MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland.
Objectives: Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from...
| Published in: | European Radiology Vol. 31; no. 6; pp. 4042 - 4053 |
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| Main Authors: | , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Jun2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150343616&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150343616 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2021 vid: 31 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150343616 147210596 150343616 NLM33211145 150343616 10.1007/s00330-020-07483-4 NLM33211145 150343616 ppf: 4042 ppct: 11 formats: fmt: @attributes: type: P tig: atl: MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland. aug: au: Zheng, Ying-mei Li, Jian Liu, Song Cui, Jiu-fa Zhan, Jin-feng Pang, Jing Zhou, Rui-zhi Li, Xiao-li Dong, Cheng affil: Health Management Center, The Affiliated Hospital of Qingdao University, No.16, Jiangsu Road, 266000, Qingdao, China sug: subj: Parotid Gland Models, Statistical Retrospective Design Diagnosis, Differential Magnetic Resonance Imaging Scales Human ab: Objectives: Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT.Methods: A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets.Results: Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness.Conclusions: The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process.Key Points: • Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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