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本文引用的文献

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Autosurv: interpretable deep learning framework for cancer survival analysis incorporating clinical and multi-omics data.Autosurv:用于癌症生存分析的可解释深度学习框架,整合临床和多组学数据。
NPJ Precis Oncol. 2024 Jan 5;8(1):4. doi: 10.1038/s41698-023-00494-6.
2
Are medical oncologists ready for the artificial intelligence revolution? Evaluation of the opinions, knowledge, and experiences of medical oncologists about artificial intelligence technologies.医学肿瘤学家是否已准备好迎接人工智能革命?评估医学肿瘤学家对人工智能技术的意见、知识和经验。
Med Oncol. 2023 Oct 9;40(11):327. doi: 10.1007/s12032-023-02200-9.
3
Artificial intelligence in breast cancer: application and future perspectives.人工智能在乳腺癌中的应用及未来展望。
J Cancer Res Clin Oncol. 2023 Nov;149(17):16179-16190. doi: 10.1007/s00432-023-05337-2. Epub 2023 Sep 1.
4
Machine learning approaches for predicting 5-year breast cancer survival: A multicenter study.机器学习方法预测 5 年乳腺癌生存:一项多中心研究。
Cancer Sci. 2023 Oct;114(10):4063-4072. doi: 10.1111/cas.15917. Epub 2023 Jul 25.
5
Novel models by machine learning to predict prognosis of breast cancer brain metastases.基于机器学习的新型模型预测乳腺癌脑转移的预后。
J Transl Med. 2023 Jun 21;21(1):404. doi: 10.1186/s12967-023-04277-2.
6
Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR/HER2 breast cancer.基于数字病理学和深度学习对HR/HER2乳腺癌的临床病理特征、多组学事件及预后进行预测
J Thorac Dis. 2023 May 30;15(5):2528-2543. doi: 10.21037/jtd-23-445. Epub 2023 May 23.
7
Deep multi-modal fusion network with gated unit for breast cancer survival prediction.基于门控单元的深度多模态融合网络在乳腺癌生存预测中的应用。
Comput Methods Biomech Biomed Engin. 2024 May;27(7):883-896. doi: 10.1080/10255842.2023.2211188. Epub 2023 May 11.
8
Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery.利用多种细胞死亡模式预测手术后三阴性乳腺癌患者的预后和药物敏感性。
Int J Surg. 2022 Nov;107:106936. doi: 10.1016/j.ijsu.2022.106936. Epub 2022 Sep 20.
9
Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors.用于预测IV期转移性乳腺癌患者五年生存率的人工智能:关注肌肉减少症和其他宿主因素。
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10
Machine learning predicts the prognosis of breast cancer patients with initial bone metastases.机器学习预测初始骨转移乳腺癌患者的预后。
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人工智能驱动的乳腺癌生存预测创新:一项叙述性综述。

Innovations in Artificial Intelligence-Driven Breast Cancer Survival Prediction: A Narrative Review.

作者信息

Mooghal Mehwish, Nasir Saad, Arif Aiman, Khan Wajiha, Rashid Yasmin Abdul, Vohra Lubna M

机构信息

Section Breast Surgery, Department of Surgery, Aga Khan University Hospital Karachi, Sindh, Pakistan.

Department of Medicine, Aga Khan University Hospital Karachi, Sindh, Pakistan.

出版信息

Cancer Inform. 2024 Sep 29;23:11769351241272389. doi: 10.1177/11769351241272389. eCollection 2024.

DOI:10.1177/11769351241272389
PMID:39483314
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11526191/
Abstract

This narrative review explores the burgeoning field of Artificial Intelligence (AI)-driven Breast Cancer (BC) survival prediction, emphasizing the transformative impact on patient care. From machine learning to deep neural networks, diverse models demonstrate the potential to refine prognosis accuracy and tailor treatment strategies. The literature underscores the need for clinician integration and addresses challenges of model generalizability and ethical considerations. Crucially, AI's promise extends to Low- and Middle-Income Countries (LMICs), presenting an opportunity to bridge healthcare disparities. Collaborative efforts in research, technology transfer, and education are essential to empower healthcare professionals in LMICs. As we navigate this frontier, AI emerges not only as a technological advancement but as a guiding light toward personalized, accessible BC care, marking a significant stride in the global fight against this formidable disease.

摘要

本叙述性综述探讨了人工智能(AI)驱动的乳腺癌(BC)生存预测这一新兴领域,强调其对患者护理的变革性影响。从机器学习到深度神经网络,各种模型都显示出提高预后准确性和定制治疗策略的潜力。文献强调了临床医生整合的必要性,并讨论了模型通用性和伦理考量等挑战。至关重要的是,人工智能的前景延伸到低收入和中等收入国家(LMICs),为缩小医疗保健差距提供了契机。在研究、技术转让和教育方面的合作努力对于增强低收入和中等收入国家医疗保健专业人员的能力至关重要。在我们探索这一前沿领域时,人工智能不仅作为一项技术进步出现,而且作为实现个性化、可及的乳腺癌护理的指引之光,标志着在全球抗击这一可怕疾病的斗争中迈出了重要一步。