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Item does not contain fulltext OBJECTIVE: To investigate the relationship between obesity and sarcopenia in relation to overall survival (OS) and disease-specific survival (DSS) in high-grade endometrial cancer patients. METHODS: We conducted a retrospective study in women diagnosed with high-grade endometrial cancer (EC) between February 2006 and August 2017 in the Royal Cornwall Hospital who had abdominal computerized tomography (CT)-scan as part of routine staging work-up. Sarcopenia was assessed by measuring psoas-, paraspinal- and abdominal wall muscles on CT and defined by skeletal muscle index ≤41 cm(2) /m(2) . Sarcopenic obesity was defined as sarcopenia combined with body mass index (BMI) ≥30 kg/m(2) . RESULTS: A total of 176 patients with median age of 70 years and median BMI of 29.4 kg/m(2) were included in the study. The majority of patients (38%) had endometrioid type histology. Sarcopenia was not associated with OS (P = 0.951) or DSS (P = 0.545) However, in multivariate analysis, sarcopenic obesity was associated with reduced OS in endometrioid endometrial cancer (EEC) patients (P = 0.048). CONCLUSION: Sarcopenic obesity is associated with OS in high-grade EEC patients, while sarcopenia without obesity is not related to OS or DSS in high-grade EC. In non-endometrioid endometrial cancer, there is no association between sarcopenic obesity and survival.

Background In endometrial cancer (EC), preoperative pelvic MRI is recommended for local staging, while final tumor stage and grade are established by surgery and pathology. MRI‐based radiomic tumor profiling may aid in preoperative risk‐stratification and support clinical treatment decisions in EC. Purpose To develop MRI‐based whole‐volume tumor radiomic signatures for prediction of aggressive EC disease. Study Type Retrospective. Population A total of 138 women with histologically confirmed EC, divided into training (nT = 108) and validation cohorts (nV = 30). Field Strength/Sequence Axial oblique T1‐weighted gradient echo volumetric interpolated breath‐hold examination (VIBE) at 1.5T (71/138 patients) and DIXON VIBE at 3T (67/138 patients) at 2 minutes postcontrast injection. Assessment Primary tumors were manually segmented by two radiologists with 4 and 8 years' of experience. Radiomic tumor features were computed and used for prediction of surgicopathologically‐verified deep (≥50%) myometrial invasion (DMI), lymph node metastases (LNM), advanced stage (FIGO III + IV), nonendometrioid (NE) histology, and high‐grade endometrioid tumors (E3). Corresponding analyses were also conducted using radiomics extracted from the axial oblique image slice depicting the largest tumor area. Statistical Tests Logistic least absolute shrinkage and selection operator (LASSO) was applied for radiomic modeling in the training cohort. The diagnostic performances of the radiomic signatures were evaluated by area under the receiver operating characteristic curve in the training (AUCT) and validation (AUCV) cohorts. Progression‐free survival was assessed using the Kaplan–Meier and Cox proportional hazard model. Results The whole‐tumor radiomic signatures yielded AUCT/AUCV of 0.84/0.76 for predicting DMI, 0.73/0.72 for LNM, 0.71/0.68 for FIGO III + IV, 0.68/0.74 for NE histology, and 0.79/0.63 for high‐grade (E3) tumor. Single‐slice radiomics yielded comparable AUCT but significantly lower AUCV for LNM and FIGO III + IV (both P

BackgroundIn endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy.MethodsProtein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing.ResultsLNM was predicted with area under the curve 0.72–0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of bothFN1andCCND1messenger RNA was related to cancer invasion and mesenchymal phenotype.ConclusionsWe demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.

Background & aims: Visceral adipose tissue (VAT) volume is associated with common lifestyle diseases. Dietary quality, including food matrix and degree of carbohydrate cellularity, as well as the carbohydrate/fat ratio, may influence VAT volume. We aimed to determine the effects of isocaloric diets differing in either “cellularity”, a novel marker of dietary carbohydrate quality, or carbohydrate amount on visceral fat volume and anthropometric measures in adults with obesity. Methods: In a randomized controlled trial of 193 people with obesity/central adiposity, we compared changes in VAT volume after 6 and 12 months, measured by abdominal computed tomography, on three isocaloric eating patterns based on “acellular” carbohydrate sources (e.g., flour-based whole-grain products; comparator arm), “cellular” carbohydrate sources (minimally processed foods with intact cellular structures such as fruits, potatoes/tubers, and rice), or low-carbohydrate high-fat (LCHF) principles. Outcomes were compared by an intention-to-treat (ITT) analysis using constrained linear mixed-effects modelling (cLMM) providing baseline-adjusted change scores and proper missing data handling without imputation. Results: 78 and 57 participants completed 6 and 12 months, respectively, with similar intakes of energy (females: 1820−2060 kcal, males: 2480−2550 kcal) and protein (16–17 energy percent, E%) throughout the intervention, and only modest reductions in energy from baseline. Reported dietary intakes were 42–44, 41–42, and 11–15 E% carbohydrate and 36–38, 37–38, and 66–70 E% fat in the acellular, cellular and LCHF groups, respectively. There were no significant between-group differences in VAT volume after 6 months (cellular vs. acellular [95% CI]: −55 cm³ [−545, 436]; LCHF vs. acellular [95% CI]: −225 cm³ [−703, 253]) or after 12 months (cellular vs. acellular [95% CI]: −122 cm³ [−757, 514]; LCHF vs. acellular [95% CI]: −317 cm³ [−943, 309]). VAT volume decreased significantly within all groups by 14–18% and 12–17% after 6 and 12 months, respectively. Waist circumference was reduced to a significantly greater degree in the LCHF vs. acellular group at 6 months (LCHF vs. acellular [95% CI]: −2.78 cm [−5.54, −0.017]). Conclusions: Despite modest energy restriction, the three isocaloric eating patterns, differing in carbohydrate cellularity and amount, decreased visceral fat volume significantly and to a similar clinically relevant degree. publishedVersion

The purpose of this study was to establish a gene signature that may predict CIN3 regression and that may aid in selecting patients who may safely refrain from conization. Oncomine mRNA data including 398 immune-related genes from 21 lesions with confirmed regression and 28 with persistent CIN3 were compared. L1000 mRNA data from a cervical cancer cohort was available for validation (n = 239). Transcriptomic analyses identified TDO2 (p = 0.004), CCL5 (p &lt, 0.001), CCL3 (p = 0.04), CD38 (p = 0.02), and PRF1 (p = 0.005) as upregulated, and LCK downregulated (p = 0.01) in CIN3 regression as compared to persistent CIN3 lesions. From these, a gene signature predicting CIN3 regression with a sensitivity of 91% (AUC = 0.85) was established. Transcriptomic analyses revealed proliferation as significantly linked to persistent CIN3. Within the cancer cohort, high regression signature score associated with immune activation by Gene Set enrichment Analyses (GSEA) and immune cell infiltration by histopathological evaluation (p &lt, 0.001). Low signature score was associated with poor survival (p = 0.007) and large tumors (p = 0.01). In conclusion, the proposed six-gene signature predicts CIN regression and favorable cervical cancer prognosis and points to common drivers in precursors and cervical cancer lesions.

Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, $$p = 0.06$$ p = 0.06 ). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, $$p=0.08$$ p = 0.08 , $$p=0.60$$ p = 0.60 , and $$p=0.05$$ p = 0.05 ). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer.

Integrative tumor characterization linking radiomic profiles to corresponding gene expression profiles has the potential to identify specific genetic alterations based on non-invasive radiomic profiling in cancer. The aim of this study was to develop and validate a radiomic prognostic index (RPI) based on preoperative magnetic resonance imaging (MRI) and assess possible associations between the RPI and gene expression profiles in endometrial cancer patients. Tumor texture features were extracted from preoperative 2D MRI in 177 endometrial cancer patients. The RPI was developed using least absolute shrinkage and selection operator (LASSO) Cox regression in a study cohort (n = 95) and validated in an MRI validation cohort (n = 82). Transcriptional alterations associated with the RPI were investigated in the study cohort. Potential prognostic markers were further explored for validation in an mRNA validation cohort (n = 161). The RPI included four tumor texture features, and a high RPI was significantly associated with poor disease-specific survival in both the study cohort (p &lt, 0.001) and the MRI validation cohort (p = 0.030). The association between RPI and gene expression profiles revealed 46 significantly differentially expressed genes in patients with a high RPI versus a low RPI (p &lt, 0.001). The most differentially expressed genes, COMP and DMBT1, were significantly associated with disease-specific survival in both the study cohort and the mRNA validation cohort. In conclusion, a high RPI score predicts poor outcome and is associated with specific gene expression profiles in endometrial cancer patients. The promising link between radiomic tumor profiles and molecular alterations may aid in developing refined prognostication and targeted treatment strategies in endometrial cancer.

Objectives To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC). Methods Preoperative whole-body 18F-FDG PET/CT and pelvic MRI were performed in 215 consecutive patients with histologically confirmed EC. PET/CT-based tumor standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and PET-positive lymph nodes (LNs) (SUVmax > 2.5) were analyzed together with the MRI-based tumor volume (VMRI), mean apparent diffusion coefficient (ADCmean), and MRI-positive LN (maximum short-axis diameter ≥ 10 mm). Imaging parameters were explored in relation to surgicopathological stage and tumor grade. Receiver operating characteristic (ROC) curves were generated yielding optimal cutoff values for imaging parameters, and regression analyses were used to assess their diagnostic performance for prediction of LNM and progression-free survival. Results For prediction of LNM, MTV yielded the largest area under the ROC curve (AUC) (AUC = 0.80), whereas VMRI had lower AUC (AUC = 0.72) (p = 0.03). Furthermore, MTV > 27 ml yielded significantly higher specificity (74%, p p MRI > 10 ml (58%, 62%, and 9.7, respectively). MTV > 27 ml also tended to yield higher sensitivity than PET-positive LN (81% vs 50%, p = 0.13). Both VMRI > 10 ml and MTV > 27 ml were significantly associated with reduced progression-free survival. Conclusions Tumor markers from 18F-FDG PET/CT outperform MRI markers for the prediction of LNM. MTV > 27 ml yields a high diagnostic performance for predicting aggressive disease and represents a promising supplement to conventional PET/CT reading in EC. Key Points • Metabolic tumor volume (MTV) outperforms other 18F-FDG PET/CT and MRI markers for preoperative prediction of lymph node metastases (LNM) in endometrial cancer patients. • Using cutoff values for tumor volume for prediction of LNM, MTV > 27 ml yielded higher specificity and accuracy than VMRI> 10 ml. • MTV represents a promising supplement to conventional PET/CT reading for predicting aggressive disease in EC.

Endometrial cancer has a high prevalence among post-menopausal women in developed countries. We aimed to explore whether certain metabolic patterns could be related to the characteristics of aggressive disease and poorer survival among endometrial cancer patients in Western Norway. Patients with endometrial cancer with short survival (n = 20) were matched according to FIGO (International Federation of Gynecology and Obstetrics, 2009 criteria) stage, histology, and grade, with patients with long survival (n = 20). Plasma metabolites were measured on a multiplex system including 183 metabolites, which were subsequently determined using liquid chromatography-mass spectrometry. Partial least square discriminant analysis, together with hierarchical clustering, was used to identify patterns which distinguished short from long survival. A proposed signature of metabolites related to survival was suggested, and a multivariate receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.820–0.965 (p ≤ 0.001). Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in these patients. In a subgroup with preoperative contrast-enhanced computed tomography data, selected metabolites correlated with the estimated abdominal fat distribution parameters. Metabolic signatures may predict prognosis and be promising supplements when evaluating phenotypes and exploring metabolic pathways related to the progression of endometrial cancer. In the future, this may serve as a useful tool in cancer management. publishedVersion

Background Ewing sarcoma (ES) is currently staged using Radiography, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI) and skeletal scintigraphy (bone scan). 18F- fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET-CT) is increasingly used for staging and follow-up, but its role is still under evaluation. Objective To evaluate the added information from 18F-FDG PET-CT studies compared to conventional imaging and to estimate radiation doses received from radiological and nuclear medicine imaging during staging of Ewing sarcoma. Material and methods Sixty-one patients under the age of 30 years (mean 16, range 5–26) were diagnosed with Ewing sarcoma in Norway during the period 2005–2012. Nineteen patients met the inclusion criteria for this population-based study: pre therapeutic 18F-FDG PET-CT and a minimum follow-up of 12 months. Imaging reports, medical records and pathology reports were collected and compared for all patients. Biopsy histology, supplementary imaging and long-term follow-up (median 27 months) were taken as composite gold standard. Results 18F-FDG PET-CT detected more lesions than conventional imaging in four patients (21%) but this did not change planned treatment as they all had extensive metastatic disease. The 18F-FDG PET-CT study was false negative in one patient and showed false positive lesions in three patients (16%). The estimated mean (range) effective total radiation dose was from CT: 7 mSv (2–16), skeletal scintigraphy: 3 mSv (0–5) and 18F-FDG PET-CT: 5 mSv (4–6). Conclusion 18F-FDG PET-CT is useful for staging of Ewing sarcoma and increase detection of metastases. False positive lesions are quite common, emphasizing the need for supplementary imaging or biopsy of suspected FDG positive metastases.

Background Obesity is an important cause of multiple cancer types, amongst which endometrial cancer (EC). The relation between obesity and cancer is complicated and involves alterations in insulin metabolism, response to inflammation and alterations in estradiol metabolism. Visceral obesity is assumed to play the most important role in the first two mechanisms, but its role in estradiol metabolism is unclear. Therefore, this retrospective study explores the relationship of body mass index (BMI), visceral fat volume (VAV) and subcutaneous fat volume (SAV) and serum levels of sex steroids and lipids in patients with endometrial cancer. Methods Thirty-nine postmenopausal EC patients with available BMI, blood serum and Computed Tomography (CT) scans were included. Serum was analyzed for estradiol, dehydroepiandrosterone sulfate (DHEAS), androstenedione, testosterone, cholesterol, triglycerides and high (HDL), low (LDL) and non-high density (NHDL) lipoprotein. VAV and SAV were quantified on abdominal CT scan images. Findings were interpreted using pearson correlation coefficient and linear regression with commonality analysis. Results Serum estradiol is moderately correlated with BMI (r = 0.62) and VAV (r = 0.58) and strongly correlated with SAV (r = 0.74) (p

ObjectiveBetter biomarkers are needed in order to identify patients with endometrial carcinoma at risk of recurrence and who may profit from a more aggressive treatment regimen. Our objective was to explore the applicability of plasma growth differentiation factor 15 (GDF-15) as a marker for recurrent disease, as well as a marker for poor prognosis and lymph node metastases.MethodsEDTA-blood samples were obtained from 235 patients with endometrial cancer before primary surgery. For 36 of these patients, matching blood samples were collected at time of recurrence. Blood samples were also collected from 78 patients with endometrial hyperplasia. Plasma GDF-15 was measured by an enzyme-linked immunosorbent assay (ELISA). Preoperative pelvic MRI scans for 141 patients were investigated in parallel for imaging variables.ResultsPreoperative plasma level of GDF-15 was significantly higher for patients who experienced recurrence (1780 ng/L; 95% CI; 518-9475 ng/L) than for patients who did not develop recurrent disease (1236 ng/L; 95% CI; 307-7030 ng/L) (pConclusionElevated plasma level of GDF-15 is associated with aggressive disease and lymph node metastasis in endometrial carcinoma. GDF-15 may be helpful in indicating recurrent disease.