Accurate preoperative clinical assessment of adnexal masses can optimize outcomes by ensuring appropriate and timely surgery. This article addresses whether a new technology, ovarian HistoScanning, has an additional diagnostic value in mathematical models developed for the differential diagnosis of adnexal masses.Transvaginal sonography-based morphological variables were obtained through blinded analysis of archived images in 199 women enrolled in a prospective study to assess the performance of ovarian HistoScanning. Logistic regression (LR) and neural network (NN) models including these variables and clinical and patient data along with the HistoScanning score (HSS) (range, 0-125; based on mathematical algorithms) were developed in a learning set (60% patients). The remaining 40% patients (evaluation set) were used to assess model performance.Of all morphological and clinical variables tested, serum CA-125, presence of a solid component, and HSS were most significant and used to develop the LR model. The NN model included all variables. The novel variable, HSS, offered significant improvement in the LR and NN models' performance. The LR and NN models in an independent evaluation set were found to have area under the receiver operating characteristic curve = 0.97 (95% confidence interval [CI], 94-99) and 0.93 (95% CI, 88-98), sensitivities = 83% (95% CI, 71%-91%) and 80% (95% CI, 67%-89%), and specificities = 98% (95% CI, 89%-99%) and 86% (95% CI, 72%-95%), respectively. In addition, these models showed an improved performance when compared with 3 other existing models (all P < 0.05).This initial report shows a clear benefit of including ovarian HistoScanning into mathematical models used for discriminating benign from malignant ovarian masses. These models may be specifically helpful to the less experienced examiner. Future research should assess performance of these models in prospective clinical trials in different populations.
Preclinical in vitro and in vivo studies suggest that statins could exhibit anticancer properties in ovarian cancer. Similar effects have also been reported in observational studies but their results remain inconsistent and could be impaired by methodological limitations. This study aimed to investigate whether statin use is associated with improved survival in ovarian cancer patients at the Belgian population-level. All patients with invasive epithelial ovarian cancer diagnosed between 2004 and 2012 were identified from the Belgian Cancer Registry. Vital statuses were obtained from the Crossroads Bank for Social Security and ovarian cancer-specific deaths were identified from death certificates provided by regional administrations. Information on cancer treatments and statin use were retrieved from health insurance databases. Statin use was modelled as a time-varying covariate in Cox regression models to calculate adjusted hazards ratios (HR) and 95% confidence intervals (95%CI) for the association between postdiagnostic exposure to statins and overall- or ovarian cancer-specific mortality within three years after diagnosis. Adjustments were made for age at diagnosis, year of diagnosis, comorbidities, cancer stage, and cancer treatments. A total of 5,416 patients with epithelial ovarian cancer met the inclusion criteria. Of these 1,255 (23%) had at least one statin prescription within three years after diagnosis. Postdiagnostic use of statins was associated with a reduced risk of overall mortality (adjusted HR = 0.81, 95%CI:0.72-0.90, p<0.001). In analyses by statin type, this association was only significant for simvastatin (adjusted HR = 0.86, 95%CI:0.74-0.99, p = 0.05) or rosuvastatin (adjusted HR = 0.71, 95%CI:0.55-0.92, p = 0.01). In subgroup analyses by statin prediagnostic use, the protective association for postdiagnostic statin use was only observed in patients who were also using statins before diagnosis (adjusted HR = 0.73, 95%CI:0.64-0.83, p<0.001). Similar results were observed for ovarian cancer-specific mortality. In this large nation-wide cohort of ovarian cancer patients postdiagnostic use of statins was associated with improved survival.
The advantage of adjuvant chemotherapy (ACT) for treating Stage III colon cancer patients is well established and widely accepted. However, many patients with Stage III colon cancer do not receive ACT. Moreover, there are controversies around the effectiveness of ACT for Stage II patients. We investigated the administration of ACT and its association with overall survival in resected Stage II (overall and stratified by low‐/high‐risk) and Stage III colon cancer patients in three European countries including The Netherlands (2009–2014), Belgium (2009–2013) and Sweden (2009–2014). Hazard ratios (HR) for death were obtained by Cox regression models adjusted for potential confounders. A total of 60244 resected colon cancer patients with pathological Stages II and III were analyzed. A small proportion (range 9–24%) of Stage II and over half (range 55–68%) of Stage III patients received ACT. Administration of ACT in Stages II and III tumors decreased with higher age of patients. Administration of ACT was significantly associated with higher overall survival in high‐risk Stage II patients (in The Netherlands (HR; 95%CI = 0.82 (0.67–0.99), Belgium (0.73; 0.59–0.90) and Sweden (0.58; 0.44–0.75)), and in Stage III patients (in The Netherlands (0.47; 0.43–0.50), Belgium (0.46; 0.41–0.50) and Sweden (0.48; 0.43–0.54)). In Stage III, results were consistent across subgroups including elderly patients. Our results show an association of ACT with higher survival among Stage III and high‐risk Stage II colon cancer patients. Further investigations are needed on the selection criteria of Stages II and III colon cancer patients for ACT.
In the European countries, age standardized incidence rates (European standard) for ovarian cancer vary between 7.2 and 19.3/100,000 while mortality rates are ranging between 2.8 and 12.2/100,000.1 In Belgium, ovarian cancer is not as frequent as breast cancer since breast cancer presents with very high incidence rates (similar for other European countries). However, age standardized breast cancer mortality rates in 2008 were less than one fourth of the age standardized incidence rates whereas for ovarian cancer, mortality rates were two thirds of the incidence rates (see figure 1). And, unlike for breast cancer, mortality rates for ovarian cancer were not decreasing over the past years.2 Indeed, ovarian cancer is one of the leading causes of death from gynaecological malignancies.3 This is explained by the fact that in general, ovarian cancer is detected at too advanced stages. Early diagnosis of ovarian cancer is thus the key for improving outcomes for the disease. Medical imaging techniques have revolutionised medicine during the last decades. Ultrasound (US) in particular gives access to vital data in a non-invasive way and is effective for imaging soft tissues of the body. Compared to other medical imaging modalities, US has the following positive attributes: • US is a real-time, easy operation medical imaging technique • US has a non-invasive and radiation free nature • US is relatively low-priced, Hence US has become widely used as a diagnostic technique in general clinical practice. In gynaecology, US is one of the most important and primary diagnostic tools. Its use continues to increase and it is now an essential part of the diagnostic procedure in examining the female pelvis. Indeed, the use of US morphology to characterize adnexal masses and thus diagnose ovarian cancer is well established.4 As a part of patient management, gynaecologists use US morphology to differentiate between malignant and non-malignant ovarian masses that come to their attention. In addition, a large number of indexes and mathematical prediction models that assess the likelihood of malignancy for an ovarian mass have been developed and they all incorporate US. A novel computer-aided technology, HistoScanningTM, makes use of US data for characterising ovarian tissue suspicious of being malignant. In part I of this research work, we investigated whether ovarian HistoScanning could improve the performance of existing prediction models for the differential diagnosis of ovarian masses and we also explored what place could be granted to this new technology in clinical practise. Part I is organised as follows: Chapter 1 concerns the epidemiology of ovarian cancer and discusses the importance of differential diagnosis of ovarian masses. Detailed aims are described in chapter 2. Chapter 3 introduces the general methods used for this work. In chapter 4, two publications that present the results of this work are presented. Finally, a discussion, regarding the results presented in part I, concludes this work in chapter 5.
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