Publications by authors named "B Delvoux"
(1) : The worldwide endometrial cancer (EC) incidence is rising, amongst others linked to obesity, type 2 diabetes mellitus (T2DM), and metabolic syndrome, possibly due to low-grade adipose tissue inflammation. We studied immune cell infiltration in the endometrium in relation to diagnosis and obesity. (2) : A cohort was created ( = 44) from postmenopausal women, lean ( = 15) and obese ( = 29), with bleeding complaints due to EC ( = 18) or benign pathology ( = 26).
View Article and Find Full Text PDFAbstract: Sex steroids are converted to bioactive metabolites and vice versa by endometrial steroid-metabolising enzymes. Studies indicate that alterations in this metabolism might affect endometrial receptivity. This pilot study determined whether the endometrial formation and inactivation of 17β-oestradiol differed between the supposedly embryo-receptive endometrium and non-receptive endometrium of women undergoing IVF/intracytoplasmic sperm injection (ICSI).
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- The study investigates how steroid concentrations in endometrial tissue and serum relate to gene expression of steroid-metabolizing enzymes to assess endometrial receptivity in IVF patients.
- It involves a case-control design with 40 IVF patients, comparing 20 women who achieved clinical pregnancy to 20 who did not, while controlling for various factors like fertility type and age.
- Results show no overall differences in steroid levels between pregnant and nonpregnant groups, but pregnant women with primary infertility had lower estrone levels and a distinct estrone:androstenedione ratio compared to their nonpregnant counterparts.
Background: Serous ovarian carcinoma is the most common type of ovarian carcinoma. Tumor-associated macrophages (TAMs) promote ovarian cancer progression. Most macrophages are generated by monocyte differentiation.
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- The study aimed to create a machine learning model to predict the histology, stage, and grade of endometrial carcinoma before surgery, which could help improve diagnoses and ensure timely treatment for patients.
- Researchers analyzed a database of preoperative examination data from 329 patients using three algorithms: random forest, logistic regression, and deep neural networks, finding that the random forest provided the best performance in terms of accuracy and area under the curve (AUC).
- The model outperformed doctors' predictions alone, especially when assisted by AI, suggesting that integrating machine learning can enhance diagnostic accuracy for endometrial cancer.