Machine Learning in Oncology: Methods, Applications, and Challenges.
作者信息
Bertsimas Dimitris, Wiberg Holly
机构信息
Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA.
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA.
出版信息
JCO Clin Cancer Inform. 2020 Oct;4:885-894. doi: 10.1200/CCI.20.00072.
Abstract
摘要
相似文献
1
Machine Learning in Oncology: Methods, Applications, and Challenges.肿瘤学中的机器学习：方法、应用与挑战。
JCO Clin Cancer Inform. 2020 Oct;4:885-894. doi: 10.1200/CCI.20.00072.
2
Machine Learning and Imaging Informatics in Oncology.肿瘤学中的机器学习和成像信息学。
Oncology. 2020;98(6):344-362. doi: 10.1159/000493575. Epub 2018 Nov 23.
3
Potential and limitations of radiomics in neuro-oncology.放射组学在神经肿瘤学中的潜力和局限性。
J Clin Neurosci. 2021 Aug;90:206-211. doi: 10.1016/j.jocn.2021.05.015. Epub 2021 Jun 11.
4
Machine Learning in oncology: A clinical appraisal.机器学习在肿瘤学中的应用：临床评价。
Cancer Lett. 2020 Jul 1;481:55-62. doi: 10.1016/j.canlet.2020.03.032. Epub 2020 Apr 3.
5
Role of Machine Learning and Artificial Intelligence in Interventional Oncology.机器学习和人工智能在介入肿瘤学中的作用。
Curr Oncol Rep. 2021 Apr 20;23(6):70. doi: 10.1007/s11912-021-01054-6.
6
Machine Learning in Oncology: What Should Clinicians Know?机器学习在肿瘤学中的应用：临床医生应该了解哪些知识？
JCO Clin Cancer Inform. 2020 Sep;4:799-810. doi: 10.1200/CCI.20.00049.
7
Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging.我们正处于十字路口还是高原期？腹部肿瘤影像学中的放射组学和机器学习。
Abdom Radiol (NY). 2019 Jun;44(6):1985-1989. doi: 10.1007/s00261-018-1613-1.
8
From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment.从模式到患者：癌症诊断、预后和治疗的临床机器学习进展。
Cell. 2023 Apr 13;186(8):1772-1791. doi: 10.1016/j.cell.2023.01.035. Epub 2023 Mar 10.
9
Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications.肌肉骨骼肿瘤影像学中的人工智能：当前应用的批判性综述
Diagn Interv Imaging. 2023 Jan;104(1):18-23. doi: 10.1016/j.diii.2022.10.004. Epub 2022 Oct 18.
10
Machine learning applications in gynecological cancer: A critical review.机器学习在妇科肿瘤中的应用：一项批判性综述。
Crit Rev Oncol Hematol. 2022 Nov;179:103808. doi: 10.1016/j.critrevonc.2022.103808. Epub 2022 Sep 7.
引用本文的文献
1
Machine Learning Algorithm to Explore Patients With Heterogeneous Treatment Effects of Clinically Significant CMV Infection and Non-Relapse Mortality After HSCT.探索异质性治疗效果的HSCT后具有临床意义的巨细胞病毒感染和非复发死亡率患者的机器学习算法
EJHaem. 2025 Aug 9;6(4):e70117. doi: 10.1002/jha2.70117. eCollection 2025 Aug.
2
Integrative machine learning approach for forecasting lung cancer chemosensitivity: From algorithm to cell line validation.用于预测肺癌化疗敏感性的整合机器学习方法：从算法到细胞系验证
Comput Struct Biotechnol J. 2025 Jul 24;27:3307-3318. doi: 10.1016/j.csbj.2025.07.043. eCollection 2025.
3
An Interpretable Machine Learning Model Based on Inflammatory-Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery.一种基于炎症-营养生物标志物的可解释机器学习模型，用于预测结直肠癌手术后的异时性肝转移。
Biomedicines. 2025 Jul 12;13(7):1706. doi: 10.3390/biomedicines13071706.
4
5
Aging in the Skeletal Muscle Revealed by Molecular Immunohistochemical Imaging.分子免疫组织化学成像揭示骨骼肌衰老
Int J Mol Sci. 2025 Jun 22;26(13):5986. doi: 10.3390/ijms26135986.
6
Progress and challenges for the application of machine learning for neglected tropical diseases.机器学习在 neglected tropical diseases 中的应用进展与挑战。 （注：“neglected tropical diseases”直译为“被忽视的热带病” ）
F1000Res. 2025 May 20;12:287. doi: 10.12688/f1000research.129064.2. eCollection 2023.
7
Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy.对接受化疗、激素治疗、手术和放疗的乳腺癌患者生存结果的机器学习分析。
Sci Rep. 2025 Jul 10;15(1):24981. doi: 10.1038/s41598-025-97763-0.
8
Current Bioinformatics Tools in Precision Oncology.精准肿瘤学中的当前生物信息学工具
MedComm (2020). 2025 Jul 9;6(7):e70243. doi: 10.1002/mco2.70243. eCollection 2025 Jul.
9
An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.一种用于预测胰腺癌手术后早期肝转移的可解释机器学习模型。
BMC Cancer. 2025 Jul 1;25(1):1117. doi: 10.1186/s12885-025-14503-3.
10
A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study.用于评估、选择和解释2型糖尿病患者心血管疾病结局机器学习模型的责任框架：方法与验证研究
JMIR Med Inform. 2025 Jun 27;13:e66200. doi: 10.2196/66200.
本文引用的文献
1
Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.低剂量 CT 随访筛查中肺癌风险的预测：深度学习方法的训练和验证研究。
Lancet Digit Health. 2019 Nov;1(7):e353-e362. doi: 10.1016/S2589-7500(19)30159-1. Epub 2019 Oct 17.
2
From Local Explanations to Global Understanding with Explainable AI for Trees.利用可解释人工智能实现从局部解释到树木的全局理解
Nat Mach Intell. 2020 Jan;2(1):56-67. doi: 10.1038/s42256-019-0138-9. Epub 2020 Jan 17.
3
FOLFOX treatment response prediction in metastatic or recurrent colorectal cancer patients via machine learning algorithms.基于机器学习算法的转移性或复发性结直肠癌患者 FOLFOX 治疗反应预测。
Cancer Med. 2020 Feb;9(4):1419-1429. doi: 10.1002/cam4.2786. Epub 2020 Jan 1.
4
A Machine-Based Approach to Preoperatively Identify Patients with the Most and Least Benefit Associated with Resection for Intrahepatic Cholangiocarcinoma: An International Multi-institutional Analysis of 1146 Patients.一种基于机器的方法，用于术前识别与肝内胆管癌切除相关获益最多和获益最少的患者：来自 1146 例患者的国际多机构分析。
Ann Surg Oncol. 2020 Apr;27(4):1110-1119. doi: 10.1245/s10434-019-08067-3. Epub 2019 Nov 14.
5
Key challenges for delivering clinical impact with artificial intelligence.人工智能实现临床影响的关键挑战。
BMC Med. 2019 Oct 29;17(1):195. doi: 10.1186/s12916-019-1426-2.
6
Deep Learning to Improve Breast Cancer Detection on Screening Mammography.深度学习在提高筛查性乳房 X 光摄影乳腺癌检测中的应用。
Sci Rep. 2019 Aug 29;9(1):12495. doi: 10.1038/s41598-019-48995-4.
7
Artificial Intelligence in Health Care: Will the Value Match the Hype?医疗保健中的人工智能：其价值能否与炒作相匹配？
JAMA. 2019 Jun 18;321(23):2281-2282. doi: 10.1001/jama.2019.4914.
8
A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction.基于深度学习的乳腺 X 线摄影模型提高乳腺癌风险预测。
Radiology. 2019 Jul;292(1):60-66. doi: 10.1148/radiol.2019182716. Epub 2019 May 7.
9
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging.深度学习从连续医学成像预测肺癌治疗反应。
Clin Cancer Res. 2019 Jun 1;25(11):3266-3275. doi: 10.1158/1078-0432.CCR-18-2495. Epub 2019 Apr 22.
10
Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.基于人工神经网络的 MRI 神经肿瘤学中肿瘤自动定量反应评估：多中心回顾性研究。
Lancet Oncol. 2019 May;20(5):728-740. doi: 10.1016/S1470-2045(19)30098-1. Epub 2019 Apr 2.
Zotero 插件