将数据科学成果转化为精准肿瘤学决策：一篇综述短文
Translating Data Science Results into Precision Oncology Decisions: A Mini Review.
作者信息
Capobianco Enrico, Dominietto Marco
机构信息
The Jackson Laboratory, 10 Discovery Drive, Farmington, CT 06032, USA.
Paul Scherrer Institut, Forschungsstrasse 111, 5232 Villigen, Switzerland.
出版信息
J Clin Med. 2023 Jan 5;12(2):438. doi: 10.3390/jcm12020438.
Abstract
While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.
摘要
在回顾和讨论数据科学在肿瘤学中的潜力时，我们强调医学成像和放射组学是衡量人工智能（AI）和机器学习（ML）发展影响的主要背景框架。鉴于当前的障碍和局限性，我们设想了一些领域和研究方向，在这些领域和方向中，放射组学将变得更加重要。
相似文献
1
Translating Data Science Results into Precision Oncology Decisions: A Mini Review.将数据科学成果转化为精准肿瘤学决策：一篇综述短文
J Clin Med. 2023 Jan 5;12(2):438. doi: 10.3390/jcm12020438.
2
Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects.人工智能驱动的头颈部癌症放射组学：现状与未来展望。
Int J Med Inform. 2024 Aug;188:105464. doi: 10.1016/j.ijmedinf.2024.105464. Epub 2024 Apr 23.
3
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.
4
Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review.人工智能与放射组学在膀胱癌、肾癌和前列腺癌中的新趋势：批判性综述
Cancers (Basel). 2024 Feb 16;16(4):810. doi: 10.3390/cancers16040810.
5
A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.一篇关于人工智能和放射组学在肿瘤学中当前成像应用的叙述性综述：重点关注三种最常见的癌症。
Radiol Med. 2022 Aug;127(8):819-836. doi: 10.1007/s11547-022-01512-6. Epub 2022 Jun 30.
6
Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization.人工智能：肿瘤放射组学中的深度学习及其可解释性和数据协调的挑战。
Phys Med. 2021 Mar;83:108-121. doi: 10.1016/j.ejmp.2021.03.009. Epub 2021 Mar 22.
7
Molecular-based precision oncology clinical decision making augmented by artificial intelligence.基于分子的精准肿瘤学临床决策制定，辅以人为智能。
Emerg Top Life Sci. 2021 Dec 21;5(6):757-764. doi: 10.1042/ETLS20210220.
8
Machine Learning-Based Radiomics in Neuro-Oncology.基于机器学习的神经肿瘤放射组学。
Acta Neurochir Suppl. 2022;134:139-151. doi: 10.1007/978-3-030-85292-4_18.
9
Artificial intelligence in oncology: Path to implementation.人工智能在肿瘤学中的应用：实施之路。
Cancer Med. 2021 Jun;10(12):4138-4149. doi: 10.1002/cam4.3935. Epub 2021 May 7.
10
Challenges and perspectives in use of artificial intelligence to support treatment recommendations in clinical oncology.人工智能在临床肿瘤学中支持治疗建议应用的挑战与展望。
Cancer Med. 2024 Jun;13(12):e7398. doi: 10.1002/cam4.7398.
本文引用的文献
1
Reinforcement learning strategies in cancer chemotherapy treatments: A review.强化学习策略在癌症化疗治疗中的应用：综述。
Comput Methods Programs Biomed. 2023 Feb;229:107280. doi: 10.1016/j.cmpb.2022.107280. Epub 2022 Nov 26.
2
Big data in basic and translational cancer research.基础和转化癌症研究中的大数据。
Nat Rev Cancer. 2022 Nov;22(11):625-639. doi: 10.1038/s41568-022-00502-0. Epub 2022 Sep 5.
3
iRECIST and atypical patterns of response to immuno-oncology drugs.iRECIST 与免疫肿瘤药物反应的非典型模式。
J Immunother Cancer. 2022 Jun;10(6). doi: 10.1136/jitc-2022-004849.
4
Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations.人工智能在肿瘤学中的应用：当前能力、未来机遇和伦理考量。
Am Soc Clin Oncol Educ Book. 2022 Apr;42:1-10. doi: 10.1200/EDBK_350652.
5
Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer.基于计算机断层扫描的免疫生态多样性指数的开发和验证作为非小细胞肺癌的影像学生物标志物。
Eur Radiol. 2022 Dec;32(12):8726-8736. doi: 10.1007/s00330-022-08873-6. Epub 2022 May 31.
6
AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance.人工智能放射组学可以改善纳入标准和临床试验表现。
Tomography. 2022 Feb 2;8(1):341-355. doi: 10.3390/tomography8010028.
7
Reinforcement Learning for Precision Oncology.用于精准肿瘤学的强化学习
Cancers (Basel). 2021 Sep 15;13(18):4624. doi: 10.3390/cancers13184624.
8
Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients.计算机断层扫描影像组学特征作为肺腺癌患者免疫治疗选择生物标志物的初步报告
Cancers (Basel). 2021 Aug 7;13(16):3992. doi: 10.3390/cancers13163992.
9
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.基于人工智能的诊断和预后预测模型研究报告指南（TRIPOD-AI）和偏倚风险工具（PROBAST-AI）制定方案。
BMJ Open. 2021 Jul 9;11(7):e048008. doi: 10.1136/bmjopen-2020-048008.
10
Artificial intelligence in oncology: Path to implementation.人工智能在肿瘤学中的应用：实施之路。
Cancer Med. 2021 Jun;10(12):4138-4149. doi: 10.1002/cam4.3935. Epub 2021 May 7.
Zotero 插件