Summary
Summary
Revista Brasileira de Ginecologia e Obstetrícia. 2024;46:e-FPS08
Summary
Revista Brasileira de Ginecologia e Obstetrícia. 2024;46:e-rbgo5
This study aims to correlate pelvic ultrasound with female puberty and evaluate the usual ultrasound parameters as diagnostic tests for the onset of puberty and, in particular, a less studied parameter: the Doppler evaluation of the uterine arteries.
Cross-sectional study with girls aged from one to less than eighteen years old, with normal pubertal development, who underwent pelvic ultrasound examination from November 2020 to December 2021. The presence of thelarche was the clinical criterion to distinguish pubescent from non-pubescent girls. The sonographic parameters were evaluated using the ROC curve and the cutoff point defined through the Youden index (J).
60 girls were included in the study. Uterine volume ≥ 2.45mL had a sensitivity of 93%, specificity of 90%, PPV of 90%, NPV of 93% and accuracy of 91% (AUC 0.972) for predicting the onset of puberty. Mean ovarian volume ≥ 1.48mL had a sensitivity of 96%, specificity of 90%, PPV of 90%, NPV of 97% and accuracy of 93% (AUC 0.966). Mean PI ≤ 2.75 had 100% sensitivity, 48% specificity, 62% PPV, 100% NPV and 72% accuracy (AUC 0.756) for predicting the onset of puberty.
Pelvic ultrasound proved to be an excellent tool for female pubertal assessment and uterine and ovarian volume, the best ultrasound parameters for detecting the onset of puberty. The PI of the uterine arteries, in this study, although useful in the pubertal evaluation, showed lower accuracy in relation to the uterine and ovarian volume.
Summary
Revista Brasileira de Ginecologia e Obstetrícia. 2024;46:e-rbgo39i
This study aims to create a new screening for preterm birth < 34 weeks after gestation with a cervical length (CL) ≤ 30 mm, based on clinical, demographic, and sonographic characteristics.
This is a post hoc analysis of a randomized clinical trial (RCT), which included pregnancies, in middle-gestation, screened with transvaginal ultrasound. After observing inclusion criteria, the patient was invited to compare pessary plus progesterone (PP) versus progesterone only (P) (1:1). The objective was to determine which variables were associated with severe preterm birth using logistic regression (LR). The area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV) were calculated for both groups after applying LR, with a false positive rate (FPR) set at 10%.
The RCT included 936 patients, 475 in PP and 461 in P. The LR selected: ethnics white, absence of previous curettage, previous preterm birth, singleton gestation, precocious identification of short cervix, CL < 14.7 mm, CL in curve > 21.0 mm. The AUC (CI95%), sensitivity, specificity, PPV, and PNV, with 10% of FPR, were respectively 0.978 (0.961-0.995), 83.4%, 98.1%, 83.4% and 98.1% for PP < 34 weeks; and 0.765 (0.665-0.864), 38.7%, 92.1%, 26.1% and 95.4%, for P < 28 weeks.
Logistic regression can be effective to screen preterm birth < 34 weeks in patients in the PP Group and all pregnancies with CL ≤ 30 mm.
Summary
Revista Brasileira de Ginecologia e Obstetrícia. 2024;46:e-rbgo50
To determine the relationship between early age at menarche, late age at menopause with specific subtypes of breast cancer (BC).
A literature search was conducted in Embase, Lilacs, PubMed, Scopus, and Scielo databases, following the Joanna Briggs Institute scoping review protocol and answering the question “How early age at menarche or late age at menopause are related to different breast cancer subtypes?”.
A number of 4,003 studies were identified, of which 17 were selected. Most of the included articles found a clear relationship between early menarche, late menopause and some subtypes of BC, mainly, PR+, ER+, luminal, and HER-2 tumors. However, some studies have found a contradictory relationship and one study didn’t find any relationship between them.
A relationship between early age at menarche and advanced age at menopause was observed with some subtypes of breast cancer, since other factors must be considered in its understanding.
Summary
Revista Brasileira de Ginecologia e Obstetrícia. 2024;46:e-rbgo68
To evaluate the association between the dietary patterns (DPs) of pregnant women with GDM (gestational diabetes mellitus) and the birth weight (BW) of the infants.
Cross-sectional study with 187 adult pregnant women with GDM attended at a maternity in Rio de Janeiro from 2011 to 2014. Dietary intake was assessed in the third trimester using a semiquantitative food frequency questionnaire (FFQ). The outcomes were BW and weight adequacy for gestational age (GA). Reduced Rank Regression (RRR) was used to explain the following response variables: density of carbohydrates, fibres, and saturated fatty acids. Statistical analyzes included multinomial logistic regression models.
The mean BW was 3261.9 (± 424.5) g. Three DPs were identified, with DP 3 (high consumption of refined carbohydrates, fast foods/snacks, whole milk, sugars/sweets, and soft drinks and low consumption of beans, vegetables, and low-fat milk and derivatives) being the main pattern, explaining 48.37% of the response variables. In the multinomial logistic regression analysis no statistically significant association was found between the tertiles of DPs and BW or the adequacy of weight for GA, even after adjustments of confounding covariates.
No significant associations were found between maternal DPs in the third trimester of pregnancy and infant BW or adequacy of weight for GA.
Summary
Revista Brasileira de Ginecologia e Obstetrícia. 2024;46:e-rbgo71
To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography.
Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms “Artificial Intelligence,” “Mammography,” and their respective MeSH terms. We filtered publications from the past ten years (2014 – 2024) and in English.
A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work.
The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity.
It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone.
AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals.