EXPLORING BREAST CANCER RESPONSE PREDICTION TO NEOADJUVANT SYSTEMIC THERAPY USING MRI-BASED RADIOMICS: A SYSTEMATIC REVIEW
DOI:
https://doi.org/10.53555/nnmhs.v9i5.1666Keywords:
Breast cancer, MRI-based radiomics, Neoadjuvant, Systemic therapyAbstract
Breast cancer affects more than one out of every ten persons diagnosed with cancer each year, making it the most common type of cancer in women. It is the second most common cause of cancer-related death among females worldwide. The milk-producing glands are placed in front of the chest wall, according to breast anatomy. Breast cancer progression is almost often missed. The majority of people find out they have the illness through a normal test. Others may present with an unintentional breast lump, a change in the shape or size of their breasts, or a discharge from their nips. When compared to adjuvant chemotherapy, NST allows for in vivo tumor response, tumor size reduction (allowing for breast-conserving therapy where mastectomy was recommended), and pathologic complete response (pCR). Breast cancer tumor response to NST can be predicted using imaging modalities. Breast magnetic resonance imaging (MRI) is the most effective imaging modality for evaluating tumors and predicting response. Its precision in evaluating and forecasting tumor response to NST is insufficient to change clinical treatment. NST tumor response cannot be predicted using pretreatment MRI. As a result, breast MRI accuracy must be continuously enhanced. Despite large methodological heterogeneity in each step of the radiomics workflow, studies focusing on MRI-based radiomics for tumor response prediction to NST in breast cancer patients yielded promising results.
References
Narod SA. Personalised medicine and population health: breast and ovarian cancer. Hum Genet. 2018 Oct;137(10):769–78.
Ban KA, Godellas CV. Epidemiology of breast cancer. Surg Oncol Clin N Am. 2014;23:409–22.
Bray F, Ferlay J, Soerjomataram I, et al, Bray F, Ferlay J, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.
Veronesi U, Boyle P, Goldhirsch A, Orecchia R, Viale G. Breast Cancer. Lancet. 2005 May;365(9472):1727–41.
Cain EH, Saha A, Harowicz MR, Marks JR, Marcom PK, Mazurowski MA. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set. Breast Cancer Res Treat. 2019;173:455–63.
Mahvi DA, Liu R, Grinstaff MW, Colson YL, Raut CP. Local Cancer Recurrence: The Realities, Challenges, and Opportunities for New Therapies. CA Cancer J Clin. 2018 Nov;68(6):488–505.
Siu AL. Screening for Breast Cancer: U.S. Preventive Services Task Force Recommendation Statement. Ann Intern Med. 2016 Feb;164(4):279–96.
Loibl S, Denkert C, von Minckwitz G. Neoadjuvant treatment of breast cancer–clinical and research perspective. The Breast. 2015;24:S73–7.
Thompson AM, Moulder-Thompson SL. Neoadjuvant treatment of breast cancer. Ann Oncol. 2012;23:x231–6.
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441–6.
Bitencourt AG V, Gibbs P, Rossi Saccarelli C, Daimiel I, Lo Gullo R, Fox MJ, et al. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer. EBioMedicine. 2020 Nov;61:103042.
Leithner D, Bernard-Davila B, Martinez DF, Horvat J V, Jochelson MS, Marino MA, et al. Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes. Mol imaging Biol. 2020 Apr;22(2):453–61.
Leithner D, Horvat J V, Marino MA, Bernard-Davila B, Jochelson MS, Ochoa-Albiztegui RE, et al. Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results. Breast Cancer Res. 2019 Sep;21(1):106.
Granzier RWY, Ibrahim A, Primakov SP, Samiei S, van Nijnatten TJA, de Boer M, et al. MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study. Cancers (Basel). 2021 May;13(10).
Liu Z, Li Z, Qu J, Zhang R, Zhou X, Li L, et al. Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study. Clin Cancer Res. 2019;25(12):3538–47.
Xiong Q, Zhou X, Liu Z, Lei C, Yang C, Yang M, et al. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy. Clin Transl Oncol. 2020;22:50–9.
Rutman AM, Kuo MD. Radiogenomics: Creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol [Internet]. 2009;70(2):232–41. Available from: https://www.sciencedirect.com/science/article/pii/S0720048X09000680
Rye IH, Trinh A, Saetersdal AB, Nebdal D, Lingjaerde OC, Almendro V, et al. Intratumor heterogeneity defines treatment-resistant HER2+ breast tumors. Mol Oncol. 2018 Nov;12(11):1838–55.
Metzger Filho O, Viale G, Trippa L, Li T, Yardley DA, Mayer IA, et al. HER2 heterogeneity as a predictor of response to neoadjuvant T-DM1 plus pertuzumab: Results from a prospective clinical trial. American Society of Clinical Oncology; 2019.
Hou Y, Nitta H, Wei L, Banks PM, Portier B, Parwani A V, et al. HER2 intratumoral heterogeneity is independently associated with incomplete response to anti-HER2 neoadjuvant chemotherapy in HER2-positive breast carcinoma. Breast Cancer Res Treat. 2017 Nov;166(2):447–57.
Fan M, Li H, Wang S, Zheng B, Zhang J, Li L. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS One. 2017;12(2):e0171683.
Pesapane F, Rotili A, Agazzi GM, Botta F, Raimondi S, Penco S, et al. Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future. Curr Oncol. 2021 Jun;28(4):2351–72.
Romeo V, Cuocolo R, Sanduzzi L, Carpentiero V, Caruso M, Lama B, et al. MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer. Vol. 15, Cancers. 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Journal of Advance Research in Medical & Health Science (ISSN: 2208-2425)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Licensing
Ninety Nine Publication publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
