Differentiation of gastric schwannomas from gastrointestinal stromal tumors by CT using machine learning

Differentiation of gastric schwannomas from gastrointestinal stromal tumors by CT using machine learning

Objective To identify schwannomas from gastrointestinal stromal tumors (GISTs) by CT features using Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). Methods This study enrolled 49 patients with schwannomas and 139 with GISTs proven by pat...

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Veröffentlicht in:Abdominal imaging 2021-05, Vol.46 (5), p.1773-1782
Hauptverfasser: Wang, Jian, Xie, Zongyu, Zhu, Xiandi, Niu, Zhongfeng, Ji, Hongli, He, Linyang, Hu, Qiuxiang, Zhang, Cui
Format: Artikel
Sprache:eng
Schlagworte:
Attenuation
Decision trees
Diagnostic systems
Differentiation
Gastroenterology
Gastrointestinal cancer
Growth patterns
Hepatology
Hollow Organ GI
Imaging
Learning algorithms
Life Sciences & Biomedicine
Lymph nodes
Machine learning
Medicine
Medicine & Public Health
Multivariate analysis
Necrosis
Performance evaluation
Radiology
Radiology, Nuclear Medicine & Medical Imaging
Science & Technology
Tumors
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Zusammenfassung:Objective To identify schwannomas from gastrointestinal stromal tumors (GISTs) by CT features using Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT). Methods This study enrolled 49 patients with schwannomas and 139 with GISTs proven by pathology. CT features with P  
ISSN:2366-004X
2366-0058
DOI:10.1007/s00261-020-02797-9