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European Radiology
Published in: European Radiology 11/2020

01-11-2020 | Magnetic Resonance Imaging | Magnetic Resonance

MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes

Authors: LuoDan Qian, JiaLiang Ren, AiShi Liu, Yang Gao, FenE Hao, Lei Zhao, Hui Wu, GuangMing Niu

Published in: European Radiology | Issue 11/2020

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Abstract

Objective

To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC).

Methods

In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC.

Results

Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types.

Conclusion

The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

Key Points

• The combined radiomics model resulted in a better predictive model than that from a single sequence model.
• The traditional model showed higher classification accuracy than the combined radiomics model.
• Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.
Appendix
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Metadata
Title
MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes
Authors
LuoDan Qian
JiaLiang Ren
AiShi Liu
Yang Gao
FenE Hao
Lei Zhao
Hui Wu
GuangMing Niu
Publication date
01-11-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 11/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06993-5

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