Stefania Rizzo1, Francesca Botta2, Sara Raimondi3, Daniela Origgi2, Valentina Buscarino4, Anna Colarieti5, Federica Tomao6, Giovanni Aletti7,8, Vanna Zanagnolo7, Maria Del Grande9, Nicoletta Colombo7,10, Massimo Bellomi11,8. 1. Department of Radiology, European Institute of Oncology, Via Ripamonti 435, 20141, Milan, Italy. stefania.rizzo@ieo.it. 2. Medical Physics, European Institute of Oncology, Milan, Italy. 3. Department of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy. 4. Università degli Studi di Milano, Postgraduation School in Radiodiagnostics, Milan, Italy. 5. Dipartimento di Medicina Interna e Specialità mediche, Università degli Studi di Roma La Sapienza, Roma, Italy. 6. Dipartimento di scienze ginecologico ostetriche e scienze urologiche, Università degli Studi di Roma La Sapienza, Roma, Italy. 7. Department of Gynecologic Oncology, European Institute of Oncology, Milan, Italy. 8. Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy. 9. Oncology Institute of Southern Switzerland, San Giovanni Hospital, 6500, Bellinzona, Switzerland. 10. Gynecologic Oncology Program, European Institute of Oncology and University of Milan-Bicocca, Milan, Italy. 11. Department of Radiology, European Institute of Oncology, Via Ripamonti 435, 20141, Milan, Italy.
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.
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.
Authors: David S P Tan; Christian Rothermundt; Karen Thomas; Elizabeth Bancroft; Rosalind Eeles; Susan Shanley; Audrey Ardern-Jones; Andrew Norman; Stanley B Kaye; Martin E Gore Journal: J Clin Oncol Date: 2008-10-27 Impact factor: 44.544
Authors: Andreas du Bois; Alexander Reuss; Eric Pujade-Lauraine; Philipp Harter; Isabelle Ray-Coquard; Jacobus Pfisterer Journal: Cancer Date: 2009-03-15 Impact factor: 6.860
Authors: Francesca De Piano; Valentina Buscarino; Dulia Maresca; Patrick Maisonneuve; Giovanni Aletti; Roberta Lazzari; Andrea Vavassori; Massimo Bellomi; Stefania Rizzo Journal: Radiol Med Date: 2019-08-31 Impact factor: 3.469
Authors: Minkook Seo; Moon Hyung Department Of Radiology Eunpyeong St Mary's Hospital College Of Medicine The Catholic University Of Korea Seoul Republic Of Korea Catholic Smart Imaging Center Eunpyeong St Mary's Hospital College Of Medicine The Catholic University Of Korea Seoul Republic Of Korea Choi; Young Joon Lee; Seung Eun Jung; Sung Eun Rha Journal: Diagn Interv Radiol Date: 2021-07 Impact factor: 2.630