Related Experiment Video
Updated: Aug 4, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter-Based Machine Learning Model
Pinfa Zou1, Lingfeng Zhang1, Ruifang Zhang2
1Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Insights
An integrated multimodal model shows promise for diagnosing idiopathic central precocious puberty (ICPP) without invasive tests. This approach combines imaging and clinical data for accurate ICPP diagnosis.
Area of Science:
- Pediatric Endocrinology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Idiopathic central precocious puberty (ICPP) negatively impacts child development if not treated early.
- The current standard diagnostic test, the gonadotropin-releasing hormone stimulation test, is invasive and may deter timely diagnosis and intervention.
Purpose of the Study:
- To develop and validate a diagnostic model for ICPP by integrating data from pituitary MRI, carpal bone age, gonadal ultrasound, and basic clinical information.
- To explore the efficacy of machine learning models in diagnosing ICPP using multimodal data.
Main Methods:
- A retrospective study involving 492 girls with precocious puberty (PP) for training and internal validation, and 51 subjects for external validation.
- Radiomics features were extracted from pituitary MRIs. Bone age, ovarian, follicle, and uterine volumes, and endometrium presence were assessed via radiographs and gonadal ultrasound.
- Four machine learning models were developed: pituitary MRI radiomics, integrated imaging, basic clinical, and an integrated multimodal model combining all data.
Main Results:
- The integrated multimodal model achieved the highest diagnostic performance, with an area under the ROC curve (AUC) of 0.860 in training data.
- Internal and external validation confirmed the integrated multimodal model's efficacy, yielding AUCs of 0.862 and 0.866, respectively.
- The integrated multimodal model demonstrated superior diagnostic capability compared to models using only radiomics, imaging, or basic clinical data.
Conclusions:
- The integrated multimodal model shows significant potential as a non-invasive alternative for diagnosing ICPP.
- This approach may improve early detection and intervention for ICPP, mitigating its impact on child development.
Background:
Idiopathic central precocious puberty (ICPP) impairs child development, without early intervention. The current reference standard, the gonadotropin-releasing hormone stimulation test, is invasive which may hinder diagnosis and intervention.
Purpose:
To develop a model for accurate diagnosis of ICPP, by integrating pituitary MRI, carpal bone age, gonadal ultrasound, and basic clinical data.
Study Type:
Retrospective.
Population:
A total of 492 girls with PP (185 with ICPP and 307 peripheral precocious puberty [PPP]) were randomly divided by reference standard into training (75%) and internal validation (25%) data. Fifty-one subjects (16 with ICPP, 35 with PPP) provided by another hospital as external validation.
Field Strength/Sequence:
T1-weighted (spin echo [SE], fast SE, cube) and T2-weighted (fast SE-fat suppression) imaging at 3.0 T or 1.5 T.
Assessment:
Radiomics features were extracted from pituitary MRI after manual segmentation. Carpal bone age, ovarian, follicle and uterine volumes and endometrium presence were assessed from radiographs and gonadal ultrasound. Four machine learning methods were developed: a pituitary MRI radiomics model, an integrated image model (with pituitary MRI, gonadal ultrasound and bone age), a basic clinical model (with age and sex hormone data), and an integrated multimodal model combining all features.
Statistical Tests:
Intraclass correlation coefficients were used to assess consistency of segmentation. Receiver operating characteristic (ROC) curves and the Delong tests were used to assess and compare the diagnostic performance of models. P < 0.05 was considered statistically significant.
Results:
The area under of the ROC curve (AUC) of the pituitary MRI radiomics model, integrated image model, basic clinical model, and integrated multimodal model in the training data was 0.668, 0.809, 0.792, and 0.860. The integrated multimodal model had higher diagnostic efficacy (AUC of 0.862 and 0.866 for internal and external validation).
Conclusion:
The integrated multimodal model may have potential as an alternative clinical approach to diagnose ICPP.
Evidence Level:
3.
Technical Efficacy:
Stage 2.
