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

Eur Radiol. 2021 Jan;31(1):403-410. doi: 10.1007/s00330-020-07091-2. Epub 2020 Aug 2.

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.

Keywords: Diagnosis; Epithelial ovarian cancer; Machine learning; Magnetic resonance imaging; radiomics.

Publication types

  • Multicenter Study

MeSH terms

  • Carcinoma, Ovarian Epithelial / diagnostic imaging
  • Female
  • Humans
  • Magnetic Resonance Imaging*
  • Ovarian Neoplasms* / diagnostic imaging
  • ROC Curve
  • Retrospective Studies