The Diagnostic Value of Pituitary MRI Radiomics Combined with Clinical Features for Central Precocious Puberty in Girls
- 1Hebei Children’s Hospital, Hebei Clinical Medicine Research Center for Children's Health and Diseases, Shijiazhuang, Hebei, China
- 2Hebei Yiling Hospital,, Shijiazhuang, Hebei, 050035, China
- 3Shijiazhuang Maternity and Child Healthcare, Shijiazhuang, Hebei, China
Abstract
Objective: To evaluate the diagnostic value of pituitary magnetic resonance imaging (MRI) radiomics features combined with clinical parameters in distinguishing idiopathic central precocious puberty (ICPP) from isolated premature thelarche (PT), and to establish a practical and effective predictive model.
Materials and Methods: A retrospective analysis was conducted on 316 girls presenting with breast development, including 231 with ICPP and 85 with PT, from Hebei Children’s Hospital (internal dataset), and 47 girls (26 with ICPP and 21 with PT) from Shijiazhuang Children’s Hospital (external validation dataset). Clinical models, pituitary MRI radiomics models, and combined clinical-radiomics models were developed. Model performance was evaluated using ROC curves, calibration analysis, and decision curve analysis.
Results: Significant differences were observed between the ICPP and isolated PT groups in body mass index (BMI), bone age index (BAI), follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2) (all P < 0.01). The pituitary gland was more frequently protruding in the ICPP group than in the PT group (80.5% vs. 49.5%, P < 0.05). In radiomics models, texture features contributed most significantly, with sagittal anterior pituitary features showing the greatest discriminative value. The clinical-radiomics fusion model outperformed individual clinical or radiomics models in predictive performance.
Discussion: Key clinical and radiomics factors for differentiating idiopathic central precocious puberty (ICPP) from premature thelarche (PT) were identified: baseline BMI, BAI, FSH, LH, E2, and PRL are critical clinical indicators (with baseline LH exhibiting superior specificity as an independent diagnostic marker), and multimodal pituitary MRI radiomics analysis (predominantly the RAD-Total model) highlights the anterior pituitary as a dominant predictive feature, with ICPP-related pituitary enlargement attributed to premature hypothalamic-pituitary-gonadal axis (HPGA) activation.
Notably, the clinical-radiomics fusion model (Clinical+RAD-Total) outperforms standalone clinical or radiomics models in diagnostic accuracy, achieving an AUC of 0.944 and considerable clinical net benefit, despite limitations including low sensitivity due to sample imbalance and slightly diminished performance in external validation cohorts.
Conclusion: Clinical-radiomics models demonstrated excellent predictive performance in both internal and external datasets. Among these, the Clinical+RAD-Total model provided the highest diagnostic accuracy and clinical utility.
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BibTeX
@article{li2026diagnostic,
title = {{The Diagnostic Value of Pituitary MRI Radiomics Combined with Clinical Features for Central Precocious Puberty in Girls}},
author = {Yan Li and Wan Chen and Fangying Liu and Qiang Wang and LiJuan Xu and YuLei Zhai and Xuechao Liu and Fan Yang and Shujing Li},
journal = {Current Medical Imaging},
year = {2026},
month = jul,
doi = {10.2174/0115734056498449260721212122},
url = {https://doi.org/10.2174/0115734056498449260721212122},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The Diagnostic Value of Pituitary MRI Radiomics Combined with Clinical Features for Central Precocious Puberty in Girls
AU - Yan Li
AU - Wan Chen
AU - Fangying Liu
AU - Qiang Wang
AU - LiJuan Xu
AU - YuLei Zhai
AU - Xuechao Liu
AU - Fan Yang
AU - Shujing Li
PY - 2026
DA - 2026/07/25
JO - Current Medical Imaging
DO - 10.2174/0115734056498449260721212122
UR - https://doi.org/10.2174/0115734056498449260721212122
AB - Objective: To evaluate the diagnostic value of pituitary magnetic resonance imaging (MRI) radiomics features combined with clinical parameters in distinguishing idiopathic central precocious puberty (ICPP) from isolated premature thelarche (PT), and to establish a practical and effective predictive model. Materials and Methods: A retrospective analysis was conducted on 316 girls presenting with breast development, including 231 with ICPP and 85 with PT, from Hebei Children’s Hospital (internal dataset), and 47 girls (26 with ICPP and 21 with PT) from Shijiazhuang Children’s Hospital (external validation dataset). Clinical models, pituitary MRI radiomics models, and combined clinical-radiomics models were developed. Model performance was evaluated using ROC curves, calibration analysis, and decision curve analysis. Results: Significant differences were observed between the ICPP and isolated PT groups in body mass index (BMI), bone age index (BAI), follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2) (all P < 0.01). The pituitary gland was more frequently protruding in the ICPP group than in the PT group (80.5% vs. 49.5%, P < 0.05). In radiomics models, texture features contributed most significantly, with sagittal anterior pituitary features showing the greatest discriminative value. The clinical-radiomics fusion model outperformed individual clinical or radiomics models in predictive performance. Discussion: Key clinical and radiomics factors for differentiating idiopathic central precocious puberty (ICPP) from premature thelarche (PT) were identified: baseline BMI, BAI, FSH, LH, E2, and PRL are critical clinical indicators (with baseline LH exhibiting superior specificity as an independent diagnostic marker), and multimodal pituitary MRI radiomics analysis (predominantly the RAD-Total model) highlights the anterior pituitary as a dominant predictive feature, with ICPP-related pituitary enlargement attributed to premature hypothalamic-pituitary-gonadal axis (HPGA) activation. Notably, the clinical-radiomics fusion model (Clinical+RAD-Total) outperforms standalone clinical or radiomics models in diagnostic accuracy, achieving an AUC of 0.944 and considerable clinical net benefit, despite limitations including low sensitivity due to sample imbalance and slightly diminished performance in external validation cohorts. Conclusion: Clinical-radiomics models demonstrated excellent predictive performance in both internal and external datasets. Among these, the Clinical+RAD-Total model provided the highest diagnostic accuracy and clinical utility.
SN - 1573-4056
ER -