Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study-Reference-Cited by-同舟云学术

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Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study

Published:2023-12 Issue:12 Volume:13 Page:7828-7841
ISSN:2223-4292
Container-title:Quantitative Imaging in Medicine and Surgery
language:
Short-container-title:Quant Imaging Med Surg

Author:

Zhang Rong,Hong Minping,Cai Hongjie,Liang Yanting,Chen Xinjie,Liu Ziwei,Wu Meilian,Zhou Cuiru,Bao Chenzhengren,Wang Huafeng,Yang Shaomin,Hu Qiugen

Publisher

AME Publishing Company

Subject

Radiology, Nuclear Medicine and imaging

Link

https://qims.amegroups.com/article/download/117947/pdf

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study;Quantitative Imaging in Medicine and Surgery;2024-07

2. An interpretable clinical ultrasound-radiomics combined model for diagnosis of stage I cervical cancer;Frontiers in Oncology;2024-05-23

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