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02.08.2020 | Imaging Informatics and Artificial Intelligence

MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers

verfasst von: Junming Jian, Yong’ai Li, Perry J. Pickhardt, Wei Xia, Zhang He, Rui Zhang, Shuhui Zhao, Xingyu Zhao, Songqi Cai, Jiayi Zhang, Guofu Zhang, Jingxuan Jiang, Yan Zhang, Keying Wang, Guangwu Lin, Feng Feng, Xiaodong Wu, Xin Gao, Jinwei Qiang

Erschienen in: European Radiology | Ausgabe 1/2021

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Abstract

Objectives

Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image–based radiomics model to differentiate between type I and type II EOC.

Methods

In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation.

Results

The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection.

Conclusions

MR image–based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis.

Key Points

• Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively).

• The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS.

• The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.

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Titel: MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers
verfasst von: Junming Jian
Yong’ai Li
Perry J. Pickhardt
Wei Xia
Zhang He
Rui Zhang
Shuhui Zhao
Xingyu Zhao
Songqi Cai
Jiayi Zhang
Guofu Zhang
Jingxuan Jiang
Yan Zhang
Keying Wang
Guangwu Lin
Feng Feng
Xiaodong Wu
Xin Gao
Jinwei Qiang
Publikationsdatum: 02.08.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 1/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07091-2

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MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers