Purpose: The present prediction model was intended to verify whether serum FSH level could be predictive of testis histology in patients with non-obstructive azoospermia (NOA).

Methods: We evaluated two datasets of patients with NOA: the first (San Paolo dataset) comprising 558 patients, 18-63 years old, the second (Procrea dataset) composed by 143 patients, 26-62 years old; bot datasets were combined to obtain a validation set. Multinomial logistic regression was first run with serum FSH and testis volume as independent predictors of testis histology, then, the correctly classified histological subcategories were set as outcome variables of a prediction model in both development and validation sets.

Results: Multinomial logistic regression showed that FSH was a significant predictor of testis histology in 58% of cases, although it was unable to correctly classify cases with focal SCO or maturation arrest (MA). A prediction model was then run with hypospermatogenesis (HYPO) and Sertoli-only syndrome (SCO) as outcome variables of a binary logistic regression. FSH significantly predicted both HYPO and SCO, with a sensitivity of 40.9 and 80.7 and a specificity of 84.3 and 46.8 respectively. The model showed a fair discriminative ability (ROC AUC 0.705 and 0.709 respectively) and was adequately calibrated.

Conclusions: Supported by a robust statistical analysis, we conclude that serum FSH level cannot be considered a prognostic marker of spermatogenic dysfunction in patients with NOA.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6911140PMC
http://dx.doi.org/10.1007/s10815-019-01613-8DOI Listing

Publication Analysis

Top Keywords

prediction model
16
testis histology
16
serum fsh
12
logistic regression
12
non-obstructive azoospermia
8
fsh level
8
patients noa
8
multinomial logistic
8
outcome variables
8
regression fsh
8

Similar Publications

Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

View Article and Find Full Text PDF

Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.

Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.

View Article and Find Full Text PDF

Pharmaceuticals removal from aqueous solution by water hyacinth (Eichhornia crassipes): a comprehensive investigation of kinetics, equilibrium, and thermodynamics.

Environ Sci Pollut Res Int

January 2025

Grupo de Investigación Materiales Con Impacto (Mat&Mpac), Facultad de Ciencias Básicas, Universidad de Medellín, Carrera 87 No. 30-65, 050026, Medellín, Colombia.

This study shows the efficiency of WH-C450, an adsorbent obtained from water hyacinth (WH) biomass, in the removal of sulfamethoxazole (SMX) from aqueous solutions. The process involves calcination of WH at 450 °C to produce an optimal adsorbent material capable of removing up to 73% of SMX and maximum SMX adsorption capacity of 132.23 mg/g.

View Article and Find Full Text PDF

We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.

View Article and Find Full Text PDF

Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!