Interpretable machine learning model based on CT semantic features and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors

Abstract To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs) patients. We retrospectively collected the clinical, imaging and pathological d...

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Bibliografische Detailangaben

Hauptverfasser:	Yating Wang, Genji Bai, Yan Liu, Min Huang, Wei Chen, First Wang
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2024-11-01
Schriftenreihe:	Scientific Reports
Schlagworte:	Gastrointestinal stromal tumor Machine learning Support vector machine Radiomics
Online-Zugang:	https://doi.org/10.1038/s41598-024-80978-y
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