Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging–Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study
Kyungchul Song, Taehoon Ko, Hyun Wook Chae, Jun Suk Oh, Ho-Seong Kim, Hyun Joo Shin, Jeong-Ho Kim, Ji-Hoon Na, Chae Jung Park, Beomseok Sohn · 2024
Read Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging–Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature: Cross-Sectional, Multicenter Study by Kyungchul Song, Taehoon Ko, Hyun Wook Chae, Jun Suk Oh, Ho-Seong Kim, Hyun Joo Shin, Jeong-Ho Kim, Ji-Hoon Na, Chae Jung Park, Beomseok Sohn (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11635315.txt.
Description
Kyungchul Song and co-authors build a prediction model for short stature in children in a 2024 paper. Growth hormone deficiency and unexplained short stature are the main causes, but confirming them needs invasive hormone tests. The team asked whether MRI image features could help instead.
They studied 293 children for training and 47 from other hospitals for testing. All had pituitary MRI scans and hormone provocation tests. Software extracted 186 radiomic features from the gland, paired with clinical measures. A machine learning model learned to sort the two diagnoses. The aim is a gentler path to diagnosis for short children.
Catalog record
| CREATOR | Kyungchul Song, Taehoon Ko, Hyun Wook Chae, Jun Suk Oh, Ho-Seong Kim, Hyun Joo Shin, Jeong-Ho Kim, Ji-Hoon Na, Chae Jung Park, Beomseok Sohn |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |