CT-based radiomics research for discriminating the risk stratification of pheochromocytoma using different machine learning models: a multi-center study

Objectives The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. Methods A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training coh...

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Bibliographic Details
Published in:Abdominal imaging 2024-05, Vol.49 (5), p.1569-1583
Main Authors: Zhao, Jinhong, Zhan, Yuan, Zhou, Yongjie, Yang, Zhili, Xiong, Xiaoling, Ye, Yinquan, Yao, Bin, Xu, Shiguo, Peng, Yun, Xiao, Xiaoyi, Zeng, Xianjun, Zuo, Minjing, Dai, Xijian, Gong, Lianggeng
Format: Article
Language:English
Subjects:
Adrenal Gland Neoplasms - diagnostic imaging
Adult
Aged
Bladder
Computed tomography
Female
Gastroenterology
Hepatology
Humans
Image enhancement
Imaging
Kidneys
Learning algorithms
Machine Learning
Male
Medical imaging
Medicine
Medicine & Public Health
Middle Aged
Performance evaluation
Pheochromocytoma
Pheochromocytoma - diagnostic imaging
Radiographic Image Interpretation, Computer-Assisted - methods
Radiology
Radiomics
Regression analysis
Retroperitoneum
Retrospective Studies
Risk
Risk Assessment
Tomography, X-Ray Computed - methods
Ureters
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Summary:Objectives The purpose of this study was to explore and verify the value of various machine learning models in preoperative risk stratification of pheochromocytoma. Methods A total of 155 patients diagnosed with pheochromocytoma through surgical pathology were included in this research (training cohort: n  = 105; test cohort: n  = 50); the risk stratification scoring system classified a PASS score of 
ISSN:2366-0058
2366-004X
2366-0058
DOI:10.1007/s00261-024-04279-8