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Artificial intelligence propels lung cancer screening: innovations and the challenges of explainability and reproducibility.

作者信息

Mascalchi Mario, Marzi Chiara, Diciotti Stefano

机构信息

Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Firenze, Italy.

Department of Statistics, Computer Science, Applications "G. Parenti", University of Firenze, Firenze, Italy.

出版信息

Signal Transduct Target Ther. 2025 Jan 24;10(1):18. doi: 10.1038/s41392-024-02111-9.

DOI:10.1038/s41392-024-02111-9
PMID:39848967
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11758031/
Abstract
摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25ce/11758031/2e513f8395a1/41392_2024_2111_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25ce/11758031/2e513f8395a1/41392_2024_2111_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25ce/11758031/2e513f8395a1/41392_2024_2111_Fig1_HTML.jpg

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Nat Med. 2024 Nov;30(11):3184-3195. doi: 10.1038/s41591-024-03211-3. Epub 2024 Sep 17.
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Guiding questions to avoid data leakage in biological machine learning applications.指导问题以避免生物机器学习应用中的数据泄露。
Nat Methods. 2024 Aug;21(8):1444-1453. doi: 10.1038/s41592-024-02362-y. Epub 2024 Aug 9.
3
Incidental Findings on Low-Dose CT Scan Lung Cancer Screenings and Deaths From Respiratory Diseases.
低剂量 CT 扫描肺癌筛查中的偶然发现与呼吸系统疾病导致的死亡。
Chest. 2022 Apr;161(4):1092-1100. doi: 10.1016/j.chest.2021.11.015. Epub 2021 Nov 25.
4
Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography.深度学习预测肺癌低剂量计算机断层扫描筛查的心血管疾病风险。
Nat Commun. 2021 May 20;12(1):2963. doi: 10.1038/s41467-021-23235-4.
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Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography.基于低剂量 CT 检测肺结节的良恶性鉴别预测模型评估。
JAMA Netw Open. 2020 Feb 5;3(2):e1921221. doi: 10.1001/jamanetworkopen.2019.21221.