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Predicting the Biological Behavior of Cervical Squamous Cell Carcinoma: A Machine Learning Approach Using Apparent Transverse Relaxation Rate (R2* maps) Radiomics Nomogram

2025-07-29

Abstract excerpt

<title>Abstract</title> <p>Background Accurate prediction of the biological behavior of cervical squamous cell carcinoma (CSCC) is essential for optimizing therapeutic strategies and enhancing patient outcomes. This study aims to develop and validate a radiomics nomogram based on the apparent transverse relaxation rate (R2* maps) to predict deep stromal invasion (DSI), lymph node metastasis (LNM), and lymph-vascu...

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Literature Corpus work
04acf9ec-c38f-543c-8860-8a0c860b3be5
DOI
10.21203/rs.3.rs-7082528/v1
Open publication

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Predicting the Biological Behavior of Cervical Squamous Cell Carcinoma: A Machine Learning Approach Using Apparent Transverse Relaxation Rate (R2* maps) Radiomics NomogramDOI 10.21203/rs.3.rs-7082528/v1
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