真实世界数据与证据：精准肿瘤学的革新领域

姜晶鑫; 潘唯玮; 孙里杨; 庞立威; 陈海浪; 黄建; 陈武臻

doi:10.1631/jzus.B2400285

Your Location：

Home >

Browse articles >

真实世界数据与证据：精准肿瘤学的革新领域

Scan QR Code

文章被引用时，请邮件提醒。

Submit

Review | Updated：2026-01-27

真实世界数据与证据：精准肿瘤学的革新领域
Real-world data and evidence: pioneering frontiers in precision oncology
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2026年27卷第1期页码：44-57
Affiliations：

1.Department of Breast Surgery (Surgical Oncology), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
2.Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
3.Cancer Institute, Zhejiang University, Hangzhou 310009, China
4.Department of Neurosurgery, Lanxi People’s Hospital, Jinhua 413045, China
5.Department of Oncology, Lanxi People’s Hospital, Jinhua 413045, China
Funds：

the Zhejiang Provincial Natural Science Foundation(ZCLY24H1601);the National Natural Science Foundation of China(82403697);the Medical and Health Science and Technology Project of Zhejiang Province(2025KY411);the National Key R&D Program of China(2022YFC2505100)
DOI：10.1631/jzus.B2400285
中图分类号：
CSTR：
收稿：2024-06-03，

修回：2024-11-11，

网络出版：2025-12-22，

纸质出版：2026-01-27
Accepted：

姜晶鑫,潘唯玮,孙里杨等.真实世界数据与证据：精准肿瘤学的革新领域[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(01):44-57.

Jingxin JIANG, Weiwei PAN, Liyang SUN, et al. Real-world data and evidence: pioneering frontiers in precision oncology[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(1): 44-57.
姜晶鑫,潘唯玮,孙里杨等.真实世界数据与证据：精准肿瘤学的革新领域[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(01):44-57. DOI： 10.1631/jzus.B2400285.

Jingxin JIANG, Weiwei PAN, Liyang SUN, et al. Real-world data and evidence: pioneering frontiers in precision oncology[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(1): 44-57. DOI： 10.1631/jzus.B2400285.

摘要

真实世界研究（RWS）已成为肿瘤学研究领域中的一股革新力量，它通过提供在常规临床环境中实施肿瘤治疗的综合视角，有效补充了传统随机对照试验（RCT）的局限。本综述探讨了RWS在肿瘤领域中的发展现状，重点关注其具体实践、方法学考量以及对精准医疗的推动作用。本文系统分析了RWS如何整合电子健康记录（EHR）、保险索赔和患者登记等多源数据，生成真实世界证据，从而搭建起连接临床试验与真实世界临床实践的桥梁。本综述强调了RWS在精准肿瘤治疗中的贡献，包括评估在传统研究中被低估人群中的治疗结果、拓展药物适应症，以及在真实临床环境中肿瘤治疗的长期安全性和有效性。此外，我们还讨论了人工智能等新兴技术在应对数据质量变异和隐私问题等重大挑战中的应用潜力。总之，RWS与传统临床研究的整合正在革新精准肿瘤的研究范式，使基于真实世界证据的个性化治疗策略成为可能。

Abstract

Real-world studies (RWSs) have emerged as a transformative force in oncology research

complementing traditional randomized controlled trials (RCTs) by providing comprehensive insights into cancer care within routine clinical settings. This review examines the evolving landscape of RWSs in oncology

focusing on their implementation

methodological considerations

and impact on precision medicine. We systematically analyze how RWSs leverage diverse data sources

including electronic health records (EHRs)

insurance claims

and patient registries

to generate evidence that bridges the gap between controlled clinical trials and real-world clinical practice. The review underscores the key contributions of RWSs

including capturing therapeutic outcomes in traditionally underrepresented populations

expanding drug indications

and evaluating long-term safety and effectiveness in routine clinical settings. While acknowledging significant challenges

including data quality variability and privacy concerns

we discuss how emerging technologies like artificial intelligence are helping to address these limitations. The integration of RWSs with traditional clinical research is revolutionizing the paradigm of precision oncology and enabling more personalized treatment approaches based on real-world evidence.

关键词

Keywords

references

Agiro A , DeVries A , Malin J , et al. , 2018 . Real-world impact of a decision support tool on colony-stimulating factor use and chemotherapy-induced febrile neutropenia among patients with breast cancer . J Natl Compr Canc Netw , 16 ( 2 ): 162 - 169 . https://doi.org/10.6004/jnccn.2017.7033 https://doi.org/10.6004/jnccn.2017.7033

Anwar M , Chen QT , Ouyang DJ , et al. , 2021 . Pyrotinib treatment in patients with HER2-positive metastatic breast cancer and brain metastasis: exploratory final analysis of real-world, multicenter data . Clin Cancer Res , 27 ( 16 ): 4634 - 4641 . https://doi.org/10.1158/1078-0432.CCR-21-0474 https://doi.org/10.1158/1078-0432.CCR-21-0474

Arlett P , Kjær J , Broich K , et al. , 2022 . Real-world evidence in EU medicines regulation: enabling use and establishing value . Clin Pharmacol Ther , 111 ( 1 ): 21 - 23 . https://doi.org/10.1002/cpt.2479 https://doi.org/10.1002/cpt.2479

Arondekar B , Duh MS , Bhak RH , et al. , 2022 . Real-world evidence in support of oncology product registration: a systematic review of new drug application and biologics license application approvals from 2015-2020 . Clin Cancer Res , 28 ( 1 ): 27 - 35 . https://doi.org/10.1158/1078-0432.CCR-21-2639 https://doi.org/10.1158/1078-0432.CCR-21-2639

Bankhead P , Loughrey MB , Fernández JA , et al. , 2017 . QuPath: open source software for digital pathology image analysis . Sci Rep , 7 : 16878 . https://doi.org/10.1038/s41598-017-17204-5 https://doi.org/10.1038/s41598-017-17204-5

Basulaiman B , Awan AA , Fergusson D , et al. , 2019 . Creating a pragmatic trials program for breast cancer patients: rethinking clinical trials (REaCT) . Breast Cancer Res Treat , 177 ( 1 ): 93 - 101 . https://doi.org/10.1007/s10549-019-05274-0 https://doi.org/10.1007/s10549-019-05274-0

Baumfeld Andre E , Reynolds R , Caubel P , et al. , 2020 . Trial designs using real-world data: the changing landscape of the regulatory approval process . Pharmacoepidemiol Drug Saf , 29 ( 10 ): 1201 - 1212 . https://doi.org/10.1002/pds.4932 https://doi.org/10.1002/pds.4932

Berger M , Daniel G , Frank K , et al. , 2017 . A Framework for Regulatory Use of Real-World Evidence . Duke Margolis Center for Health Policy , Washington , DC, USA .

Black N , 1996 . Why we need observational studies to evaluate the effectiveness of health care . BMJ , 312 ( 7040 ): 1215 - 1218 . https://doi.org/10.1136/bmj.312.7040.1215 https://doi.org/10.1136/bmj.312.7040.1215

Booth CM , Karim S , Mackillop WJ , 2019 . Real-world data: towards achieving the achievable in cancer care . Nat Rev Clin Oncol , 16 ( 5 ): 312 - 325 . https://doi.org/10.1038/s41571-019-0167-7 https://doi.org/10.1038/s41571-019-0167-7

Chakiryan NH , Jiang DD , Gillis KA , et al. , 2021 . Real-world survival outcomes associated with first-line immunotherapy, targeted therapy, and combination therapy for metastatic clear cell renal cell carcinoma . JAMA Netw Open , 4 ( 5 ): e2111329 . https://doi.org/10.1001/jamanetworkopen.2021.11329 https://doi.org/10.1001/jamanetworkopen.2021.11329

Chen XX , Wang XM , Zhang K , et al. , 2022 . Recent advances and clinical applications of deep learning in medical image analysis . Med Image Anal , 79 : 102444 . https://doi.org/10.1016/j.media.2022.102444 https://doi.org/10.1016/j.media.2022.102444

Chlebowski RT , Hendrix SL , Langer RD , et al. , 2003 . Influence of estrogen plus progestin on breast cancer and mammography in healthy postmenopausal women: the women’s health initiative randomized trial . JAMA , 289 ( 24 ): 3243 - 3253 . https://doi.org/10.1001/jama.289.24.3243 https://doi.org/10.1001/jama.289.24.3243

Cluze C , Rey D , Huiart L , et al. , 2012 . Adjuvant endocrine therapy with tamoxifen in young women with breast cancer: determinants of interruptions vary over time . Ann Oncol , 23 ( 4 ): 882 - 890 . https://doi.org/10.1093/annonc/mdr330 https://doi.org/10.1093/annonc/mdr330

Collaborative Group on Hormonal Factors in Breast Cancer , 1997 . Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52 705 women with breast cancer and 108 411 women without breast cancer . Lancet , 350 ( 9084 ): 1047 - 1059 . https://doi.org/10.1016/S0140-6736(97)08233-0 https://doi.org/10.1016/S0140-6736(97)08233-0

Collins R , Bowman L , Landray M , et al. , 2020 . The magic of randomization versus the myth of real-world evidence . N Engl J Med , 382 ( 7 ): 674 - 678 . https://doi.org/10.1056/NEJMsb1901642 https://doi.org/10.1056/NEJMsb1901642

Cook JA , Collins GS , 2015 . The rise of big clinical databases . Br J Surg , 102 ( 2 ): e93 - e101 . https://doi.org/10.1002/bjs.9723 https://doi.org/10.1002/bjs.9723

Corrigan-Curay J , 2018 . Framework for FDA’s Real-World Evidence Program . U.S. Food and Drug Administration, Silver Spring. https://www.fda.gov/media/123160/download https://www.fda.gov/media/123160/download [ accessed on Nov. 25, 2024 ] .

Dai WF , Beca JM , Nagamuthu C , et al. , 2022a . Comparative effectiveness and safety of pertuzumab and trastuzumab plus chemotherapy vs trastuzumab plus chemotherapy for treatment of metastatic breast cancer . JAMA Netw Open , 5 ( 2 ): e2145460 . https://doi.org/10.1001/jamanetworkopen.2021.45460 https://doi.org/10.1001/jamanetworkopen.2021.45460

Dai WF , Beca JM , Nagamuthu C , et al. , 2022b . Cost-effectiveness analysis of pertuzumab with trastuzumab in patients with metastatic breast cancer . JAMA Oncol , 8 ( 4 ): 597 - 606 . https://doi.org/10.1001/jamaoncol.2021.8049 https://doi.org/10.1001/jamaoncol.2021.8049

Derksen JWG , May AM , Koopman M , 2019 . The era of alternative designs to connect randomized clinical trials and real-world data . Nat Rev Clin Oncol , 16 ( 9 ): 589 . https://doi.org/10.1038/s41571-019-0250-0 https://doi.org/10.1038/s41571-019-0250-0

Diéras V , Miles D , Verma S , et al. , 2017 . Trastuzumab emtansine versus capecitabine plus lapatinib in patients with previously treated HER2-positive advanced breast cancer (EMILIA): a descriptive analysis of final overall survival results from a randomised, open-label, phase 3 trial . Lancet Oncol , 18 ( 6 ): 732 - 742 . https://doi.org/10.1016/S1470-2045(17)30312-1 https://doi.org/10.1016/S1470-2045(17)30312-1

Dinan MA , Wilson LE , Reed SD , 2019 . Chemotherapy costs and 21-gene recurrence score genomic testing among medicare beneficiaries with early-stage breast cancer, 2005 to 2011 . J Natl Compr Canc Netw , 17 ( 3 ): 245 - 254 . https://doi.org/10.6004/jnccn.2018.7097 https://doi.org/10.6004/jnccn.2018.7097

Duggento A , Conti A , Mauriello A , et al. , 2021 . Deep computational pathology in breast cancer . Semin Cancer Biol , 72 : 226 - 237 . https://doi.org/10.1016/j.semcancer.2020.08.006 https://doi.org/10.1016/j.semcancer.2020.08.006

Eggermont AMM , Apolone G , Baumann M , et al. , 2019 . Cancer Core Europe: a translational research infrastructure for a European mission on cancer . Mol Oncol , 13 ( 3 ): 521 - 527 . https://doi.org/10.1002/1878-0261.12447 https://doi.org/10.1002/1878-0261.12447

Ethier JL , Desautels D , Robinson A , et al. , 2021 . Practice patterns and outcomes of novel targeted agents for the treatment of ERBB2 -positive metastatic breast cancer . JAMA Oncol , 7 ( 9 ): e212140 . https://doi.org/10.1001/jamaoncol.2021.2140 https://doi.org/10.1001/jamaoncol.2021.2140

Farkas AH , Nattinger AB , 2023 . Breast cancer screening and prevention . Ann Intern Med , 176 ( 11 ): ITC161 - ITC176 . https://doi.org/10.7326/AITC202311210 https://doi.org/10.7326/AITC202311210

Flynn R , Plueschke K , Quinten C , et al. , 2022 . Marketing authorization applications made to the European medicines agency in 2018‒2019: what was the contribution of real-world evidence? Clin Pharmacol Ther , 111 ( 1 ): 90 - 97 . https://doi.org/10.1002/cpt.2461 https://doi.org/10.1002/cpt.2461

Franklin JM , Schneeweiss S , 2017 . When and how can real world data analyses substitute for randomized controlled trials? Clin Pharmacol Ther , 102 ( 6 ): 924 - 933 . https://doi.org/10.1002/cpt.857 https://doi.org/10.1002/cpt.857

Gennaro G , Bucchi L , Ravaioli A , et al. , 2024 . The risk-based breast screening (RIBBS) study protocol: a personalized screening model for young women . Radiol Med , 129 ( 5 ): 727 - 736 . https://doi.org/10.1007/s11547-024-01797-9 https://doi.org/10.1007/s11547-024-01797-9

Global Burden of Disease Cancer Collaboration , 2019 . Global , regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2017: a systematic analysis for the global burden of disease study . JAMA Oncol, 5 ( 12 ): 1749 - 1768 . https://doi.org/10.1001/jamaoncol.2019.2996 https://doi.org/10.1001/jamaoncol.2019.2996

Global Burden of Disease 2019 Cancer Collaboration , 2022 . Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019. JAMA Oncol, 8 ( 3 ): 420 - 444 . https://doi.org/10.1001/jamaoncol.2021.6987 https://doi.org/10.1001/jamaoncol.2021.6987

Gross AJ , Pisano CE , Khunsriraksakul C , et al. , 2023 . Real-world data: applications and relevance to cancer clinical trials . Semin Radiat Oncol , 33 ( 4 ): 374 - 385 . https://doi.org/10.1016/j.semradonc.2023.06.003 https://doi.org/10.1016/j.semradonc.2023.06.003

Hripcsak G , Duke JD , Shah NH , et al. , 2015 . Observational health data sciences and informatics (OHDSI): opportunities for observational researchers . Stud Health Technol Inform , 216 : 574 - 578 . https://doi.org/10.3233/978-1-61499-564-7-574 https://doi.org/10.3233/978-1-61499-564-7-574

Hsu WH , Ko AT , Weng CS , et al. , 2023 . Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer . J Cachexia Sarcopenia Muscle , 14 ( 5 ): 2044 - 2053 . https://doi.org/10.1002/jcsm.13282 https://doi.org/10.1002/jcsm.13282

Jarow JP , LaVange L , Woodcock J , 2017 . Multidimensional evidence generation and FDA regulatory decision making: defining and using “real-world” data . JAMA , 318 ( 8 ): 703 - 704 . https://doi.org/10.1001/jama.2017.9991 https://doi.org/10.1001/jama.2017.9991

Jazieh AR , Onal HC , Tan DSW , et al. , 2021 . Real-world treatment patterns and clinical outcomes in patients with stage III NSCLC: results of KINDLE, a multicountry observational study . J Thorac Oncol , 16 ( 10 ): 1733 - 1744 . https://doi.org/10.1016/j.jtho.2021.05.003 https://doi.org/10.1016/j.jtho.2021.05.003

Jiang XY , Hu ZJ , Wang SH , et al. , 2023 . Deep learning for medical image-based cancer diagnosis . Cancers , 15 ( 14 ): 3608 . https://doi.org/10.3390/cancers15143608 https://doi.org/10.3390/cancers15143608

Kantarjian H , Stein A , Gökbuget N , et al. , 2017 . Blinatumomab versus chemotherapy for advanced acute lymphoblastic leukemia . N Engl J Med , 376 ( 9 ): 836 - 847 . https://doi.org/10.1056/NEJMoa1609783 https://doi.org/10.1056/NEJMoa1609783

Karakatsanis A , Markopoulos C , 2020 . The challenge of avoiding over- and under-treatment in older women with ductal cancer in situ: a scoping review of existing knowledge gaps and a meta-analysis of real-world practice patterns . J Geriatr Oncol , 11 ( 6 ): 917 - 925 . https://doi.org/10.1016/j.jgo.2020.02.005 https://doi.org/10.1016/j.jgo.2020.02.005

Kennedy-Martin T , Curtis S , Faries D , et al. , 2015 . A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results . Trials , 16 : 495 . https://doi.org/10.1186/s13063-015-1023-4 https://doi.org/10.1186/s13063-015-1023-4

Khozin S , Blumenthal GM , Pazdur R , 2017 . Real-world data for clinical evidence generation in oncology . J Natl Cancer Inst , 109 ( 11 ): djx187 . https://doi.org/10.1093/jnci/djx187 https://doi.org/10.1093/jnci/djx187

Kim HS , 2024 . Dark data in real-world evidence: challenges, implications, and the imperative of data literacy in medical research . J Korean Med Sci , 39 ( 9 ): e92 . https://doi.org/10.3346/jkms.2024.39.e92 https://doi.org/10.3346/jkms.2024.39.e92

Kirkham AA , Bland KA , Wollmann H , et al. , 2019 . Maintenance of fitness and quality-of-life benefits from supervised exercise offered as supportive care for breast cancer . J Natl Compr Canc Netw , 17 ( 6 ): 695 - 702 . https://doi.org/10.6004/jnccn.2018.7276 https://doi.org/10.6004/jnccn.2018.7276

Kleppe A , Skrede OJ , de Raedt S , et al. , 2021 . Designing deep learning studies in cancer diagnostics . Nat Rev Cancer , 21 ( 3 ): 199 - 211 . https://doi.org/10.1038/s41568-020-00327-9 https://doi.org/10.1038/s41568-020-00327-9

Kovačević A , Bašaragin B , Milošević N , et al. , 2024 . De-identification of clinical free text using natural language processing: a systematic review of current approaches . Artif Intell Med , 151 : 102845 . https://doi.org/10.1016/j.artmed.2024.102845 https://doi.org/10.1016/j.artmed.2024.102845

Li XC , Zhang S , Zhang Q , et al. , 2019 . Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study . Lancet Oncol , 20 ( 2 ): 193 - 201 . https://doi.org/10.1016/S1470-2045(18)30762-9 https://doi.org/10.1016/S1470-2045(18)30762-9

Liao WQ , Coupland CAC , Burchardt J , et al. , 2023 . Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19.67 million people and evaluation of model performance against seven other risk prediction models . Lancet Respir Med , 11 ( 8 ): 685 - 697 . https://doi.org/10.1016/S2213-2600(23)00050-4 https://doi.org/10.1016/S2213-2600(23)00050-4

Lin AP , Huang TW , Tam KW , 2021 . Treatment of male breast cancer: meta-analysis of real-world evidence . Br J Surg , 108 ( 9 ): 1034 - 1042 . https://doi.org/10.1093/bjs/znab279 https://doi.org/10.1093/bjs/znab279

Lu YT , Chu Q , Li Z , et al. , 2024 . Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer . Brief Bioinform , 25 ( 2 ): bbae071 . https://doi.org/10.1093/bib/bbae071 https://doi.org/10.1093/bib/bbae071

Lu ZH , Sim JA , Wang JX , et al. , 2021 . Natural language processing and machine learning methods to characterize unstructured patient-reported outcomes: validation study . J Med Internet Res , 23 ( 11 ): e26777 . https://doi.org/10.2196/26777 https://doi.org/10.2196/26777

Malmberg R , Zietse M , Dumoulin DW , et al. , 2022 . Alternative dosing strategies for immune checkpoint inhibitors to improve cost-effectiveness: a special focus on nivolumab and pembrolizumab . Lancet Oncol , 23 ( 12 ): e552 - e561 . https://doi.org/10.1016/S1470-2045(22)00554-X https://doi.org/10.1016/S1470-2045(22)00554-X

Malone DC , Brown M , Hurwitz JT , et al. , 2018 . Real-world evidence: useful in the real world of us payer decision making? How? When? And What studies? Value Health , 21 ( 3 ): 326 - 333 . https://doi.org/10.1016/j.jval.2017.08.3013 https://doi.org/10.1016/j.jval.2017.08.3013

Martin P , Cohen JB , Wang M , et al. , 2023 . Treatment outcomes and roles of transplantation and maintenance rituximab in patients with previously untreated mantle cell lymphoma: results from large real-world cohorts . J Clin Oncol , 41 ( 3 ): 541 - 554 . https://doi.org/10.1200/JCO.21.02698 https://doi.org/10.1200/JCO.21.02698

McCarney R , Warner J , Iliffe S , et al. , 2007 . The Hawthorne effect: a randomised, controlled trial . BMC Med Res Methodol , 7 : 30 . https://doi.org/10.1186/1471-2288-7-30 https://doi.org/10.1186/1471-2288-7-30

Merola D , Campbell U , Gautam N , et al. , 2023 . The aetion coalition to advance real-world evidence through randomized controlled trial emulation initiative: oncology . Clin Pharmacol Ther , 113 ( 6 ): 1217 - 1222 . https://doi.org/10.1002/cpt.2800 https://doi.org/10.1002/cpt.2800

Miksad RA , Abernethy AP , 2018 . Harnessing the power of real-world evidence (RWE): a checklist to ensure regulatory-grade data quality . Clin Pharmacol Ther , 103 ( 2 ): 202 - 205 . https://doi.org/10.1002/cpt.946 https://doi.org/10.1002/cpt.946

Moore S , Leung B , Wu J , et al. , 2019 . Real-world treatment of stage III NSCLC: the role of trimodality treatment in the era of immunotherapy . J Thorac Oncol , 14 ( 8 ): 1430 - 1439 . https://doi.org/10.1016/j.jtho.2019.04.005 https://doi.org/10.1016/j.jtho.2019.04.005

Newman LA , 2022 . Breast cancer screening in low and middle-income countries . Best Pract Res Clin Obstet Gynaecol , 83 : 15 - 23 . https://doi.org/10.1016/j.bpobgyn.2022.03.018 https://doi.org/10.1016/j.bpobgyn.2022.03.018

Núñez ER , Caverly TJ , Zhang SQ , et al. , 2021 . Adherence to follow-up testing recommendations in US veterans screened for lung cancer, 2015-2019 . JAMA Netw Open , 4 ( 7 ): e2116233 . https://doi.org/10.1001/jamanetworkopen.2021.16233 https://doi.org/10.1001/jamanetworkopen.2021.16233

Patil VM , Noronha V , Menon N , et al. , 2023 . Low-dose immunotherapy in head and neck cancer: a randomized study . J Clin Oncol , 41 ( 2 ): 222 - 232 . https://doi.org/10.1200/JCO.22.01015 https://doi.org/10.1200/JCO.22.01015

Penberthy LT , Rivera DR , Lund JL , et al. , 2022 . An overview of real-world data sources for oncology and considerations for research . CA Cancer J Clin , 72 ( 3 ): 287 - 300 . https://doi.org/10.3322/caac.21714 https://doi.org/10.3322/caac.21714

Perez-Lopez R , Ghaffari Laleh N , Mahmood F , et al. , 2024 . A guide to artificial intelligence for cancer researchers . Nat Rev Cancer , 24 ( 6 ): 427 - 441 . https://doi.org/10.1038/s41568-024-00694-7 https://doi.org/10.1038/s41568-024-00694-7

Poirion OB , Jing Z , Chaudhary K , et al. , 2021 . DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data . Genome Med , 13 : 112 . https://doi.org/10.1186/s13073-021-00930-x https://doi.org/10.1186/s13073-021-00930-x

Pollard S , Weymann D , Chan B , et al. , 2022 . Defining a core data set for the economic evaluation of precision oncology . Value Health , 25 ( 8 ): 1371 - 1380 . https://doi.org/10.1016/j.jval.2022.01.005 https://doi.org/10.1016/j.jval.2022.01.005

Prelaj A , Miskovic V , Zanitti M , et al. , 2024 . Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review . Ann Oncol , 35 ( 1 ): 29 - 65 . https://doi.org/10.1016/j.annonc.2023.10.125 https://doi.org/10.1016/j.annonc.2023.10.125

Przepiorka D , Ko CW , Deisseroth A , et al. , 2015 . FDA approval: blinatumomab . Clin Cancer Res , 21 ( 18 ): 4035 - 4039 . https://doi.org/10.1158/1078-0432.CCR-15-0612 https://doi.org/10.1158/1078-0432.CCR-15-0612

Qaiser T , Rajpoot NM , 2019 . Learning where to see: a novel attention model for automated immunohistochemical scoring . IEEE Trans Med Imaging , 38 ( 11 ): 2620 - 2631 . https://doi.org/10.1109/TMI.2019.2907049 https://doi.org/10.1109/TMI.2019.2907049

Roshandel G , Badar F , Barchuk A , et al. , 2023 . REPCAN: guideline for REporting population-based CANcer registry data . Asian Pac J Cancer Prev , 24 ( 9 ): 3297 - 3303 . https://doi.org/10.31557/APJCP.2023.24.9.3297 https://doi.org/10.31557/APJCP.2023.24.9.3297

Samlowski W , Robert NJ , Chen LW , et al. , 2023 . Real-world nivolumab dosing patterns and safety outcomes in patients receiving adjuvant therapy for melanoma . Cancer Med , 12 ( 3 ): 2378 - 2388 . https://doi.org/10.1002/cam4.5061 https://doi.org/10.1002/cam4.5061

Sherman RE , Anderson SA , Dal Pan GJ , et al. , 2016 . Real-world evidence — what is it and what can it tell us? N Engl J Med , 375 ( 23 ): 2293 - 2297 . https://doi.org/10.1056/NEJMsb1609216 https://doi.org/10.1056/NEJMsb1609216

Sim JA , Huang XL , Horan MR , et al. , 2023 . Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: a systematic review . Artif Intell Med , 146 : 102701 . https://doi.org/10.1016/j.artmed.2023.102701 https://doi.org/10.1016/j.artmed.2023.102701

Sinha S , Laskar SG , Wadasadawala T , et al. , 2022 . Adopting health economic research in radiation oncology: a perspective from low- or middle-income countries . JCO Glob Oncol , 8 : e2100374 . https://doi.org/10.1200/GO.21.00374 https://doi.org/10.1200/GO.21.00374

Skovlund E , Leufkens HGM , Smyth JF , 2018 . The use of real-world data in cancer drug development . Eur J Cancer , 101 : 69 - 76 . https://doi.org/10.1016/j.ejca.2018.06.036 https://doi.org/10.1016/j.ejca.2018.06.036

Soliman R , Oke J , Sidhom I , et al. , 2023 . Cost-effectiveness of childhood cancer treatment in Egypt: lessons to promote high-value care in a resource-limited setting based on real-world evidence . eClinicalMedicine , 55 : 101729 . https://doi.org/10.1016/j.eclinm.2022.101729 https://doi.org/10.1016/j.eclinm.2022.101729

Stabellini N , Cao LF , Towe CW , et al. , 2023 . Validation of the PREDICT prognostication tool in US patients with breast cancer . J Natl Compr Canc Netw , 21 ( 10 ): 1011 - 1019.e6 . https://doi.org/10.6004/jnccn.2023.7048 https://doi.org/10.6004/jnccn.2023.7048

Stein-O'Brien GL , Le DT , Jaffee EM , et al. , 2023 . Converging on a cure: the roads to predictive immunotherapy . Cancer Discov , 13 ( 5 ): 1053 - 1057 . https://doi.org/10.1158/2159-8290.CD-23-0277 https://doi.org/10.1158/2159-8290.CD-23-0277

Tang M , Pearson SA , Simes RJ , et al. , 2023 . Harnessing real-world evidence to advance cancer research . Curr Oncol , 30 ( 2 ): 1844 - 1859 . https://doi.org/10.3390/curroncol30020143 https://doi.org/10.3390/curroncol30020143

Terranova N , Venkatakrishnan K , 2024 . Machine learning in modeling disease trajectory and treatment outcomes: an emerging enabler for model-informed precision medicine . Clin Pharmacol Ther , 115 ( 4 ): 720 - 726 . https://doi.org/10.1002/cpt.3153 https://doi.org/10.1002/cpt.3153

Timmins IR , Jones ME , O'Brien KM , et al. , 2024 . International pooled analysis of leisure-time physical activity and premenopausal breast cancer in women from 19 cohorts . J Clin Oncol , 42 ( 8 ): 927 - 939 . https://doi.org/10.1200/JCO.23.01101 https://doi.org/10.1200/JCO.23.01101

Topp MS , Gökbuget N , Stein AS , et al. , 2015 . Safety and activity of blinatumomab for adult patients with relapsed or refractory B-precursor acute lymphoblastic leukaemia: a multicentre, single-arm, phase 2 study . Lancet Oncol , 16 ( 1 ): 57 - 66 . https://doi.org/10.1016/S1470-2045(14)71170-2 https://doi.org/10.1016/S1470-2045(14)71170-2

Tortora M , Cordelli E , Sicilia R , et al. , 2021 . Deep reinforcement learning for fractionated radiotherapy in non-small cell lung carcinoma . Artif Intell Med , 119 : 102137 . https://doi.org/10.1016/j.artmed.2021.102137 https://doi.org/10.1016/j.artmed.2021.102137

Trentham-Dietz A , Chapman CH , Jayasekera J , et al. , 2024 . Collaborative modeling to compare different breast cancer screening strategies: a decision analysis for the US preventive services task force . JAMA , 331 ( 22 ): 1947 - 1960 . https://doi.org/10.1001/jama.2023.24766 https://doi.org/10.1001/jama.2023.24766

Tsai CJ , Riaz N , Gomez SL , 2019 . Big data in cancer research: real-world resources for precision oncology to improve cancer care delivery . Semin Radiat Oncol , 29 ( 4 ): 306 - 310 . https://doi.org/10.1016/j.semradonc.2019.05.002 https://doi.org/10.1016/j.semradonc.2019.05.002

U . S . Food and Drug Administration , 2023 . Using the PICOTS Framework to Strengthen Evidence Gathered in Clinical Trials—Guidance from the AHRQ’s Evidence-based Practice Centers Program. U.S. Food and Drug Administration, Silver Spring. https://www.fda.gov/media/109448/download https://www.fda.gov/media/109448/download [ accessed on Nov. 25, 2024 ] .

van den Puttelaar R , Meester RGS , Peterse EFP , et al. , 2023 . Risk-stratified screening for colorectal cancer using genetic and environmental risk factors: a cost-effectiveness analysis based on real-world data . Clin Gastroenterol Hepatol , 21 ( 13 ): 3415 - 3423.e29 . https://doi.org/10.1016/j.cgh.2023.03.003 https://doi.org/10.1016/j.cgh.2023.03.003

Veitch Z , Khan OF , Tilley D , et al. , 2019 . Real-world outcomes of adjuvant chemotherapy for node-negative and node-positive HER2-positive breast cancer . J Natl Compr Canc Netw , 17 ( 1 ): 47 - 56 . https://doi.org/10.6004/jnccn.2018.7066 https://doi.org/10.6004/jnccn.2018.7066

Velmovitsky PE , Bevilacqua T , Alencar P , et al. , 2021 . Convergence of precision medicine and public health into precision public health: toward a big data perspective . Front Public Health , 9 : 561873 . https://doi.org/10.3389/fpubh.2021.561873 https://doi.org/10.3389/fpubh.2021.561873

Verkerk K , Voest EE , 2024 . Generating and using real-world data: a worthwhile uphill battle . Cell , 187 ( 7 ): 1636 - 1650 . https://doi.org/10.1016/j.cell.2024.02.012 https://doi.org/10.1016/j.cell.2024.02.012

Voelker RA , 2023 . Cervical cancer screening . JAMA , 330 ( 20 ): 2030 . https://doi.org/10.1001/jama.2023.21987 https://doi.org/10.1001/jama.2023.21987

Vorisek CN , Lehne M , Klopfenstein SAI , et al. , 2022 . Fast healthcare interoperability resources (FHIR) for interoperability in health research: systematic review . JMIR Med Inform , 10 ( 7 ): e35724 . https://doi.org/10.2196/35724 https://doi.org/10.2196/35724

Wang SV , Pinheiro S , Hua W , et al. , 2021 . STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies . BMJ , 372 : m4856 . https://doi.org/10.1136/bmj.m4856 https://doi.org/10.1136/bmj.m4856

Wang X , Feng KX , Wang WY , et al. , 2022 . Long-term outcomes of intraoperative radiotherapy for early-stage breast cancer in China: a multicenter real-world study . Cancer Commun , 42 ( 3 ): 277 - 280 . https://doi.org/10.1002/cac2.12258 https://doi.org/10.1002/cac2.12258

Wang YS , 2024 . 123P Goserelin 10.8 mg and 3.6 mg depots in breast cancer (BC): a large real-world noninferiority study . ESMO Open , 9 ( S4 ): 103111 . https://doi.org/10.1016/j.esmoop.2024.103111 https://doi.org/10.1016/j.esmoop.2024.103111

Wedam S , Fashoyin-Aje L , Bloomquist E , et al. , 2020 . FDA approval summary: palbociclib for male patients with metastatic breast cancer . Clin Cancer Res , 26 ( 6 ): 1208 - 1212 . https://doi.org/10.1158/1078-0432.CCR-19-2580 https://doi.org/10.1158/1078-0432.CCR-19-2580

Wu SY , Li JW , Wang YJ , et al. , 2023 . Clinical feasibility and oncological safety of non-radioactive targeted axillary dissection after neoadjuvant chemotherapy in biopsy-proven node-positive breast cancer: a prospective diagnostic and prognostic study . Int J Surg , 109 ( 7 ): 1863 - 1870 . https://doi.org/10.1097/JS9.0000000000000331 https://doi.org/10.1097/JS9.0000000000000331

Xu C , Song YH , Zhang D , et al. , 2023 . Spatiotemporal knowledge teacher-student reinforcement learning to detect liver tumors without contrast agents . Med Image Anal , 90 : 102980 . https://doi.org/10.1016/j.media.2023.102980 https://doi.org/10.1016/j.media.2023.102980

Xu JY , Wu WK , Zhang X , et al. , 2024 . The use of real-world evidence for regulatory decisions in China . Clin Pharmacol Ther , 116 ( 1 ): 82 - 95 . https://doi.org/10.1002/cpt.3257 https://doi.org/10.1002/cpt.3257

Yala A , Mikhael PG , Lehman C , et al. , 2022 . Optimizing risk-based breast cancer screening policies with reinforcement learning . Nat Med , 28 ( 1 ): 136 - 143 . https://doi.org/10.1038/s41591-021-01599-w https://doi.org/10.1038/s41591-021-01599-w

Yan YT , Lv R , Wang TY , et al. , 2023 . Real-world treatment patterns, discontinuation and clinical outcomes in patients with B-cell lymphoproliferative diseases treated with BTK inhibitors in China . Front Immunol , 14 : 1184395 . https://doi.org/10.3389/fimmu.2023.1184395 https://doi.org/10.3389/fimmu.2023.1184395

Yim WW , Yetisgen M , Harris WP , et al. , 2016 . Natural language processing in oncology: a review . JAMA Oncol , 2 ( 6 ): 797 - 804 . https://doi.org/10.1001/jamaoncol.2016.0213 https://doi.org/10.1001/jamaoncol.2016.0213

Zhang JD , Wu JJ , Zhou XS , et al. , 2023 . Recent advancements in artificial intelligence for breast cancer: image augmentation, segmentation, diagnosis, and prognosis approaches . Semin Cancer Biol , 96 : 11 - 25 . https://doi.org/10.1016/j.semcancer.2023.09.001 https://doi.org/10.1016/j.semcancer.2023.09.001

Zhao YF , Kosorok MR , Zeng DL , 2009 . Reinforcement learning design for cancer clinical trials . Stat Med , 28 ( 26 ): 3294 - 3315 . https://doi.org/10.1002/sim.3720 https://doi.org/10.1002/sim.3720

Zhu R , Vora B , Menon S , et al. , 2023 . Clinical pharmacology applications of real-world data and real-world evidence in drug development and approval‒an industry perspective . Clin Pharmacol Ther , 114 ( 4 ): 751 - 767 . https://doi.org/10.1002/cpt.2988 https://doi.org/10.1002/cpt.2988

浏览量

Downloads

CSCD

工具集

关联资源

暂无数据