Predictive Value of Radiomics-based Machine Learning for the Disease-free Survival in Breast Cancer: a Systematic Review and Meta-analysis

Overview

Journal Front Oncol

Specialty Oncology

Date 2023 Sep 4

PMID 37664048

Authors

Dongmei Lu

Yuke Yan

Min Jiang

Shaoqin Sun

Haifeng Jiang

Yashan Lu

Wenwen Zhang

Xing Zhou

Affiliations

Soon will be listed here.

Abstract

Purpose: This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy.

Materials And Methods: This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software.

Results: A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up.

Conclusion: This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance.

Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.

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References

Huang Y, Wei L, Hu Y, Shao N, Lin Y, He S . Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer. Front Oncol. 2021; 11:706733. PMC: 8416497. DOI: 10.3389/fonc.2021.706733. View

de Vries B, Zwezerijnen G, Burchell G, van Velden F, Menke-van der Houven van Oordt C, Boellaard R . Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Front Med (Lausanne). 2023; 10:1180773. PMC: 10213317. DOI: 10.3389/fmed.2023.1180773. View

Cho H, Kim H, Nam S, Lee J, Han B, Ko E . Measurement of Perfusion Heterogeneity within Tumor Habitats on Magnetic Resonance Imaging and Its Association with Prognosis in Breast Cancer Patients. Cancers (Basel). 2022; 14(8). PMC: 9025287. DOI: 10.3390/cancers14081858. View

Sun D, Li A, Tang B, Wang M . Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome. Comput Methods Programs Biomed. 2018; 161:45-53. DOI: 10.1016/j.cmpb.2018.04.008. View

Kim S, Kim M, Kim E, Yoon J, Park V . MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer. Sci Rep. 2020; 10(1):3750. PMC: 7048756. DOI: 10.1038/s41598-020-60822-9. View

Yu F, Hang J, Deng J, Yang B, Wang J, Ye X . Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study. Br J Radiol. 2021; 94(1126):20210188. PMC: 9328043. DOI: 10.1259/bjr.20210188. View

Mohebian M, Marateb H, Mansourian M, Mananas M, Mokarian F . A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning. Comput Struct Biotechnol J. 2016; 15:75-85. PMC: 5173316. DOI: 10.1016/j.csbj.2016.11.004. View

Lambin P, Leijenaar R, Deist T, Peerlings J, de Jong E, van Timmeren J . Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017; 14(12):749-762. DOI: 10.1038/nrclinonc.2017.141. View

Campbell P, Yachida S, Mudie L, Stephens P, Pleasance E, Stebbings L . The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature. 2010; 467(7319):1109-13. PMC: 3137369. DOI: 10.1038/nature09460. View

10.

Zhang L, Jiang X, Xie X, Wu Y, Zheng S, Tian W . The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC. Front Oncol. 2021; 10:523327. PMC: 7888274. DOI: 10.3389/fonc.2020.523327. View

11.

Chakraborty D, Ivan C, Amero P, Khan M, Rodriguez-Aguayo C, Basagaoglu H . Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer. Cancers (Basel). 2021; 13(14). PMC: 8303703. DOI: 10.3390/cancers13143450. View

12.

Wu X, Ye Y, Barcenas C, Chow W, Meng Q, Chavez-MacGregor M . Personalized Prognostic Prediction Models for Breast Cancer Recurrence and Survival Incorporating Multidimensional Data. J Natl Cancer Inst. 2017; 109(7). PMC: 6279311. DOI: 10.1093/jnci/djw314. View

13.

Choi W, Cha J, Kim H, Shin H, Chae E, Jung K . Long-term Survival Outcomes of Primary Breast Cancer in Women With or Without Preoperative Magnetic Resonance Imaging: A Matched Cohort Study. Clin Oncol (R Coll Radiol). 2017; 29(10):653-661. DOI: 10.1016/j.clon.2017.06.015. View

14.

Xia B, Wang H, Wang Z, Qian Z, Xiao Q, Liu Y . A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy. Front Genet. 2021; 12:783513. PMC: 8632946. DOI: 10.3389/fgene.2021.783513. View

15.

Li Q, Xiao Q, Li J, Duan S, Wang H, Gu Y . MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC. Cancer Manag Res. 2020; 12:10603-10613. PMC: 7602910. DOI: 10.2147/CMAR.S271876. View

16.

Kwan M, Kushi L, Weltzien E, Tam E, Castillo A, Sweeney C . Alcohol consumption and breast cancer recurrence and survival among women with early-stage breast cancer: the life after cancer epidemiology study. J Clin Oncol. 2010; 28(29):4410-6. PMC: 2988633. DOI: 10.1200/JCO.2010.29.2730. View

17.

Chan H, van der Velden B, Loo C, Gilhuijs K . Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI: a feasibility study. Phys Med Biol. 2017; 62(16):6467-6485. DOI: 10.1088/1361-6560/aa7dc5. View

18.

Siegel R, Miller K, Fuchs H, Jemal A . Cancer statistics, 2022. CA Cancer J Clin. 2022; 72(1):7-33. DOI: 10.3322/caac.21708. View

19.

de Boniface J, Frisell J, Bergkvist L, Andersson Y . Breast-conserving surgery followed by whole-breast irradiation offers survival benefits over mastectomy without irradiation. Br J Surg. 2018; 105(12):1607-1614. PMC: 6220856. DOI: 10.1002/bjs.10889. View

20.

Hayashi Y, Satake H, Ishigaki S, Ito R, Kawamura M, Kawai H . Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes. Br J Radiol. 2019; 93(1106):20190712. PMC: 7055451. DOI: 10.1259/bjr.20190712. View