Characteristics of 114 patients with primary metastatic breast cancer.

<p>HR = hormonal receptor (estrogen and/or progesterone receptor), GGT = gamma-glutamyltransferase, HER2 = human epidermal growth factor 2-receptor</p><p>Characteristics of 114 patients with primary metastatic breast cancer.</p

Univariate and multivariable overall-survival analyses.

<p>^aKaplan—Meier analysis (log-rank test)</p><p>^<b>b</b>Multivariable Cox-regression analysis</p><p>*mean (SD) age: 61.0 (13.4) years; GGT = gamma-glutamyltransferase, HER2 = human epidermal growth factor 2-receptor, OS = overall survival, HR = hazard ratio, 95% CI = 95% Confidence Interval</p><p>Univariate and multivariable overall-survival analyses.</p

Pre-therapeutic GGT serum levels of patients with primary metastatic breast cancer broken down by clinical-pathological parameters.

<p>* One-way ANOVA</p><p>⁺ Student’s T-test</p><p>^⇞ Values are given as mean (standard deviation), GGT = gamma-glutamyltransferase, HER2 = human epidermal growth factor 2-receptor, SD = standard deviation</p><p>Pre-therapeutic GGT serum levels of patients with primary metastatic breast cancer broken down by clinical-pathological parameters.</p

Kaplan-Meier curves for pre-therapeutic GGT levels groups A and B (n = 73, upper line) vs. C and D (n = 41, lower line) and overall survival (<i>P-value</i> for log-rank test <0.041).

<p>Kaplan-Meier curves for pre-therapeutic GGT levels groups A and B (n = 73, upper line) vs. C and D (n = 41, lower line) and overall survival (<i>P-value</i> for log-rank test <0.041).</p

Development of a tool for prediction of ovarian cancer in patients with adnexal masses: Value of plasma fibrinogen

<div><p>Objective</p><p>To develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.</p><p>Study design</p><p>We performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA) was then used to evaluate the clinical net benefit of the prediction model.</p><p>Results</p><p>Ovarian cancer including borderline tumours was found in 241 (26.6%) patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87–0.94). DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.</p><p>Conclusion</p><p>We confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.</p></div

The Prognostic Value of C-Reactive Protein Serum Levels in Patients with Uterine Leiomyosarcoma

<div><p>Objective</p><p>C-reactive protein (CRP) has previously been shown to serve as a prognostic parameter in women with gynecologic malignancies. Due to the lack of valid prognostic markers for uterine leiomyosarcoma (ULMS) this study set out to investigate the value of pre-treatment CRP serum levels as prognostic parameter.</p><p>Methods</p><p>Data of women with ULMS were extracted from databases of three Austrian centres for gynaecologic oncology. Pre-treatment CRP serum levels were measured and correlated with clinico-pathological parameters. Univariate and multivariable survival analyses were performed.</p><p>Results</p><p>In total, 53 patients with ULMS were included into the analysis. Mean (SD) CRP serum level was 3.46 mg/dL (3.96). Solely, an association between pre-treatment CRP serum levels and tumor size (<i>p</i> = 0.04) but no other clinic-pathologic parameter such as tumor stage (<i>p</i> = 0.16), or histological grade (<i>p</i> = 0.07), was observed. Univariate and multivariable survival analyses revealed that CRP serum levels (<i>HR 2</i>.<i>7 [1</i>.<i>1–7</i>.<i>2]</i>, <i>p</i> = 0.037) and tumor stage (<i>HR 6</i>.<i>1 [1</i>.<i>9–19</i>.<i>5]</i>, <i>p</i> = 0.002) were the only independent prognostic factors for overall survival (OS) in patients with ULMS. Patients with high pre-treatment CRP serum levels showed impaired OS compared to women with low levels (5-year-OS rates: 22.6% and 52.3%, <i>p</i> = 0.007).</p><p>Conclusion</p><p>High pre-treatment CRP serum levels were independently associated with impaired prognosis in women with ULMS and might serve as a prognostic parameter in these patients.</p></div

Relative risk of death on the basis of the muscle attenuation (MA) score; optimal cutoff set at 39 as inferred by the Multivariable Fractional Polynomials (mfp) R-package using the optimal non-linear association (MA/100)²+(MA/100)³ with overall survival.

<p>Relative risk of death on the basis of the muscle attenuation (MA) score; optimal cutoff set at 39 as inferred by the Multivariable Fractional Polynomials (mfp) R-package using the optimal non-linear association (MA/100)²+(MA/100)³ with overall survival.</p

Pre-operative characteristics in 906 patients who underwent surgery for suspected adnexal masses.

<p>Pre-operative characteristics in 906 patients who underwent surgery for suspected adnexal masses.</p

ROC-curves representing the accuracy for detection of ovarian malignancy in patients with adnexal masses for CA-125, fibrinogen, the full model (including fibrinogen, CA-125, malignancy-criteria on ultrasound), and the model without fibrinogen.

<p>AUC = area under the curve.</p

Example of an axial computed tomography (CT) image of the third lumbar vertebra region with the analyzed skeletal muscles highlighted in red.

<p>Example of an axial computed tomography (CT) image of the third lumbar vertebra region with the analyzed skeletal muscles highlighted in red.</p