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Tytuł pozycji:

Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery.

Tytuł:
Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery.
Autorzy:
Qin H; Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China.
Que Q; Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China.
Lin P; Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China.
Li X; Department of GE Healthcare Global Research, GE Healthcare, Shanghai, 201203, People's Republic of China.
Wang XR; Department of GE Healthcare Global Research, GE Healthcare, Shanghai, 201203, People's Republic of China.
He Y; Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China.
Chen JQ; Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China. .
Yang H; Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Guangxi Zhuang Autonomous Region, Nanning, People's Republic of China. .
Źródło:
La Radiologia medica [Radiol Med] 2021 Oct; Vol. 126 (10), pp. 1312-1327. Date of Electronic Publication: 2021 Jul 08.
Typ publikacji:
Comparative Study; Journal Article
Język:
English
Imprint Name(s):
Publication: Milan : Springer Milan
Original Publication: Torino [etc.] Minerva medica.
MeSH Terms:
Lymphatic Metastasis/*diagnostic imaging
Magnetic Resonance Imaging/*methods
Thyroid Cancer, Papillary/*diagnostic imaging
Thyroid Neoplasms/*diagnostic imaging
Adult ; Decision Trees ; Female ; Humans ; Logistic Models ; Lymphatic Metastasis/pathology ; Male ; Models, Statistical ; Neck/diagnostic imaging ; Preoperative Care ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity ; Statistics, Nonparametric ; Thyroid Cancer, Papillary/secondary ; Thyroid Neoplasms/pathology
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Grant Information:
2017GXNSFAA198253 Guangxi National Nature Science Foundation; NSFC81860319 National Natural Science Foundation of China; NSFC81960329 National Natural Science Foundation of China; GuiKeAB17195020 Guangxi Science and Technology Program; 1598011-4 Guangxi Scientific Research and Technology Development Plan
Contributed Indexing:
Keywords: Cervical lymph node metastases; Magnetic resonance imaging; Papillary thyroid cancer
Entry Date(s):
Date Created: 20210708 Date Completed: 20211020 Latest Revision: 20211020
Update Code:
20240105
DOI:
10.1007/s11547-021-01393-1
PMID:
34236572
Czasopismo naukowe
Purpose: To compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively.
Materials and Methods: A retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models.
Results: The area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC.
Conclusion: Our combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.
(© 2021. Italian Society of Medical Radiology.)

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