Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest-Reference-Cited by-同舟云学术

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Predicting malignancy of pulmonary ground-glass nodules and their invasiveness by random forest

Published:2018-01 Issue:1 Volume:10 Page:458-463
ISSN:2072-1439
Container-title:Journal of Thoracic Disease
language:
Short-container-title:J. Thorac. Dis.

Author:

Mei Xueyan,Wang Rui,Yang Wenjia,Qian Fangfei,Ye Xiaodan,Zhu Li,Chen Qunhui,Han Baohui,Deyer Timothy,Zeng Jingyi,Dong Xiaomeng,Gao Wen,Fang Wentao

Publisher

AME Publishing Company

Subject

Pulmonary and Respiratory Medicine

Cited by 28 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. Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach;BMC Medical Imaging;2024-09-13

2. Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique;BMC Cancer;2024-09-02

3. Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning;The Clinical Respiratory Journal;2024-05

4. Construction and validation of a predictive model of invasive adenocarcinoma in pure ground-glass nodules less than 2 cm in diameter;BMC Surgery;2024-02-14

5. Potential Role of Intrapulmonary Concomitant Lesions in Differentiating Non-Neoplastic and Neoplastic Ground Glass Nodules;Journal of Inflammation Research;2023-12

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