A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland
Autor: | Ying-Mei Zheng, Jie Li, Guo-Zhang Tang, Dapeng Hao, Hexiang Wang, Cheng Dong, Chuan-Ping Gao, Xuejun Liu, Wenjian Xu |
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Rok vydání: | 2020 |
Předmět: |
medicine.medical_specialty
Training set business.industry General Medicine Benign lymphoepithelial lesion Nomogram medicine.disease 030218 nuclear medicine & medical imaging Parotid gland 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Decision curve analysis Radiomics 030220 oncology & carcinogenesis medicine Radiology Nuclear Medicine and imaging Radiology Differential diagnosis business Neuroradiology |
Zdroj: | European Radiology. 31:2886-2895 |
ISSN: | 1432-1084 0938-7994 |
DOI: | 10.1007/s00330-020-07421-4 |
Popis: | Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland. A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models. Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness. The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process. • Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy. |
Databáze: | OpenAIRE |
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