Biological Maturity Status in Elite Youth Soccer Players: A Comparison of Pragmatic Diagnostics With Magnetic Resonance Imaging

The influence of biological maturity status (BMS) on talent identification and development within elite youth soccer is critically debated. During adolescence, maturity-related performance differences within the same age group may cause greater chances of being selected for early maturing players. Therefore, coaches need to consider players' BMS. While standard methods for assessing BMS in adolescents are expensive and time-consuming imaging techniques (i.e., X-ray and MRI), there also exist more pragmatic procedures. This study aimed to evaluate commonly used methods to assess BMS within a highly selected sample of youth soccer players. A total of N = 63 elite male soccer players (U12 and U14) within the German Soccer Association's talent promotion program completed a test battery assessing BMS outcomes. Utilizing MRI diagnostics, players' skeletal age (SAMRI) was determined by radiologists and served as the reference method. Further commonly used methods included skeletal age measured by an ultrasound device (SAUS), the maturity offset (MOMIR), and the percentage of adult height (PAHKR). The relation of these alternative BMS outcomes to SAMRI was examined using different perspectives: performing bivariate correlation analyses (1), modeling BMS as a latent variable (BMSlat) based on the multiple alternative diagnostics (2), and investigating individual differences in agreement (3). (1) Correlations of SAMRI and the further BMS variables ranked from r = 0.80 to r = 0.84 for the total sample and were lower for U12 (0.56 ≤ r ≤ 0.66), and U14 (0.61 ≤ r ≤ 0.74) (2). The latent structural equation modeling (SEM) (R 2 = 51%) revealed a significant influence on BMSlat for MOMIR (β = 0.51, p <0.05). The additional contribution of PAHKR (β = 0.27, p = 0.06) and SAUS (β = -0.03, p = 0.90) was rather small (3). The investigation of individual differences between the reference method and alternative diagnostics indicated a significant bias for MOMIR (p <0.01). The results support the use of economical and time-efficient methods for assessing BMS within elite youth soccer. Bivariate correlation analyses as well as the multivariate latent variable approach highlight the measures' usefulness. However, the observed individual level differences for some of the utilized procedures led to the recommendation for practitioners to use at least two alternative assessment methods in order to receive more reliable information about players' BMS within the talent promotion process

Biological Maturity Status in Elite Youth Soccer Players: A Comparison of Pragmatic Diagnostics With Magnetic Resonance Imaging

The influence of biological maturity status (BMS) on talent identification and development within elite youth soccer is critically debated. During adolescence, maturity-related performance differences within the same age group may cause greater chances of being selected for early maturing players. Therefore, coaches need to consider players' BMS. While standard methods for assessing BMS in adolescents are expensive and time-consuming imaging techniques (i.e., X-ray and MRI), there also exist more pragmatic procedures. This study aimed to evaluate commonly used methods to assess BMS within a highly selected sample of youth soccer players. A total of N = 63 elite male soccer players (U12 and U14) within the German Soccer Association's talent promotion program completed a test battery assessing BMS outcomes. Utilizing MRI diagnostics, players' skeletal age (SAMRI) was determined by radiologists and served as the reference method. Further commonly used methods included skeletal age measured by an ultrasound device (SAUS), the maturity offset (MOMIR), and the percentage of adult height (PAHKR). The relation of these alternative BMS outcomes to SAMRI was examined using different perspectives: performing bivariate correlation analyses (1), modeling BMS as a latent variable (BMSlat) based on the multiple alternative diagnostics (2), and investigating individual differences in agreement (3). (1) Correlations of SAMRI and the further BMS variables ranked from r = 0.80 to r = 0.84 for the total sample and were lower for U12 (0.56 ≤ r ≤ 0.66), and U14 (0.61 ≤ r ≤ 0.74) (2). The latent structural equation modeling (SEM) (R 2 = 51%) revealed a significant influence on BMSlat for MOMIR (β = 0.51, p <0.05). The additional contribution of PAHKR (β = 0.27, p = 0.06) and SAUS (β = -0.03, p = 0.90) was rather small (3). The investigation of individual differences between the reference method and alternative diagnostics indicated a significant bias for MOMIR (p <0.01). The results support the use of economical and time-efficient methods for assessing BMS within elite youth soccer. Bivariate correlation analyses as well as the multivariate latent variable approach highlight the measures' usefulness. However, the observed individual level differences for some of the utilized procedures led to the recommendation for practitioners to use at least two alternative assessment methods in order to receive more reliable information about players' BMS within the talent promotion process

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance