Diagnostic value of pituitary volume in girls with precocious puberty

Brain tissue biomarker impact bone age in central precocious puberty more than hormones: a quantitative synthetic magnetic resonance study

OBJECTIVE

To investigate which brain tissue component volume (BTCV) biomarkers may be more effective than hormones in influencing bone age development in central precocious puberty (CPP).

METHODS

This retrospective study included 84 children with CPP and 84 controls. Data on cranial synthetic magnetic resonance (SyMR), X-ray bone age, and three hormones were collected. BTCVs-myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF (NoN)-were obtained from SyMRI. A deep learning model assessed Tanner-Whitehouse III (TW3) bone age scores (TW3-RUS, TW3-Carpal). We evaluated the correlation between BTCVs, bone age scores, luteinizing hormone (LH), LH after gonadotropin-releasing hormone (GnRH) stimulation, and follicle-stimulating hormone (FSH).

RESULTS

Children with CPP had lower MyC, WM, and GM than controls. The TW3-RUS score did not correlate with BTCVs or hormones. The TW3-Carpal score was positively correlated with MyC (r = 0.397, P < 0.001) but not with WM, GM, CSF, NoN, or hormones. The regression model showed a positive correlation between the TW3-Carpal score and MyC (β = 0.077, P < 0.001), while LH correlated with GM and NoN (β = - 16.66, P = 0.019; β = 24.62, P = 0.019).

CONCLUSION

The TW3-Carpal score in CPP positively correlates with MyC, while two TW3 scores do not correlate with hormone levels, suggesting myelin has a greater impact on bone age development than hormones. MyC may serve as a potential biomarker in BTCVs for CPP.

Magnetic resonance imaging -based radiomics of the pituitary gland is highly predictive of precocious puberty in girls: a pilot study

Background

The aim of the study was to explore a radiomic model that could assist physicians in the diagnosis of central precocious puberty (CPP). A predictive model based on radiomic features (RFs), extracted form magnetic resonance imaging (MRI) of the pituitary gland, was thus developed to distinguish between CPP and control subjects.

Methods

45 girls with confirmed diagnosis of CPP (CA:8.4 ± 0.9 yr) according to the current criteria and 47 age-matched pre-pubertal control subjects (CA:8.7 ± 1.2 yr) were retrospectively enrolled. Two readers (R1, R2) blindly segmented the pituitary gland on MRI studies for RFs and performed a manual estimation of the pituitary volume. Radiomics was compared against pituitary volume in terms of predictive performances (metrics: ROC-AUC, accuracy, sensitivity and specificity) and reliability (metric: intraclass correlation coefficient, ICC). Pearson correlation between RFs and auxological, biochemical, and ultrasound data was also computed.

Results

Two different radiomic parameters, Shape Surface Volume Ratio and Glrlm Gray Level Non-Uniformity, predicted CPP with a high diagnostic accuracy (ROC-AUC 0.81 ± 0.08) through the application of our ML algorithm. Anthropometric variables were not confounding factors of these RFs suggesting that premature thelarche and/or pubarche would not be potentially misclassified. The selected RFs correlated with baseline and peak LH (p < 0.05) after GnRH stimulation. The diagnostic sensitivity was improved compared to pituitary volume only (0.76 versus 0.68, p<0.001) and demonstrated higher inter-reader reliability (ICC>0.57 versus ICC=0.46).

Discussion

Radiomics is a promising tool to diagnose CPP as it reflects also functional aspects. Further studies are warranted to validate these preliminary data.

Assessing the Diagnostic Performance of Automated Pituitary Gland Volume Measurement for Idiopathic Central Precocious Puberty

Background/Objectives: It is known that the pituitary gland volume (PV) in idiopathic central precocious puberty (IPP) is significantly higher than in healthy children. However, most PV measurements rely on manual quantitative methods, which are time-consuming and labor-intensive. This study aimed to automatically measure the PV of patients with IPP using artificial intelligence to accurately quantify the correlation between IPP and PV, and to improve the efficiency of diagnosing IPP. Methods: From July 2016 to February 2024, 226 patients who had been diagnosed with IPP and undergone brain MR imaging were included (117 males and 109 females; median age, 8 years; interquartile range, 7-9 years). A control group of 52 patients who had undergone brain MR imaging without symptoms of precocious puberty was also included (37 males and 15 females; median age, 8 years; interquartile range, 8-9 years). Measurement variability was examined between manual and automatic measurements (n = 57). The pituitary gland volume was measured using 1-3 mm thickness T1 sagittal images from non-enhanced brain MR imaging, analyzed with the MA-net artificial intelligence learning method. Physical characteristics (height, weight, and age) were correlated with PV, and the difference in PV between the IPP group and the control group was evaluated. Results: The intraclass correlation coefficient was 0.993 for agreement between manual and automatic measurement. Confounding bias was reduced by PSM. PV was positively correlated with age and body weight in the IPP group (17.4%, p = 0.009, and 14.0%, p = 0.037). The median values of PV were 432 mm³ in the IPP group and 380 mm³ in the control group, showing a significant difference of 52 mm³ (p < 0.05). Conclusions: The PV in the IPP group was significantly higher than in the control group. Automatically measuring PV along with assessing hormone levels could enable a faster and more straightforward diagnosis of IPP.

[Construction of a diagnostic model and scoring system for central precocious puberty in girls, with external validation]

OBJECTIVES

To establish an efficient and clinically applicable predictive model and scoring system for central precocious puberty (CPP) in girls, and to develop a diagnostic prediction application.

METHODS

A total of 342 girls aged 4 to 9 years with precocious puberty were included, comprising 216 cases of CPP and 126 cases of isolated premature thelarche. Lasso regression was used to screen for predictive factors, and logistic regression was employed to establish the predictive model. Additionally, a scoring system was constructed using the evidence weight binning method. Data from 129 girls aged 4 to 9 years with precocious puberty were collected for external validation of the scoring system.

RESULTS

The logistic regression model incorporated five predictive factors: age, insulin-like growth factor-1 (IGF-1), serum follicle-stimulating hormone (FSH), the luteinizing hormone (LH)/FSH baseline ratio, and uterine thickness. The calculation formula was: ln(P/1-P)=-8.439 + 0.216 × age (years) + 0.008 × IGF-1 (ng/mL) + 0.159 × FSH (mIU/mL) + 9.779 × LH/FSH baseline ratio + 0.284 × uterine thickness (mm). This model demonstrated good discriminative ability (area under the curve=0.892) and calibration (Hosmer-Lemeshow test P>0.05). The scoring system based on this logistic regression model showed good discrimination in both the prediction model and external validation datasets, with areas under the curve of 0.895 and 0.805, respectively. Based on scoring system scores, the population was stratified into three risk levels: high, medium, and low. In the high-risk group, the prevalence of CPP exceeded 90%, while the proportion was lower in the medium and low-risk groups.

CONCLUSIONS

The CPP diagnostic predictive model established for girls aged 4 to 9 years exhibits good diagnostic performance. The scoring system can effectively and rapidly stratify the risk of CPP, providing valuable reference for clinical decision-making.

Gut flora influences the hypothalamic-gonadal axis to regulate the pathogenesis of obesity-associated precocious puberty

The prevalence of obesity-associated precocious puberty is gradually increasing, but the relationship between gut flora and obesity-associated precocious puberty remains unclear.We analysed the gut flora characteristics of a clinical sample of 30 girls aged 5-8 years using 16s rRNA sequencing. An obesity rat model and a rat model of gut flora transplantation were also constructed. Body weight, body length, abdominal girth, food intake, vulva opening time, and gonadal index were monitored. The secretion levels of estradiol (E2), total cholesterol (TC), follicle-stimulating hormone (FSH), luteinizing hormone (LH), and thyroglobulin (Tg) were analyzed by ELISA. In addition, ovarian and uterine development was observed by HE staining. The mRNA and protein levels of kisspeptin-1 (Kiss-1) and gonadotropin-releasing.We found that the relative abundance of Dialister, Bacteroides, Bifidobacterium, Collinsella, and Romboutsia may be associated with obesity-associated precocious puberty. Obesity promotes gonadal development, and the gut flora of patients with obesity and obesity-associated precocious puberty regulated the gene and protein expression of Kiss-1 and GnRH, promoting precocious puberty and hypothalamic-gonadal axis hormone secretion in rats. In contrast, probiotic intervention slowed gonadal development, reduced hormone secretion, and attenuated hypothalamic-gonadal axis activity. Gut flora promoted obesity-associated precocious puberty by influencing the hypothalamic-gonadal axis, and probiotics have a therapeutic and preventive role in obesity-associated precocious puberty, which may be associated with the Kiss-1/GnRH pathway. These findings may provide some new strategies for clinical treatment and prevention of obesity-associated precocious puberty in girls.

Pitfalls of diagnosing pituitary hypoplasia in the patients with short stature

PURPOSE

Height age (HA) and bone age (BA) delay is well known in the patients with short stature. Therefore assessing pituitary hypoplasia based on chronological age (CA) might cause overdiagnosis of pituitary hypoplasia. We aimed to investigate the diagnostic and prognostic value of the PH and PV based on CA, HA, or BA in the patients with GHD.

METHODS

Fifty-seven patients with severe and 40 patients with partial GHD and 39 patients with ISS assigned to the study. For defining the most accurate diagnosis of pituitary hypoplasia, PH and PV were evaluated based on CA, BA and HA. The relationship of each method with clinical features was examined.

RESULTS

The mean PV was significantly larger in patients with ISS compared to the GH-deficient patients. PV was more correlated with clinical features including height SDS, stimulated GH concentration, IGF-1 and IGFBP-3 SDS, height velocity before and after rGH therapy. We found BA-based PV could discriminate GHD from ISS (Sensitivity: 17%, specificity: 98%, positive predictive value: 94%, negative predictive value: 39%), compared to the other methods based on PH or PV respect to CA and HA. 3% of patients with ISS, 17% of patients with GHD had pituitary hypoplasia based on PV-BA.

CONCLUSION

PV based on BA, has the most accurate diagnostic value for defining pituitary hypoplasia. But it should be kept in mind that there might be still misdiagnosed patients by this method. PV is also a significant predictor for the rGH response.

MRI Radiomics Features of Adenohypophysis Determine the Activation of Hypothalamic-Pituitary-Gonadal Axis in Peri-Puberty Children

BACKGROUND

The status of the hypothalamic-pituitary-gonadal (HPG) axis is important for assessing the onset of physiological or pathological puberty. The reference standard gonadotropin-releasing hormone (GnRH) stimulation test requires hospital admission and repeated blood samples. A simple noninvasive method would be beneficial.

OBJECTIVES

To explore a noninvasive method for evaluating HPG axis activation in children using an MRI radiomics model.

STUDY TYPE

Retrospective.

POPULATION

Two hundred thirty-nine children (83 male; 3.6-14.6 years) with hypophysial MRI and GnRH stimulation tests, randomly divided a training set (168 children) and a test set (71 children).

FIELD STRENGTH/SEQUENCE

3.0 T, 3D isotropic fast spin echo (CUBE) T1-weighted imaging (T1WI) sequences.

ASSESSMENT

Radiomics features were extracted from sagittal 3D CUBE T1WI, and imaging signatures were generated using the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation. Diagnostic performance for differential diagnosis of HPG status was compared between a radiomics model and MRI features (adenohypophyseal height [aPH] and volume [aPV]).

STATISTICAL TESTS

Receiver operating characteristic (ROC) and decision curve analysis (DCA). A P value <0.05 was considered statistically significant.

RESULTS

Eight hundred fifty-one radiomics features were extracted and reduced to 10 by the LASSO method in the training cohort. The radiomics model based on CUBE T1WI showed good performance in assessment of HPG axis activation with an area under the ROC curve (AUC) of 0.81 (95% CI: 0.71, 0.91) in the test set. The AUC of the radiomics model was significantly higher than that of aPH (0.81 vs. 0.65) but there was no significant difference compared to aPV (0.81 vs. 0.78, P = 0.58). In DCA analysis, the radiomics signature showed higher net benefit over the aPV and aPH models.

DATA CONCLUSIONS

The MRI radiomics model has potential to assess HPG axis activation status noninvasively, potentially providing valuable information in the diagnosis of patients with pathological puberty onset.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Altered pubertal timing in 7q11.23 copy number variations and associated genetic mechanisms

Pubertal timing, including age at menarche (AAM), is a heritable trait linked to lifetime health outcomes. Here, we investigate genetic mechanisms underlying AAM by combining genome-wide association study (GWAS) data with investigations of two rare genetic conditions clinically associated with altered AAM: Williams syndrome (WS), a 7q11.23 hemideletion characterized by early puberty; and duplication of the same genes (7q11.23 Duplication syndrome [Dup7]) characterized by delayed puberty. First, we confirm that AAM-derived polygenic scores in typically developing children (TD) explain a modest amount of variance in AAM (R2 = 0.09; p = 0.04). Next, we demonstrate that 7q11.23 copy number impacts AAM (WS < TD < Dup7; p = 1.2x10-8, η2 = 0.45) and pituitary volume (WS < TD < Dup7; p = 3x10-5, ηp2 = 0.2) with greater effect sizes. Finally, we relate an AAM-GWAS signal in 7q11.23 to altered expression in postmortem brains of STAG3L2 (p = 1.7x10-17), a gene we also find differentially expressed with 7q11.23 copy number (p = 0.03). Collectively, these data explicate the role of 7q11.23 in pubertal onset, with STAG3L2 and pituitary development as potential mediators.

Diagnostic value of adenohypophyseal MRI features in female children with precocious puberty

AIM

To evaluate the diagnostic value of adenohypophyseal magnetic resonance imaging (MRI) features for precocious puberty (PP) in female children and also to establish a non-invasive diagnostic approach in clinics.

MATERIALS AND METHODS

A total of 126 female children (37, 57, and 32 female children clinically diagnosed with central PP [CPP], incomplete PP [IPP], and controls, respectively) were enrolled in this study. Data were collected and analysed using analysis of variance. Pearson correlation and stepwise multivariate linear regression analysis were used to examine the association and build prediction models. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficacy.

RESULTS

The values of adenohypophysis volume (aPV), adenohypophysis height (aPH), and signal-intensity ratio (SIR), height, weight, and seven laboratory testing characteristics were correlated closely with the activation status of the hypothalamic-pituitary-gonad axis in the different groups (all p<0.05). Model 1 including aPV, weight, and aPH and Model 2 including SIR, aPV, and height were built to obtain predicted luteinising hormone (LH; R2 = 0.271) and LH/follicle stimulating hormone (FSH; R2 = 0.311). ROC analysis showed the predicted LH, predicted LH/FSH, and aPV were the top 3 best predictors in distinguishing CPP from controls (AUC = 0.969, 0.949, and 0.938) while predicted LH/FSH was the best predictor in distinguishing CPP from IPP and controls (AUC = 0.829 and 0.828).

CONCLUSION

The adenohypophysis volume itself and the prediction models including main adenohypophyseal MRI features increased diagnostic efficiency for PP and offered a non-invasive and credible diagnostic method.

deepPGSegNet: MRI-based pituitary gland segmentation using deep learning

Introduction

In clinical research on pituitary disorders, pituitary gland (PG) segmentation plays a pivotal role, which impacts the diagnosis and treatment of conditions such as endocrine dysfunctions and visual impairments. Manual segmentation, which is the traditional method, is tedious and susceptible to inter-observer differences. Thus, this study introduces an automated solution, utilizing deep learning, for PG segmentation from magnetic resonance imaging (MRI).

Methods

A total of 153 university students were enrolled, and their MRI images were used to build a training dataset and ground truth data through manual segmentation of the PGs. A model was trained employing data augmentation and a three-dimensional U-Net architecture with a five-fold cross-validation. A predefined field of view was applied to highlight the PG region to optimize memory usage. The model's performance was tested on an independent dataset. The model's performance was tested on an independent dataset for evaluating accuracy, precision, recall, and an F1 score.

Results and discussion

The model achieved a training accuracy, precision, recall, and an F1 score of 92.7%, 0.87, 0.91, and 0.89, respectively. Moreover, the study explored the relationship between PG morphology and age using the model. The results indicated a significant association between PG volume and midsagittal area with age. These findings suggest that a precise volumetric PG analysis through an automated segmentation can greatly enhance diagnostic accuracy and surveillance of pituitary disorders.

MRI evaluation of cranial pathologies in rapidly progressive early puberty cases aged 8-9

Purpose

The aim of this study was to investigate the frequency and distribution of intracranial pathologies in female patients between 8 and 9 years of age who were diagnosed with early puberty (rapidly progressive) through the evaluation of MRI images.

Materials and methods

A total of 74 female patients diagnosed with central precocious puberty (CPP) (6-8 years) and rapidly progressive early puberty (RPEP) (8-9 years) were included in the study. The patients were categorized into two groups, normal and abnormal, based on the findings from their MRI scans. Recent literature has classified abnormal MRI findings into three groups: pathological findings, findings with a questionable relationship to CPP, and incidental findings. Furthermore, the patients were divided into four groups based on their MRI findings and whether they had CPP or RPEP : CPP (6-8 years) +Normal MRI, RPEP (8-9 years) + Normal MRI, CPP (6-8 years) +Abnormal MRI, RPEP (8-9 years) +Abnormal MRI.

Results

Out of the 74 girls included in the study, 54% (n=40) showed normal MRI results, while abnormal MRI findings were detected in 46% (n = 34) of the cases. No malignant lesions were identified among cases with abnormal MRI findings. The occurrence of abnormal MRI findings was observed in 46% of the PP group and 45% of the RPEP group. Incidental findings were the most common MRI findings in both groups. The proportion of cases with pathological findings and findings with a questionable relationship to CPP was similar in both groups (p = 0.06). Basal luteinizing hormone (LH) concentration was found to be higher in the RPEP (8-9 years) +Abnormal MRI group compared to the CPP (6-8 years) +Normal MRI group (p = 0.01).

Conclusion

Our study is the first to investigate MRI findings in cases of rapidly progressive early puberty in the age range of 8-9 years. Our study demonstrates that there is no difference in terms of intracranial findings between cases of precocious puberty at the age of 6-8 years and cases of rapidly progressive early puberty aged 8-9.

Basal gonadotropin levels combine with pelvic ultrasound and pituitary volume: a machine learning diagnostic model of idiopathic central precocious puberty

OBJECTIVE

The current diagnosis of central precocious puberty (CPP) relies on the gonadotropin-releasing hormone analogue (GnRHa) stimulation test, which requires multiple invasive blood sampling procedures. The aim of this study was to construct machine learning models incorporating basal pubertal hormone levels, pituitary magnetic resonance imaging (MRI), and pelvic ultrasound parameters to predict the response of precocious girls to GnRHa stimulation test.

METHODS

This retrospective study included 455 girls diagnosed with precocious puberty who underwent transabdominal pelvic ultrasound, brain MRI examinations and GnRHa stimulation testing were retrospectively reviewed. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Four machine learning classifiers were developed to identify girls with CPP, including logistic regression, random forest, light gradient boosting (LightGBM), and eXtreme gradient boosting (XGBoost). The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under receiver operating characteristic (AUC) and F1 score of the models were measured.

RESULTS

The participates were divided into an idiopathic CPP group (n = 263) and a non-CPP group (n = 192). All machine learning classifiers used achieved good performance in distinguishing CPP group and non-CPP group, with the area under the curve (AUC) ranging from 0.72 to 0.81 in validation set. XGBoost had the highest diagnostic efficacy, with sensitivity of 0.81, specificity of 0.72, and F1 score of 0.80. Basal pubertal hormone levels (including luteinizing hormone, follicle-stimulating hormone, and estradiol), averaged ovarian volume, and several uterine parameters were predictors in the model.

CONCLUSION

The machine learning prediction model we developed has good efficacy for predicting response to GnRHa stimulation tests which could help in the diagnosis of CPP.

Brain structure alterations in girls with central precocious puberty

Purpose

Central precocious puberty (CPP) is puberty that occurs at an unusually early age with several negative psychological outcomes. There is a paucity of data on the morphological characteristics of the brain in CPP. This study aimed to determine the structural differences in the brain of patients with CPP.

Methods

We performed voxel- and surface-based morphometric analyses of 1.5 T T1-weighted brain images scanned from 15 girls with CPP and 13 age-matched non-CPP controls (NC). All patients with CPP were diagnosed by gonadotropin-releasing hormone (GnRH) stimulation test. The magnetic resonance imaging (MRI) data were evaluated using Levene's test for equality of variances and a two-tailed unpaired t-test for equality of means. False discovery rate correction for multiple comparisons was applied using the Benjamini-Hochberg procedure.

Results

Morphometric analyses of the brain scans identified 33 candidate measurements. Subsequently, increased thickness of the right precuneus was identified in the patients with CPP using general linear models and visualizations of cortical thickness with a t-statistical map and a random field theory map.

Conclusion

The brain scans of the patients with CPP showed specific morphological differences to those of the control. The features of brain morphology in CPP identified in this study could contribute to further understanding the association between CPP and detrimental psychological outcomes.

Evaluatıon of Pituıtary/Cranial imagıng results of central puberty precocıous cases

Background

The activation of the gonadotropin-releasing hormone (GnRH) pulse generator before the age of 8 years in girls and 9 years in boys results in central precocious puberty (CPP). Previous studies have shown that the height of the pituitary gland in the CPP cases is higher than in the normal children.

Aim

In this study, ıt was aimed to evaluate the pituitary gland volüme by MRI in CPP children, and explore the intracranial lesions among children with CPP.

Patients and Methods

The study was performed with 50 children (41 girls, 9 boys) who had been diagnosed with CPP. Pituitary MRI was performed in every child after the diagnosis of CPP. Pituitary gland volüme in CPP children was compared with age/sex-matched control subjects. In addition, if available, cranial MRI of patients were evaluated for the presence of additional intracranial abnormalities or space-occupying lesions.

Results

The mean chronological age at diagnosis was 7.1 ± 1.0 (2.4-7.9) years in girls and 7.4 ± 1.7 (3.7-8.8) years in boys. CNS imaging showed pathological findings in 17% (7/41) of the girl cases and 55.5% (5/9) of the boy cases. Pituitary volumes of girls aged 6.0-7.9 years and boys aged 8.0-8.9 years were found to be increased compared to the control group.

Conclusion

In this study, we found that CNS imaging showed pathological findings in 17% of the girl cases, and 55.5% of the boy cases. Pituitary volumes of girls aged 6.0-7.9 years and boys aged 8.0-8.9 years were found to be increased compared to the control group.

The Use of Morning Urinary Gonadotropins and Sex Hormones in the Management of Early Puberty in Chinese Girls

CONTEXT

Although gonadotropin-releasing hormone stimulation test (GnRHST) is the gold standard in diagnosing central precocious puberty (CPP), it is invasive, expensive, and time-consuming, requiring multiple blood samples to measure gonadotropin levels.

OBJECTIVE

We evaluated whether urinary hormones could be potential biomarkers for prepuberty or postpuberty, aiming to simplify the current diagnosis and prognosis procedure.

METHODS

We performed a cross-sectional study of a total of 355 girls with CPP in National Clinical Research Center for Child Health in China, including 258 girls with positive and 97 girls with negative results from GnRHST. Twenty patients received GnRH analogue (GnRHa) treatment and completed a 6-month follow up. We measured luteinizing hormone (LH), follicle-stimulating hormone (FSH), estradiol, prolactin, progesterone, testosterone, and human chorionic gonadotropin in the first morning voided urine samples.

RESULTS

Their urinary LH levels and the ratios of LH to FSH increased significantly with the advancement in Tanner stages. uLH levels were positively associated with basal and peak LH levels in the serum after GnRH stimulation. A cutoff value of 1.74 IU/L for uLH reached a sensitivity of 69.4% and a specificity of 75.3% in predicting a positive GnRHST result. For the combined threshold (uLH ≥ 1.74 + uLH-to-uFSH ratio > 0.4), the specificity reached 86.6%. After 3 months of GnRHa therapy, the uLH and uFSH levels decreased accordingly.

CONCLUSION

uLH could be a reliable biomarker for initial CPP diagnosis and screening; uLH could also be an effective marker for evaluating the efficacy of clinical treatment.