Objectives: 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. Methods: 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. Results: 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. Conclusion: 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.

Radiomics of high-grade serous ovarian cancer : association between quantitative CT features, residual tumour and disease progression within 12 months / S. Rizzo, F. Botta, S. Raimondi, D. Origgi, V. Buscarino, A. Colarieti, F. Tomao, G. Aletti, V. Zanagnolo, M. Del Grande, N. Colombo, M. Bellomi. - In: EUROPEAN RADIOLOGY. - ISSN 0938-7994. - 28:11(2018 Nov), pp. 4849-4859. [10.1007/s00330-018-5389-z]

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

S. Rizzo
;
S. Raimondi;V. Buscarino;G. Aletti;M. Bellomi
2018-11

Abstract

Objectives: 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. Methods: 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. Results: 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. Conclusion: 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; Disease progression; Ovary; Prognosis; Residual tumour; Radiology, Nuclear Medicine and Imaging
Settore MED/36 - Diagnostica per Immagini e Radioterapia
Settore MED/40 - Ginecologia e Ostetricia
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/622477
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