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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Mini-Review Article

Artificial Intelligence in Breast MRI Radiogenomics: Towards Accurate Prediction of Neoadjuvant Chemotherapy Responses

Author(s): Xiao-Xia Yin*, Yabin Jin, Mingyong Gao and Sillas Hadjiloucas

Volume 17, Issue 4, 2021

Published on: 25 August, 2020

Page: [452 - 458] Pages: 7

DOI: 10.2174/1573405616666200825161921

Price: $65

Abstract

Neoadjuvant Chemotherapy (NAC) in breast cancer patients has considerable prognostic and treatment potential and can be tailored to individual patients as part of precision medicine protocols. This work reviews recent advances in artificial intelligence so as to enable the use of radiogenomics for accurate NAC analysis and prediction. The work addresses a new problem in radiogenomics mining: How to combine structural radiomics information and non-structural genomics information for accurate NAC prediction. This requires the automated extraction of parameters from structural breast radiomics data, and finding non-structural feature vectors with diagnostic value, which then are combined with genomics data acquired from exocrine bodies in blood samples from a cohort of cancer patients to enable accurate NAC prediction. A self-attention-based deep learning approach, along with an effective multi-channel tumour image reconstruction algorithm of high dimensionality, is proposed. The aim was to generate non-structural feature vectors for accurate prediction of the NAC responses by combining imaging datasets with exocrine body related genomics analysis.

Keywords: Precision medicine, Neoadjuvant Chemotherapy (NCT), data mining, radiogenomics, exocrine bodies in blood samples, support vector machine.

Graphical Abstract

[1]
Gansler T, Ganz PA, Grant M, et al. Sixty years of CA: a cancer journal for clinicians. CA Cancer J Clin 2010; 60(6): 345-50.
[http://dx.doi.org/10.3322/caac.20088] [PMID: 21075954]
[2]
Derks MGM, van de Velde CJH. Neoadjuvant chemotherapy in breast cancer: more than just downsizing. Lancet Oncol 2018; 19(1): 2-3.
[http://dx.doi.org/10.1016/S1470-2045(17)30914-2] [PMID: 29242042]
[3]
von Minckwitz G, Untch M, Nüesch E, 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.
[http://dx.doi.org/10.1007/s10549-010-1228-x] [PMID: 21042932]
[4]
Caudle AS, Gonzalez-Angulo AM, Hunt KK, et al. Predictors of tumor progression during neoadjuvant chemotherapy in breast cancer. J Clin Oncol 2010; 28(11): 1821-8.
[http://dx.doi.org/10.1200/JCO.2009.25.3286] [PMID: 20231683]
[5]
Laviolle B, Denèfle P, Gueyffier F. participants of Giens XXXIV Round Table “Translational research”. The contribution of genomics in the medicine of tomorrow, clinical applications and issues. Therapie 2019; 74(1): 9-15.
[http://dx.doi.org/10.1016/j.therap.2018.11.012] [PMID: 30638855]
[6]
Colleoni M, Minchella I, Mazzarol G, et al. Response to primary chemotherapy in breast cancer patients with tumors not expressing estrogen and progesterone receptors. Ann Oncol 2000; 11(8): 1057-9.
[http://dx.doi.org/10.1023/A:1008334404825] [PMID: 11038046]
[7]
MacGrogan G, Mauriac L, Durand M, et al. Primary chemotherapy in breast invasive carcinoma: predictive value of the immunohistochemical detection of hormonal receptors, p53, c-erbB-2, MiB1, pS2 and GST pi. Br J Cancer 1996; 74(9): 1458-65.
[http://dx.doi.org/10.1038/bjc.1996.565] [PMID: 8912545]
[8]
Muñoz-Gonzalez D, Zeichner-Gancz I, Candelaria M, et al. Her-2/neu expression as a predictive factor for response to anthracycline-based chemotherapy in a mexican population of locally advanced breast cancer patients. Med Oncol 2005; 22(1): 23-8.
[http://dx.doi.org/10.1385/MO:22:1:023] [PMID: 15750193]
[9]
Mieog JS, van der Hage JA, van de Vijuer MJ, van de Velde CJ. Cooperating Investigators of the EORTC. Tumour response to preoperative anthracycline-based chemotherapy in operable breast cancer: the predictive role of p53 expression. Eur J Cancer 2006; 42(10): 1369-79.
[http://dx.doi.org/10.1016/j.ejca.2006.01.054] [PMID: 16766179]
[10]
Durbecq V, Desmed C, Paesmans M, et al. Correlation between topoisomerase-IIalpha gene amplification and protein expression in HER-2 amplified breast cancer. Int J Oncol 2004; 25(5): 1473-9.
[PMID: 15492841]
[11]
Chevillard S, Pouillart P, Beldjord C, et al. Sequential assessment of multidrug resistance phenotype and measurement of S-phase fraction as predictive markers of breast cancer response to neoadjuvant chemotherapy. Cancer 1996; 77(2): 292-300.
[http://dx.doi.org/10.1002/(SICI)1097-0142(19960115)77:2<292::AID-CNCR11>3.0.CO;2-X] [PMID: 8625237]
[12]
Cardoso F, van’t Veer LJ, Bogaerts J, et al. MINDACT Investigators. 70-gene signature as an aid to treatment decisions in early-stage breast cancer. N Engl J Med 2016; 375(8): 717-29.
[http://dx.doi.org/10.1056/NEJMoa1602253] [PMID: 27557300]
[13]
Wang G, Chen X, Liang Y, Wang W, Shen K. A long noncoding RNA signature that predicts pathological complete remission rate sensitively in neoadjuvant treatment of breast cancer. Transl Oncol 2017; 10(6): 988-97.
[http://dx.doi.org/10.1016/j.tranon.2017.09.005] [PMID: 29096247]
[14]
Jiang YZ, Liu YR, Xu XE, et al. Transcriptome analysis of triple-negative breast cancer reveals an integrated mRNA-lncRNA signature with predictive and prognostic value. Cancer Res 2016; 76(8): 2105-14.
[http://dx.doi.org/10.1158/0008-5472.CAN-15-3284] [PMID: 26921339]
[15]
Lin KR, Pang DM, Jin YB, et al. Circulating CD8(+) T-cell repertoires reveal the biological characteristics of tumors and clinical responses to chemotherapy in breast cancer patients. Cancer Immunol Immunother 2018; 1743-52.
[16]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48(4): 441-6.
[http://dx.doi.org/10.1016/j.ejca.2011.11.036] [PMID: 22257792]
[17]
Kumar V, Gu Y, Basu S, et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012; 30(9): 1234-48.
[http://dx.doi.org/10.1016/j.mri.2012.06.010] [PMID: 22898692]
[18]
Hahn SY, Ko EY, Han BK, Shin JH, Ko ES. Role of diffusion-weighted imaging as an adjunct to contrast-enhanced breast MRI in evaluating residual breast cancer following neoadjuvant chemotherapy. Eur J Radiol 2014; 83(2): 283-8.
[http://dx.doi.org/10.1016/j.ejrad.2013.10.023] [PMID: 24315957]
[19]
Pickles MD, Lowry M, Manton DJ, Gibbs P, Turnbull LW. Role of dynamic contrast enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy. Breast Cancer Res Treat 2005; 91(1): 1-10.
[http://dx.doi.org/10.1007/s10549-004-5819-2] [PMID: 15868426]
[20]
de Bazelaire C, Calmon R, Thomassin I, et al. Accuracy of perfusion MRI with high spatial but low temporal resolution to assess invasive breast cancer response to neoadjuvant chemotherapy: a retrospective study. BMC Cancer 2011; 11: 361.
[http://dx.doi.org/10.1186/1471-2407-11-361] [PMID: 21854572]
[21]
Karlo CA, Di Paolo PL, Chaim J, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 2014; 270(2): 464-71.
[http://dx.doi.org/10.1148/radiol.13130663] [PMID: 24029645]
[22]
Kocak B, Durmaz ES, Ates E, Ulusan MB. Radiogenomics in clear cell renal cell carcinoma: Machine learning-based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status. AJR Am J Roentgenol 2019; 212(3): W55-63.
[http://dx.doi.org/10.2214/AJR.18.20443] [PMID: 30601030]
[23]
Diehn M, Nardini C, Wang DS, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci USA 2008; 105(13): 5213-8.
[http://dx.doi.org/10.1073/pnas.0801279105] [PMID: 18362333]
[24]
Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: Identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology 2012; 264(2): 387-96.
[http://dx.doi.org/10.1148/radiol.12111607] [PMID: 22723499]
[25]
Huang YQ, Liang CH, He L, 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.
[http://dx.doi.org/10.1200/JCO.2015.65.9128] [PMID: 27138577]
[26]
Xi YB, Guo F, Xu ZL, et al. Radiomics signature: A potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. J Magn Reson Imaging 2018; 47(5): 1380-7.
[http://dx.doi.org/10.1002/jmri.25860] [PMID: 28926163]
[27]
Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging 2015; 42(4): 902-7.
[http://dx.doi.org/10.1002/jmri.24879] [PMID: 25777181]
[28]
Sutton EJ, Oh JH, Dashevsky BZ, et al. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging 2015; 42(5): 1398-406.
[http://dx.doi.org/10.1002/jmri.24890] [PMID: 25850931]
[29]
Chen J-H, Zhang Y, Chan S, Chang R-F, Su MY. Quantitative analysis of peri-tumor fat in different molecular subtypes of breast cancer. Magn Reson Imaging 2018; 53: 34-9.
[http://dx.doi.org/10.1016/j.mri.2018.06.019] [PMID: 29969646]
[30]
Halvaei S, Daryani S, Eslami-S Z, et al. Exosomes in cancer liquid biopsy: A focus on breast cancer. Mol Ther Nucleic Acids 2018; 10: 131-41.
[http://dx.doi.org/10.1016/j.omtn.2017.11.014] [PMID: 29499928]
[31]
He M, Zeng Y. Microfluidic exosome analysis toward liquid biopsy for cancer. J Lab Autom 2016; 21(4): 599-608.
[http://dx.doi.org/10.1177/2211068216651035] [PMID: 27215792]
[32]
Wang M, Ji S, Shao G, et al. Effect of exosome biomarkers for diagnosis and prognosis of breast cancer patients. Clin Transl Oncol 2018; 20: 906-11.
[http://dx.doi.org/10.1007/s12094-017-1805-0]
[33]
Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007; 25(6): 675-80.
[http://dx.doi.org/10.1038/nbt1306] [PMID: 17515910]
[34]
Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol 2009; 70(2): 232-41.
[http://dx.doi.org/10.1016/j.ejrad.2009.01.050] [PMID: 19303233]
[35]
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006.
[http://dx.doi.org/10.1038/ncomms5006] [PMID: 24892406]
[36]
Li H, Giger ML, Sun C, et al. Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys 2014; 41(3): 31917.
[http://dx.doi.org/10.1118/1.4865811] [PMID: 24593735]
[37]
Agner SC, Rosen MA, Englander S, et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study. Radiology 2014; 272(1): 91-9.
[http://dx.doi.org/10.1148/radiol.14121031] [PMID: 24620909]
[38]
Ashraf AB, Daye D, Gavenonis S, et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. Radiology 2014; 272(2): 374-84.
[http://dx.doi.org/10.1148/radiol.14131375] [PMID: 24702725]
[39]
Wang J, Kato F, Oyama-Manabe N, et al. Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: A pilot radiomics study. PLoS One 2015; 10(11): e0143308.
[http://dx.doi.org/10.1371/journal.pone.0143308] [PMID: 26600392]
[40]
Li H, Zhu Y, Burnside ES, et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology 2016; 281(2): 382-91.
[http://dx.doi.org/10.1148/radiol.2016152110] [PMID: 27144536]
[41]
Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014; 273(2): 365-72.
[http://dx.doi.org/10.1148/radiol.14132641] [PMID: 25028781]
[42]
Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. AJR Am J Roentgenol 2012; 199(3): 654-63.
[http://dx.doi.org/10.2214/AJR.11.7824] [PMID: 22915408]
[43]
Liu J, Chen F, Pan C, et al. A cascaded deep convolutional neural network for joint segmentation and genotype prediction of brainstem gliomas. IEEE Trans Biomed Eng 2018; 65(9): 1943-52.
[http://dx.doi.org/10.1109/TBME.2018.2845706] [PMID: 29993462]
[44]
Pan CC, Liu J, Tang J, et al. A machine learning-based prediction model of H3K27M mutations in brainstem gliomas using conventional MRI and clinical features. Radiother Oncol 2019; 130: 172-9.
[http://dx.doi.org/10.1016/j.radonc.2018.07.011] [PMID: 30097251]
[45]
Yin X-X, Hadjiloucas S, Chen JH, Zhang Y, Wu J-L, Su M-Y. Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs. PLoS One 2017; 12(3): e0172111.
[http://dx.doi.org/10.1371/journal.pone.0172111] [PMID: 28282379]
[46]
Yin X-X, Hadjiloucas S, Zhang Y. Pattern classification of medical images: Computer aided diagnosis. Publ: Springer-Verlag 2017.
[47]
Sun L, He J, Yin X-X, et al. An image segmentation framework for extracting tumors from breast magnetic resonance images. J Innov Opt Health Sci 2018; 11(4): 1850014.
[http://dx.doi.org/10.1142/S1793545818500141]
[48]
Yin X-X, Ng BW-H, Yang Q, Pitman A, Ramamohanarao K, Abbott D. Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging. Med Biol Eng Comput 2012; 50(1): 91-101.
[http://dx.doi.org/10.1007/s11517-011-0772-9] [PMID: 21503767]
[49]
Pandey D, Yin X, Wang H, et al. Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs. Heliyon 2018; 4(12): e01042.
[http://dx.doi.org/10.1016/j.heliyon.2018.e01042] [PMID: 30582055]
[50]
Carneiro G, Nascimento J, Bradley AP. Unregistered multiview mammogram analysis with pre-trained deep learning models. Springer Int’l Publishing 2015; pp. 159-69.
[http://dx.doi.org/10.1007/978-3-319-24574-4_78]
[51]
Jebara T. Multi-task feature and kernel selection for SVMs. Proceedings of the 21st International Conference on Machine Learning. July 2004.
[http://dx.doi.org/10.1007/978-1-4419-9011-2]
[52]
Wu H, Jin Q. Improving emotion classification on Chinese microblog texts with auxiliary cross-domain data. 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). Xi'an. 2015; pp. 821-6.
[http://dx.doi.org/10.1109/ACII.2015.7344668]
[53]
Zheng C, Xia Y, Chen Y. Early diagnosis of alzheimer’s disease by ensemble deep learning using FDG-PET, IScIDE 2018; 614-22.
[54]
Xie J, Chen B, Gu X. Self-attention-based BiLSTM model for short text fine-grained sentiment classification. IEEE Access 2019; 7: 180558-70.

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