Advanced bone age as an indicator facilitates the diagnosis of precocious puberty

Xu, Yingkun; Li, Guimei; Li, Yan

doi:10.1016/j.jped.2017.03.010

Cited by 18 publications

(9 citation statements)

References 21 publications

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“…Secondly, "BA-CA" was the second-top rank feature. Although premature adrenarche is often associated with BA-CA � 2 years [35], a recent study reported that BA-CA was the most effective predictor of positive response to the GnRHa stimulation test [36]. We found that girls with CPP presented with greater BA-CA than those without CPP, in line with previous findings [36,37].…”

Section: Plos Onesupporting

confidence: 91%

“…Although premature adrenarche is often associated with BA-CA � 2 years [35], a recent study reported that BA-CA was the most effective predictor of positive response to the GnRHa stimulation test [36]. We found that girls with CPP presented with greater BA-CA than those without CPP, in line with previous findings [36,37]. LIME analysis revealed that girls with a "BA-CA" of > 2.13 years were at high risk for CPP in both populations, whereas a "BA-CA" of < 1.16 years helped exclude CPP diagnosis.…”

Section: Plos Onementioning

confidence: 99%

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Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches

Huynh

Huang

et al. 2022

PLoS ONE

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Background A brief gonadotropin-releasing hormone analogues (GnRHa) stimulation test which solely focused on LH 30-minute post-stimulation was considered to identify girls with central precocious puberty (CPP). However, it was tested using traditional statistical methods. With advanced computer science, we aimed to develop a machine learning-based diagnostic model that processed baseline CPP-related variables and a brief GnRHa stimulation test for CPP diagnosis. Methods We recruited girls suspected of precocious puberty and underwent a GnRHa stimulation test at Children Hospital 2, Vietnam, and Cathay General Hospital, Taiwan. Clinical data, bone age measurement, and 30-min post-stimulation blood test were used to build up the predictive model. The candidate model was developed by different machine learning algorithms that were mainly evaluated by sensitivity, specificity, the area under the receiver operator characteristic curve (AUC), and F1-score in internal and external validation data to classify girls as CPP and non-CPP at different time-points (0-min, 30-min, 60-min, and 120-min post-stimulation). Results Among the 614 girls diagnosed with PP, 524 (85.3%) had CPP. The random forest algorithm yielded the highest value of F1-score (0.976), specificity (0.893), positive predicted value (0.987), and relatively high value of AUC (0.972) that contributed to high probability to identify CPP. The performance metrics of the 30-min post-stimulation diagnostic model including sensitivity and specificity surpassed those of the 0-minute model (0-min) and were equivalent to those of the model obtained 60-min and 120-min post-stimulation. Hence, our machine learning-based model helps shorten the stimulation test to 30 minutes after GnRHa injection, in general, it requires 120 minutes for a completed GnRHa stimulation test. Conclusions We developed a diagnostic model based on clinical features and a single sample 30-minute post-stimulation to identify CPP in girls that can reduce distress for children caused by multiple blood samplings.

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Section: Plos Onesupporting

confidence: 91%

Section: Plos Onementioning

confidence: 99%

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches

Huynh

Huang

et al. 2022

PLoS ONE

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“…Suh et al [10] found that accelerated growth rate, advanced bone age, and increased basal gonadotropin and insulin-like growth factor-I (IGF-I) levels were correlated with CPP. Traditional statistical analysis including t test and binary logistic regression were used to select factors correlated with the GnRH test [11-14]. Although remarkable progress has been made in these studies, there is a long way to go for their application in clinics due to the low sensitivity or specificity of tests.…”

Section: Introductionmentioning

confidence: 99%

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study

Pan¹,

Liu²,

Mao³

et al. 2019

JMIR Med Inform

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BackgroundCentral precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling.ObjectiveWe aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test.MethodsIn this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models.ResultsBoth the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability.ConclusionsThe prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

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“…It also plays a role in making-decision for treatment. Moreover, in a study, it was demonstrated that advanced bone age is the most effective predictor of the result of GnRH test (32). This indicates that advanced bone age can be used as an alternative diagnostic tool to GnRH test.…”

Section: Discussionmentioning

confidence: 95%

A Diagnostic Scoring System to Distinguish Precocious Puberty from Premature Thelarche based on Clinical and Laboratory Findings

et al. 2018

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Background: The diagnosis of patients who present with early pubertal signs can pose a challenge. Objectives: The aim of this study was to design a new diagnostic scoring system based on clinical and laboratory findings to distinguish precocious puberty (PP) from premature thelarche (PT) among girls diagnosed with early puberty. Methods: We enrolled 267 girls (PP, n = 164; PT, n = 103) previously diagnosed with early puberty using GnRH test, retrospectively. A diagnostic scoring model (M) was constructed with five variables and their cut off values were determined for scoring: age of onset (6.5 years), bone age (BA)/choronologic age (CA) ratio (≥ 1.1), estradiol (12 pg/ml), uterine length (32 mm) and ovarian volume (1.09 cm 3). Overall score was determined for model. The model was also applied prospectively to a second girls population (n = 86) that showed signs of early puberty. Results: The sensitivity and specificity of model was 89.6% and 87.4%, respectively. In the cohort group, the sensitivity and specificity of model was 90% and 89.4%, prospectively. Conclusions: A diagnostic scoring system, based on clinical and laboratory findings can be an alternative or complementary method for the differential diagnosis of early puberty.

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Advanced bone age as an indicator facilitates the diagnosis of precocious puberty

Abstract: According to the statistical analysis, advanced bone age is the most effective predictor of the result of luteinizing hormone-releasing hormone stimulation test.

Cited by 18 publications

References 21 publications

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches

Development and Validation of Clinical Diagnostic Model for Girls with Central Precocious Puberty: Machine-learning Approaches

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study

A Diagnostic Scoring System to Distinguish Precocious Puberty from Premature Thelarche based on Clinical and Laboratory Findings

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