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CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-stage Malignant Ovarian Tumors: A Multi-Class Classification and Multi-Model Comparation Study

2022-11-07

Abstract excerpt

<h4>Background: </h4> To explore and evaluate value a preoperative diagnosis model with contrast-enhanced computed tomography (CECT) imaging-based radiomics analysis in differentiating benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early-stage malignant ovarian tumors (eMOTs). Results The retrospective research was conducted with pathologically confirmed 258 ovarian tumors patients from Janu...

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Literature Corpus work
4400b063-29aa-591d-90d2-0ea43ed3a731
DOI
10.21203/rs.3.rs-2233426/v1
Open publication

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CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-stage Malignant Ovarian Tumors: A Multi-Class Classification and Multi-Model Comparation StudyDOI 10.21203/rs.3.rs-2233426/v1
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