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Development and validation of a multimodal interpretable machine learning model with SHAP for malignancy risk prediction in Bethesda III thyroid nodules: a dual-center retrospective cohort study

2026-05-08

Abstract excerpt

<title>Abstract</title> <p> Background To develop a multimodal interpretable machine learning model integrating clinical, ultrasonographic, and BRAF <sup>V600E</sup> mutation data for malignancy risk prediction in Bethesda III thyroid nodules, explore the impact of noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) reclassification on model robustness, and establish a risk st...

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Literature Corpus work
47f20f41-c632-5a71-9d11-6113010f3060
DOI
10.21203/rs.3.rs-9258955/v1
Open publication

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Development and validation of a multimodal interpretable machine learning model with SHAP for malignancy risk prediction in Bethesda III thyroid nodules: a dual-center retrospective cohort studyDOI 10.21203/rs.3.rs-9258955/v1
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