European Radiology

, Volume 28, Issue 11, pp 4849–4859 | Cite as

Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months

  • Stefania RizzoEmail author
  • Francesca Botta
  • Sara Raimondi
  • Daniela Origgi
  • Valentina Buscarino
  • Anna Colarieti
  • Federica Tomao
  • Giovanni Aletti
  • Vanna Zanagnolo
  • Maria Del Grande
  • Nicoletta Colombo
  • Massimo Bellomi



To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients.


This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007–23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster’s representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant.


Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41–99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model.


This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12.

Key Points

• No residual tumour (RT) at surgery is the most important prognostic factor in OC.

• Radiomic features related to mass size, randomness and homogeneity were associated with RT.

• Progression of disease within 12 months (PD12) indicates worse prognosis in OC.

• A model including clinical and radiomic features performed better than only-clinical model to predict PD12.


Cancer Ovary Prognosis Residual tumour Disease progression 



Three dimensions


Breast-related cancer antigens


Computed tomography


Digital imaging and communications in medicine


International Federation of Gynecology and Obstetrics


Ovarian cancer


Picture archiving and communication system


Progression of disease within 12 months


Residual tumour


Variance inflation factors


Volume of interest



The English text has been edited by Anne Prudence Collins (Editor and Translator Medical & Scientific Publications).

Data management of patients’ data was performed by Cristiana Fodor (Data Manager; Scientific Direction, European Institute of Oncology, Milan IT).


The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Stefania Rizzo.

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

One of the authors (Sara Raimondi) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all living patients in this study.

Written informed consent was waived by the institutional review board for deceased patients’ at the time of data collection.

Ethical approval

Institutional review board approval was obtained (nr. 440/16-IEO455)


• retrospective

• observational

• performed at one institution

Supplementary material

330_2018_5389_MOESM1_ESM.doc (418 kb)
ESM 1 (DOC 418 kb)


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Stefania Rizzo
    • 1
    Email author
  • Francesca Botta
    • 2
  • Sara Raimondi
    • 3
  • Daniela Origgi
    • 2
  • Valentina Buscarino
    • 4
  • Anna Colarieti
    • 5
  • Federica Tomao
    • 6
  • Giovanni Aletti
    • 7
    • 8
  • Vanna Zanagnolo
    • 7
  • Maria Del Grande
    • 9
  • Nicoletta Colombo
    • 7
    • 10
  • Massimo Bellomi
    • 1
    • 8
  1. 1.Department of RadiologyEuropean Institute of OncologyMilanItaly
  2. 2.Medical PhysicsEuropean Institute of OncologyMilanItaly
  3. 3.Department of Epidemiology and BiostatisticsEuropean Institute of OncologyMilanItaly
  4. 4.Università degli Studi di Milano, Postgraduation School in RadiodiagnosticsMilanItaly
  5. 5.Dipartimento di Medicina Interna e Specialità medicheUniversità degli Studi di Roma La SapienzaRomaItaly
  6. 6.Dipartimento di scienze ginecologico ostetriche e scienze urologicheUniversità degli Studi di Roma La SapienzaRomaItaly
  7. 7.Department of Gynecologic OncologyEuropean Institute of OncologyMilanItaly
  8. 8.Department of Oncology and Hemato-OncologyUniversità degli Studi di MilanoMilanItaly
  9. 9.Oncology Institute of Southern SwitzerlandSan Giovanni HospitalBellinzonaSwitzerland
  10. 10.Gynecologic Oncology ProgramEuropean Institute of Oncology and University of Milan-BicoccaMilanItaly

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