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Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Purpose

To evaluate the ability of MRI radiomics to categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer (OEC) patients.

Method

A total of 286 patients with pathologically proven adnexal tumor were retrospectively included in this study. We evaluated diagnostic performance of the signatures derived from MRI radiomics in differentiating (1) between benign adnexal tumors and malignancies and (2) between type I and type II OEC. The least absolute shrinkage and selection operator method was used for radiomics feature selection. Risk scores were calculated from the Lasso model and were used for survival analysis.

Result

For the classification between benign and malignant masses, the MRI radiomics model achieved a high accuracy of 0.90 in the leave-one-out (LOO) cross-validation cohort and an accuracy of 0.87 in the independent validation cohort. For the classification between type I and type II subtypes, our method made a satisfactory classification in the LOO cross-validation cohort (accuracy = 0.93) and in the independent validation cohort (accuracy = 0.84). Low-high-high short-run high gray-level emphasis and low-low-high variance from coronal T2-weighted imaging (T2WI) and eccentricity from axial T1-weighted imaging (T1WI) images had the best performance in two classification tasks. The patients with higher risk scores were more likely to have poor prognosis (hazard ratio = 4.1694, p = 0.001).

Conclusion

Our results suggest radiomics features extracted from MRI are highly correlated with OEC classification and prognosis of patients. MRI radiomics can provide survival estimations with high accuracy.

Key Points

• The MRI radiomics model could achieve a higher accuracy in discriminating benign ovarian diseases from malignancies.

• Low-high-high short-run high gray-level emphasis, low-low-high variance from coronal T2WI, and eccentricity from axial T1WI had the best performance outcomes in various classification tasks.

• The ovarian cancer patients with high-risk scores had poor prognosis.

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Abbreviations

ADC:

Apparent diffusion coefficient

DWI:

Diffusion-weighted magnetic resonance imaging

FIGO:

International Federation of Gynecology and Obstetrics

ISR:

Iterative sparse representation

OEC:

Ovarian epithelial cancer

PACS:

Picture archiving and communication system

SRC:

System sparse representation coefficient

SVM:

Support vector machine

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Funding

This work is financially supported by the Shanghai Emerging Advanced Technology Joint Research Project (SHDC12014130).

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Correspondence to Jinhua Yu or Guofu Zhang.

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Guarantor

The scientific guarantor of this publication is Guofu Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Statistician Yu Bai kindly provided all statistical work for this study.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• Retrospective

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• Performed at one institution

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Zhang, H., Mao, Y., Chen, X. et al. Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. Eur Radiol 29, 3358–3371 (2019). https://doi.org/10.1007/s00330-019-06124-9

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  • DOI: https://doi.org/10.1007/s00330-019-06124-9

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