Predicting mammographic density with linear ultrasound transducers.

BACKGROUND: High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments. METHODS: We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models. RESULTS: Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms. CONCLUSIONS: In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).

Comprehensive characterization of endometriosis patients and disease patterns in a large clinical cohort.

PURPOSE: In many diseases, it is possible to classify a heterogeneous group into subgroups relative to tumor biology, genetic variations, or clinical and pathological features. No such classification is available for endometriosis. In our retrospective case-case analysis we defined subgroups of endometriosis patients relative to the type and location of the endometriosis lesion and relative to basic patient characteristics. METHODS: From June 2013 to July 2017, a total of 1576 patients with endometriosis diagnosed at surgery were included in this study. The patients' history and clinical data were documented using a web-based remote data entry system. To build subgroups, all possible combinations of endometriosis locations/types (peritoneal; ovarian endometriosis; deeply infiltrating endometriosis; adenomyosis) were used. Due to the variation in group sizes, they were combined into five substantial larger groups. RESULTS: Age, pregnancy rate, and live birth rate were identified as characteristics that significantly differed between the five patient groups that were defined. No significant differences were noted in relation to body mass index, length of menstrual cycle, age at menarche, reason for presentation, or educational level. CONCLUSION: This study describes basic patient characteristics in relation to common clinical subgroups in a large clinical cohort of endometriosis patients. Epidemiological information about different clinical groups may be helpful in identifying groups with specific clinical courses, potentially suggesting novel approaches to early detection and to surgical and systemic treatment.

Comparison of methods for isolation and quantification of circulating cell-free DNA from patients with endometriosis.

RESEARCH QUESTION: Which is the optimal extraction method for isolating and quantifying circulating cell-free DNA (ccfDNA) from patients with endometriosis? Endometriosis is a common benign disease, associated with pain, infertility and reduced quality of life. Endometriosis is also a known risk factor for various cancers. Robust biomarkers for early detection and prediction of prognosis, however, are lacking. CcfDNA is an easy to obtain biomarker associated with prognosis of cancer patients and enables non-invasive analysis of somatic mutations. Recently, elevated levels of ccfDNA were detected in patients with endometriosis. DESIGN: Two different ccfDNA extraction methods were compared: Maxwell RSC ccfDNA plasma kit (Maxwell) and QiAamp minElute ccfDNA mini kit (QIAamp). The ccfDNA and circulating mitochondrial DNA (mtDNA) quantities from 34 patients diagnosed with endometriosis were analysed. Fluorometric measurement and quantitative reverse transcription polymerase chain reaction (qRT-PCR) of short and long ALU and mtDNA fragments were used to quantiy ccfDNA. RESULTS: The yield of ccfDNA isolated with the Maxwell method was significantly higher compared with the QIAamp method (P < 0.0001). Integrity of ccfDNA was significantly higher in the QIAamp isolate (P < 0.0001). Recovered mtDNA was not significantly different between both extraction methods used. CONCLUSIONS: The choice of extraction method can significantly influence the ccfDNA output and integrity. Both methods, however, enabled isolation of sufficient ccfDNA for further downstream applications. With this approach, isolation of ccfDNA could enable the non-invasive detection and analysis of somatic mutation within endometriosis tissue.