Top

Published in:

01-01-2020 | Magnetic Resonance Imaging | Research Article

Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy

Authors: Qianqian Xiong, Xuezhi Zhou, Zhenyu Liu, Chuqian Lei, Ciqiu Yang, Mei Yang, Liulu Zhang, Teng Zhu, Xiaosheng Zhuang, Changhong Liang, Zaiyi Liu, Jie Tian, Kun Wang

Published in: Clinical and Translational Oncology | Issue 1/2020

Abstract

Purpose

To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC).

Methods

A total of 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller–Payne grading system was applied to assess the response to NAC. Grade 1–2 cases were classified as insensitive to NAC. We extracted 1941 features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation.

Results

Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1–2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848–1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis.

Conclusion

The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.

Available only for authorised users

Kaufmann M, von Minckwitz G, Smith R, Valero V, Gianni L, Eiermann W, et al. International expert panel on the use of primary (preoperative) systemic treatment of operable breast cancer: review and recommendations. J Clin Oncol. 2003;21(13):2600–8. https://doi.org/10.1200/jco.2003.01.136.CrossRefPubMed

Kaufmann M, Hortobagyi GN, Goldhirsch A, Scholl S, Makris A, Valagussa P, et al. Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: an update. J Clin Oncol. 2006;24(12):1940–9. https://doi.org/10.1200/jco.2005.02.6187.CrossRefPubMed

Berruti A, Generali D, Kaufmann M, Puztai L, Curigliano G, Aglietta M, et al. International expert consensus on primary systemic therapy in the management of early breast cancer: highlights of the fourth symposium on primary systemic therapy in the management of operable breast cancer, Cremona, Italy (2010). J Natl Cancer Inst Monogr. 2011;2011(43):147–51. https://doi.org/10.1093/jncimonographs/lgr037.CrossRefPubMed

Kaufmann M, von Minckwitz G, Bear HD, Buzdar A, McGale P, Bonnefoi H, et al. Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: new perspectives 2006. Ann Oncol. 2007;18(12):1927–34. https://doi.org/10.1093/annonc/mdm201.CrossRefPubMed

Mamounas EP. Neoadjuvant chemotherapy for operable breast cancer: is this the future? Clin Breast Cancer. 2003;4(Suppl 1):S10–9.CrossRef

Rastogi P, Anderson SJ, Bear HD, Geyer CE, Kahlenberg MS, Robidoux A, et al. Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. J Clin Oncol. 2008;26(5):778–85. https://doi.org/10.1200/jco.2007.15.0235.CrossRefPubMed

Iotti V, Ravaioli S, Vacondio R, Coriani C, Caffarri S, Sghedoni R, et al. Contrast-enhanced spectral mammography in neoadjuvant chemotherapy monitoring: a comparison with breast magnetic resonance imaging. Breast Cancer Res. 2017;19(1):106.CrossRef

Le-Petross H, Lim B. Role of MR Imaging in Neoadjuvant Therapy Monitoring. Magn Reson Imaging Clin N Am. 2018;26(2):207–20.CrossRef

Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, et al. New guidelines to evaluate the response to treatment in solid tumors: European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000;92(3):205–16.CrossRef

10.

Ogston KN, Miller ID, Payne S, Hutcheon AW, Sarkar TK, Smith I, et al. A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival. Breast (Edinb). 2003;12(5):320–7.CrossRef

11.

Untch M, Konecny GE, Paepke S, von Minckwitz G. Current and future role of neoadjuvant therapy for breast cancer. Breast (Edinb). 2014;23(5):526–37. https://doi.org/10.1016/j.breast.2014.06.004.CrossRef

12.

Babyshkina N, Malinovskaya E, Patalyak S, Bragina O, Tarabanovskaya N, Doroshenko A, et al. Neoadjuvant chemotherapy for different molecular breast cancer subtypes: a retrospective study in Russian population. Med Oncol. 2014;31(9):165. https://doi.org/10.1007/s12032-014-0165-7.CrossRefPubMed

13.

Shen C, Liu Z, Guan M, Song J, Lian Y, Wang S, et al. 2D and 3D CT radiomics features prognostic performance comparison in non-small cell lung cancer. Transl Oncol. 2017;10(6):886–94. https://doi.org/10.1016/j.tranon.2017.08.007.CrossRefPubMedPubMedCentral

14.

Guo J, Liu Z, Shen C, Li Z, Yan F, Tian J, et al. MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation. Eur Radiol. 2018;28(9):3872–3881. https://doi.org/10.1007/s00330-018-5381-7.CrossRefPubMed

15.

Liu Z, Wang Y, Liu X, Du Y, Tang Z, Wang K, et al. Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas. NeuroImage Clin. 2018;19:271–8. https://doi.org/10.1016/j.nicl.2018.04.024.CrossRefPubMedPubMedCentral

16.

Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016;34(18):2157–64. https://doi.org/10.1200/jco.2015.65.9128.CrossRefPubMed

17.

Shen C, Liu Z, Wang Z, Guo J, Zhang H, Wang Y, et al. Building CT radiomics based nomogram for preoperative esophageal cancer patients lymph node metastasis prediction. Transl Oncol. 2018;11(3):815–24. https://doi.org/10.1016/j.tranon.2018.04.005.CrossRefPubMedPubMedCentral

18.

Liu Z, Zhang XY, Shi YJ, Wang L, Zhu HT, Tang Z, et al. Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Clin Cancer Res. 2017;23(23):7253–62. https://doi.org/10.1158/1078-0432.ccr-17-1038.CrossRefPubMed

19.

Chamming’s F, Ueno Y, Ferre R, Kao E, Jannot AS, Chong J, et al. Features from computerized texture analysis of breast cancers at pretreatment mr imaging are associated with response to neoadjuvant chemotherapy. Radiology. 2018;286(2):412–20. https://doi.org/10.1148/radiol.2017170143.CrossRefPubMed

20.

Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res BCR. 2017;19(1):57. https://doi.org/10.1186/s13058-017-0846-1.CrossRefPubMed

21.

Verma V, Simone CB 2nd, Krishnan S, Lin SH, Yang J, Hahn SM. The rise of radiomics and implications for oncologic management. J Natl Cancer Inst. 2017. https://doi.org/10.1093/jnci/djx055.CrossRefPubMed

22.

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62. https://doi.org/10.1038/nrclinonc.2017.141.CrossRefPubMed

23.

Fujii T, Kogawa T, Dong W, Sahin AA, Moulder S, Litton JK, et al. Revisiting the definition of estrogen receptor positivity in HER2-negative primary breast cancer. Ann Oncol. 2017;28(10):2420–8. https://doi.org/10.1093/annonc/mdx397.CrossRefPubMedPubMedCentral

24.

Omarini C, Guaitoli G, Noventa S, Andreotti A, Gambini A, Palma E, et al. Impact of time to surgery after neoadjuvant chemotherapy in operable breast cancer patients. Eur J Surg Oncol. 2017;43(4):613–8. https://doi.org/10.1016/j.ejso.2016.09.020.CrossRefPubMed

25.

Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007;26(30):5512–28.CrossRef

26.

Vickers AJ, Cronin AM, Elkin EB, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Med Inform Decis Mak. 2008;8:53. https://doi.org/10.1186/1472-6947-8-53.CrossRefPubMedPubMedCentral

27.

Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47. https://doi.org/10.1016/j.ejca.2008.10.026.CrossRef

28.

Zhu Q, Tannenbaum S, Kurtzman SH, DeFusco P, Ricci A Jr, Vavadi H, et al. Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters. Breast Cancer Res BCR. 2018;20(1):56. https://doi.org/10.1186/s13058-018-0975-1.CrossRefPubMed

29.

Liu YH, Ye JM, Xu L, Huang QY, Zhao JX, Duan XN, et al. Effectiveness of dynamic contrast-enhanced magnetic resonance imaging in evaluating clinical responses to neoadjuvant chemotherapy in breast cancer. Chin Med J. 2011;124(2):194–8.PubMed

30.

Chen S, Liu Y, Ouyang QW, Huang L, Luo RC, Shao ZM. Clinical and pathological response to neoadjuvant chemotherapy based on primary tumor reduction is correlated to survival in hormone receptor-positive but not hormone receptor-negative locally advanced breast cancer. Ann Surg Oncol. 2015;22(1):32–9. https://doi.org/10.1245/s10434-014-3894-0.CrossRefPubMed

31.

von Minckwitz G, Untch M, Nüesch E, Loibl S, Kaufmann M, Kümmel S, et al. Impact of treatment characteristics on response of different breast cancer phenotypes: pooled analysis of the German neo-adjuvant chemotherapy trials. Breast Cancer Res Treat. 2011;125(1):145–56.CrossRef

32.

Lips EH, Mulder L, de Ronde JJ, Mandjes IA, Koolen BB, Wessels LF, et al. Breast cancer subtyping by immunohistochemistry and histological grade outperforms breast cancer intrinsic subtypes in predicting neoadjuvant chemotherapy response. Breast Cancer Res Treat. 2013;140(1):63–71. https://doi.org/10.1007/s10549-013-2620-0.CrossRefPubMedPubMedCentral

33.

Zhu T, Liu CL, Zhang YF, Liu YH, Xu FP, Zu J, et al. A phase II trial of dose-dense (biweekly) paclitaxel plus carboplatin as neoadjuvant chemotherapy for operable breast cancer. Breast Cancer Res Treat. 2016;156(1):117–24. https://doi.org/10.1007/s10549-016-3735-x.CrossRefPubMed

Title: Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy
Authors: Qianqian Xiong
Xuezhi Zhou
Zhenyu Liu
Chuqian Lei
Ciqiu Yang
Mei Yang
Liulu Zhang
Teng Zhu
Xiaosheng Zhuang
Changhong Liang
Zaiyi Liu
Jie Tian
Kun Wang
Publication date: 01-01-2020
Publisher: Springer International Publishing
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Breast Cancer
Breast Cancer
Cytostatic Therapy
Published in: Clinical and Translational Oncology / Issue 1/2020
Print ISSN: 1699-048X
Electronic ISSN: 1699-3055
DOI: https://doi.org/10.1007/s12094-019-02109-8

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2020

25-OH-vitamin D deficiency identifies poor tumor response in hepatocellular carcinoma treated with transarterial chemoembolization

Efficacy and safety of a simplified SBRT regimen for central and peripheral lung tumours

Survival outcomes in patients aged 75 years and over with peritoneal colorectal carcinomatosis after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (HIPEC): multicenter study of the Spanish Group of Peritoneal Cancer Surgery (GECOP)

First-line carboplatin/nab-paclitaxel in advanced ovarian cancer patients, after hypersensitivity reaction to solvent-based taxanes: a single-institution experience

MiR-130b/TNF-α/NF-κB/VEGFA loop inhibits prostate cancer angiogenesis

Is it time to change follow-up recommendations in colorectal cancer?

Keynote webinar | Spotlight on antibody–drug conjugates in cancer