Rationale and Objectives
To establish a radiomics nomogram for detecting deep myometrial invasion (DMI) in
early stage endometrioid adenocarcinoma (EAC).
Materials and Methods
A total of 266 patients with stage I EAC were divided into training (n = 185) and test groups (n = 81). Logistic regression were used to identify clinical predictors. Radiomics features
were extracted and selected from multiparameter MR images. The important clinical
factors and radiomics features were integrated into a nomogram. A receiver operating
characteristic curve was used to evaluate the nomogram. Two radiologists evaluated
MR images with or without the help of the nomogram to detect DMI. The clinical benefit
of using the nomogram was evaluated by decision curve analysis (DCA) and by calculating
net reclassification index (NRI) and integrated discrimination index (IDI).
Results
Age and CA125 were independent clinical predictors. The area under the curves of the
clinical parameters, radiomics signature and nomogram in evaluating DMI were 0.744,
0.869 and 0.883, respectively. The accuracies of the two radiologists increased from
79.0% and 80.2% to 90.1% and 92.5% when they used the nomogram. The NRI of the two
radiologists were 0.262 and 0.318, and the IDI were 0.322 and 0.405. According to
DCA, the nomogram showed a higher net benefit than the radiomics signature or unaided
radiologists. Cross-validation showed the outcome of radiomics analysis may not be
influenced by changes in field strength.
Conclusion
The radiomics nomogram based on radiomics features and clinical factors can help radiologists
evaluate DMI and improve their accuracy in predicting DMI in early stage EAC.
Key Words
Abbreviations:
endometrial carcinoma ((EC)), International Federation of Gynecology and Obstetrics ((FIGO)), endometrioid adenocarcinoma ((EAC)), deep myometrial invasion ((DMI)), magnetic resonance imaging ((MRI)), dynamic contrast-enhanced MRI ((DCE-MRI)), T2-weighted image ((T2WI)), diffusion weighted imaging ((DWI)), apparent diffusion coefficient ((ADC)), late contrast-enhanced T1-weighted imaging ((LCE-T1WI)), region of interest ((ROI)), intraclass correlation coefficient ((ICC)), least absolute shrinkage and selection operator ((LASSO)), decision curve analysis ((DCA)), net reclassification index ((NRI)), integrated discrimination index ((IDI)), receiver operating characteristic ((ROC)), areas under the curves ((AUCs)), maximal correlation coefficient ((MCC))To read this article in full you will need to make a payment
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Article info
Publication history
Published online: June 28, 2022
Accepted:
May 28,
2022
Received in revised form:
May 23,
2022
Received:
April 15,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
