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The rapid spread of severe acute respiratory syndrome coronavirus 2 infection and its related disease (COVID-19) has required an immediate and coordinate healthcare response to face the worldwide emergency and define strategies to maintain the continuum of care for the non-COVID-19 diseases while protecting patients and healthcare providers. The dimension of the COVID-19 pandemic poses an unprecedented risk especially for the more vulnerable populations. To manage patients with cancer adequately, maintaining the highest quality of care, a definition of value-based priorities is necessary to define which interventions can be safely postponed without affecting patients’ outcome. The European Society for Medical Oncology (ESMO) has endorsed a tiered approach across three different levels of priority (high, medium, low) incorporating information on the value-based prioritisation and clinical cogency of the interventions that can be applied for different disease sites. Patients with gynaecological cancer are at particular risk of COVID-19 complications because of their age and prevalence of comorbidities. The definition of priority level should be based on tumour stage and histology, cancer-related symptoms or complications, aim (curative vs palliative) and magnitude of benefit of the oncological intervention, patients’ general condition and preferences. The decision-making process always needs to consider the disease-specific national and international guidelines and the local healthcare system and social resources, and a changing situation in relation to COVID-19 infection. These recommendations aim to provide guidance for the definition of deferrable and undeferrable interventions during the COVID-19 pandemic for ovarian, endometrial and cervical cancers within the context of the ESMO Clinical Practice Guidelines.

Purpose to evaluate the accuracy of Whole Body MRI including Diffusion-Weighted Imaging sequences (WB DWI MR) in the assessment of sites of disease in epithelial ovarian cancer (EOC), in comparison to CT; to evaluate whether a clinical-radiological score may predict suboptimal cytoreductive surgery. Methods patients with suspected EOC who underwent pre-operative WB DWI MR were included; CT scans were recorded. Data recorded included: age, staging, dates of examinations and surgery; tumour markers; sites of disease at imaging scans and at surgery. For calculation of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of WB DWI MR and CT, surgical evaluation represented the gold standard. The accuracy of WB DWI MR and CT was compared. The association between clinical and radiological criteria with sub-optimal cytoreduction was tested to identify a final model to predict sub-optimal cytoreduction. Results 92 patients were included; 77/92 (83.7 %) were optimally cytoreduced. Sixty-six CT and 92 MR examinations were evaluated. WB DWI MR showed overall higher accuracy than CT in assessing all sites, but it performed significantly better than CT specifically for involvement of mesentery, lumbo-aortic lymph nodes, pelvis, large bowel, sigmoid-rectum. The predicting score for suboptimal cytoreduction included: mesenteric carcinomatosis; mesenteric retraction; large bowel carcinomatosis. Conclusions In pre-operative evaluation of EOC patients, WB DW MRI is accurate for assessment of multiple sites and it is significantly more accurate than CT for specific unresectable sites. In our series, significant sites of disease for suboptimal cytoreduction were mesenteric carcinomatosis, mesenteric retraction and large bowel carcinomatosis.

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. • 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.