, 2023 Sep.
Article
en En
| MEDLINE
| ID: mdl-37499477
ABSTRACT
PURPOSE:
Therapeutic management of parotid gland tumours depends on their histological type. To aid its characterisation, we sought to develop automated decision-tree models based on multiparametric magnetic resonance imaging (MRI) parameters and to evaluate their added diagnostic value compared with morphological sequences.METHODS:
206 MRIs from 206 patients with histologically proven parotid gland tumours were included from January 2009 to January 2018. Multiparametric MRI findings (including parameters derived from diffusion-weighted imaging [DWI] and dynamic contrast-enhanced [DCE]) were used to build predictive classification and regression tree (CART) models for each histological type. All MRIs were read twice first, based on morphological sequence findings only, and second, with the addition of multiparametric sequences and CART findings. The diagnostic performance between these two readings was compared using ROC curves.RESULTS:
Compared to morphological sequences alone, the addition of multiparametric analysis significantly increased the diagnostic performance for all histological types (p < 0.001 to p = 0.011), except for lymphomas, where the increase was not significant (AUC 1.00 vs. 0.99, p = 0.066). ADCmean was the best parameter to identify pleomorphic adenomas, carcinomas and lymphomas with respective cut-offs of 1.292 × 10-3 mm2/s, 1.181 × 10-3 mm2/s and 0.611 × 10-3 mm2/s, respectively. × 10-3 mm2/s. The mean extracellular-extravascular space coefficient was the best parameter to Warthin tumours from the others, with a cut-off of 0.07.CONCLUSIONS:
The addition of decision tree prediction models based on multiparametric sequences improves the non-invasive diagnostic performance of parotid gland tumours. ADC and extracellular-extravascular space coefficient are the two best parameters for decision making.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_financiamento_saude
Asunto principal:
Neoplasias de la Parótida
/
Imágenes de Resonancia Magnética Multiparamétrica
Tipo de estudio:
Diagnostic_studies
/
Health_economic_evaluation
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Eur J Radiol
Año:
2023
Tipo del documento:
Article
País de afiliación:
Francia