Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images-Reference-Cited by-同舟云学术

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Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images

Published:2023-12-20 Issue: Volume: Page:
ISSN:1432-1084
Container-title:European Radiology
language:en
Short-container-title:Eur Radiol

Author:

Zhang Rui,Wei Ying,Wang Denian,Chen Bojiang,Sun Huaiqiang,Lei Yi,Zhou Qing,Luo Zhuang,Jiang Li,Qiu Rong,Shi Feng,Li Weimin

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s00330-023-10518-1.pdf

Reference36 articles.

1. Walter K (2021) Pulmonary nodules. JAMA 326(15):1544

2. Au-Yong ITH, Hamilton W, Rawlinson J, Baldwin DR (2020) Pulmonary nodules. BMJ 371:m3673

3. National Lung Screening Trial Research Team (2013) Results of initial low-dose computed tomographic screening for lung cancer. N Engl J Med 368(21):1980–1991

4. Oke JL, Pickup LC, Declerck J et al (2018) Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol. Diagn Progn Res 2:22

5. MacMahon H, Austin JHM, Gamsu G et al (2005) Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology 237(2):395–400

Cited by 3 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. Deep learning in pulmonary nodule detection and segmentation: a systematic review;European Radiology;2024-07-10

2. Establishment and verification of a prediction model based on clinical characteristics and computed tomography radiomics parameters for distinguishing benign and malignant pulmonary nodules;Journal of Thoracic Disease;2024-03

3. Lung Cancer Prediction Model and Its Progression;Advances in Clinical Medicine;2024

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