A CT-based radiomics nomogram for distinguishing between malignant and benign Bosniak IIF masses: a two-centre study.
Clin Radiol
; 78(8): 590-600, 2023 08.
Article
en En
| MEDLINE
| ID: mdl-37258333
ABSTRACT
AIM:
To establish and assess a computed tomography (CT)-based radiomics nomogram for identifying malignant and benign Bosniak IIF masses. MATERIALS ANDMETHODS:
In total, 150 patients with Bosniak IIF masses were separated into a training set (n=106) and a test set (n=44) in a ratio of 73. A radiomics signature was calculated based on extracted features from the three phases of CT images. A clinical model was constructed based on clinical characteristics and CT features, and a nomogram incorporating the radiomics signature and independent clinical variables was established. The calibration ability, discrimination accuracy, and clinical value of the nomogram model were assessed.RESULTS:
Twelve features derived from CT images were applied to establish the radiomics signature. The performance levels of three machine-learning models were improved by adding the synthetic minority oversampling technique algorithm. The optimised machine learning model was a combination of the minimum redundancy maximum relevance-least absolute shrinkage and selection operator feature screening method + logistic regression classifier + synthetic minority oversampling technique algorithm, which demonstrated excellent identification ability on the test set (area under the curve [AUC], 0.970; 95% confidence interval [CI], 0.940-1.000). The nomogram model displayed outstanding discrimination ability on the test set (AUC, 0.972; 95% CI, 0.942-1.000).CONCLUSIONS:
The CT-based radiomics nomogram was useful for discriminating between malignant and benign Bosniak IIF masses, which improved the precision of preoperative diagnosis.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Nomogramas
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Clin Radiol
Año:
2023
Tipo del documento:
Article
País de afiliación:
China