• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

计算机辅助诊断与治疗技术在乳腺癌护理中的公共卫生意义。

Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care.

作者信息

Cheng Kai, Wang Jiangtao, Liu Jian, Zhang Xiangsheng, Shen Yuanyuan, Su Hang

机构信息

Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264100, China.

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

出版信息

AIMS Public Health. 2023 Oct 25;10(4):867-895. doi: 10.3934/publichealth.2023057. eCollection 2023.

DOI:10.3934/publichealth.2023057
PMID:38187901
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10764974/
Abstract

Breast cancer remains a significant public health issue, being a leading cause of cancer-related mortality among women globally. Timely diagnosis and efficient treatment are crucial for enhancing patient outcomes, reducing healthcare burdens and advancing community health. This systematic review, following the PRISMA guidelines, aims to comprehensively synthesize the recent advancements in computer-aided diagnosis and treatment for breast cancer. The study covers the latest developments in image analysis and processing, machine learning and deep learning algorithms, multimodal fusion techniques and radiation therapy planning and simulation. The results of the review suggest that machine learning, augmented and virtual reality and data mining are the three major research hotspots in breast cancer management. Moreover, this paper discusses the challenges and opportunities for future research in this field. The conclusion highlights the importance of computer-aided techniques in the management of breast cancer and summarizes the key findings of the review.

摘要

乳腺癌仍然是一个重大的公共卫生问题，是全球女性癌症相关死亡的主要原因。及时诊断和有效治疗对于改善患者预后、减轻医疗负担和促进社区健康至关重要。本系统评价遵循PRISMA指南，旨在全面综合乳腺癌计算机辅助诊断和治疗的最新进展。该研究涵盖了图像分析与处理、机器学习和深度学习算法、多模态融合技术以及放射治疗计划与模拟的最新发展。综述结果表明，机器学习、增强现实和虚拟现实以及数据挖掘是乳腺癌管理的三大主要研究热点。此外，本文还讨论了该领域未来研究面临的挑战和机遇。结论强调了计算机辅助技术在乳腺癌管理中的重要性，并总结了综述的主要发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/d95cb94070ad/publichealth-10-04-057-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/782807683a88/publichealth-10-04-057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/e358cf1be41b/publichealth-10-04-057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/637240510507/publichealth-10-04-057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/cbc711aa01b9/publichealth-10-04-057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/22dc59d8ecd1/publichealth-10-04-057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/659ab1a6898b/publichealth-10-04-057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/1ec6e19b346f/publichealth-10-04-057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/76887063160c/publichealth-10-04-057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/9829e8985191/publichealth-10-04-057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/1740db4f830e/publichealth-10-04-057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/2085e4ee1f9b/publichealth-10-04-057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/9f8deef430f4/publichealth-10-04-057-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/d95cb94070ad/publichealth-10-04-057-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/782807683a88/publichealth-10-04-057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/e358cf1be41b/publichealth-10-04-057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/637240510507/publichealth-10-04-057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/cbc711aa01b9/publichealth-10-04-057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/22dc59d8ecd1/publichealth-10-04-057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/659ab1a6898b/publichealth-10-04-057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/1ec6e19b346f/publichealth-10-04-057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/76887063160c/publichealth-10-04-057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/9829e8985191/publichealth-10-04-057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/1740db4f830e/publichealth-10-04-057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/2085e4ee1f9b/publichealth-10-04-057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/9f8deef430f4/publichealth-10-04-057-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b85f/10764974/d95cb94070ad/publichealth-10-04-057-g013.jpg

相似文献

1
Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care.计算机辅助诊断与治疗技术在乳腺癌护理中的公共卫生意义。
AIMS Public Health. 2023 Oct 25;10(4):867-895. doi: 10.3934/publichealth.2023057. eCollection 2023.
2
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
3
Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review.计算机辅助组织病理学图像分析技术在乳腺癌细胞核异型性自动评分中的应用：综述。
J Digit Imaging. 2020 Oct;33(5):1091-1121. doi: 10.1007/s10278-019-00295-z.
4
Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.基于不同成像模态的用于乳腺癌计算机辅助诊断的机器学习技术：系统综述。
Comput Methods Programs Biomed. 2018 Mar;156:25-45. doi: 10.1016/j.cmpb.2017.12.012. Epub 2017 Dec 12.
5
Deep learning approaches for breast cancer detection in histopathology images: A review.深度学习方法在组织病理学图像乳腺癌检测中的应用：综述。
Cancer Biomark. 2024;40(1):1-25. doi: 10.3233/CBM-230251.
6
ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process.基于先进分割和分类流程的 3D 乳腺 X 线摄影的乳腺癌诊断新模型：ViT-MAENB7。
Comput Methods Programs Biomed. 2024 Dec;257:108373. doi: 10.1016/j.cmpb.2024.108373. Epub 2024 Aug 23.
7
Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image.基于乳腺 X 光图像的机器学习方法辅助乳腺癌检测与诊断的研究综述。
Curr Med Imaging. 2023;19(12):1361-1371. doi: 10.2174/1573405619666230213093639.
8
Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning.基于 MRI 的合成 CT 生成技术的进展：放疗计划技术和趋势的综述。
J Appl Clin Med Phys. 2024 Nov;25(11):e14499. doi: 10.1002/acm2.14499. Epub 2024 Sep 26.
9
Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.从经典方法到深度学习辅助决策支持的肺部结节诊断演进：三十年的发展历程和未来展望。
J Cancer Res Clin Oncol. 2020 Jan;146(1):153-185. doi: 10.1007/s00432-019-03098-5. Epub 2019 Nov 30.
10
Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.深度学习助力乳腺癌诊断：检测与分类技术的新进展。
PLoS One. 2024 Jul 11;19(7):e0304757. doi: 10.1371/journal.pone.0304757. eCollection 2024.

引用本文的文献

1
Oxygen deprivation in breast cancer: mechanisms, pathways, and implications.乳腺癌中的氧剥夺：机制、途径及影响
Ann Med Surg (Lond). 2025 Apr 25;87(6):3635-3659. doi: 10.1097/MS9.0000000000003334. eCollection 2025 Jun.
2
Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients.探索元宇宙：一种对乳腺癌患者具有切实潜在益处的新型虚拟工具。
J Clin Med. 2024 Jul 25;13(15):4337. doi: 10.3390/jcm13154337.

本文引用的文献

1
Arc fault detection using artificial intelligence: Challenges and benefits.利用人工智能进行电弧故障检测：挑战与益处。
Math Biosci Eng. 2023 May 23;20(7):12404-12432. doi: 10.3934/mbe.2023552.
2
Evaluation of the Sensitivity of Breast Cancer Cell Lines to Cardiac Glycosides Unveils ATP1B3 as a Possible Biomarker for the Personalized Treatment of ERα Expressing Breast Cancers.评估乳腺癌细胞系对心脏糖苷的敏感性揭示了 ATP1B3 可能成为 ERα 表达乳腺癌个体化治疗的生物标志物。
Int J Mol Sci. 2022 Sep 21;23(19):11102. doi: 10.3390/ijms231911102.
3
Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.
基于元启发式算法优化的神经网络用于超声图像乳腺癌诊断
Front Oncol. 2022 Jun 13;12:834028. doi: 10.3389/fonc.2022.834028. eCollection 2022.
4
Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning.基于深度神经网络和迁移学习的乳腺癌计算机辅助诊断。
Comput Methods Programs Biomed. 2022 Aug;223:106951. doi: 10.1016/j.cmpb.2022.106951. Epub 2022 Jun 16.
5
Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.基于有效图像处理和分割的机器学习技术在乳腺癌诊断中的应用。
Comput Math Methods Med. 2022 Apr 8;2022:6841334. doi: 10.1155/2022/6841334. eCollection 2022.
6
Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning.基于机器学习的超声图像诊断及乳腺癌位置识别新架构介绍
Diagnostics (Basel). 2021 Oct 11;11(10):1870. doi: 10.3390/diagnostics11101870.
7
Deep Learning for Breast Cancer Diagnosis from Mammograms-A Comparative Study.基于乳房X光照片的深度学习乳腺癌诊断——一项对比研究
J Imaging. 2019 Mar 13;5(3):37. doi: 10.3390/jimaging5030037.
8
Survival analysis in breast cancer using proteomic data from four independent datasets.使用来自四个独立数据集的蛋白质组学数据进行乳腺癌的生存分析。
Sci Rep. 2021 Aug 18;11(1):16787. doi: 10.1038/s41598-021-96340-5.
9
High-Resolution Full-3D Specimen Imaging for Lumpectomy Margin Assessment in Breast Cancer.高分辨率全三维标本成像在乳腺癌保乳手术切缘评估中的应用。
Ann Surg Oncol. 2021 Oct;28(10):5513-5524. doi: 10.1245/s10434-021-10499-9. Epub 2021 Jul 31.
10
Machine learning to improve breast cancer diagnosis by multimodal ultrasound.利用机器学习通过多模态超声改善乳腺癌诊断。
IEEE Int Ultrason Symp. 2018 Oct;2018. doi: 10.1109/ultsym.2018.8579953. Epub 2018 Dec 20.