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Research progress on neuroimaging biomarkers of chemotherapy-related cognitive impairment in breast cancer
WANG Lei  ZHOU Fuqing 

Cite this article as: Wang L, Zhou FQ. Research progress on neuroimaging biomarkers of chemotherapy-related cognitive impairment in breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(2): 112-115. DOI:10.12015/issn.1674-8034.2022.02.027.

[Abstract] Neuroimaging technology, especially magnetic resonance imaging (MRI), is widely used in evaluating the alterations of brain anatomy and function, and has gradually become a powerful tool for clinical study of chemotherapy-related cognitive impairment (CRCI) in breast cancer, and provides a possible neuroimaging diagnostic marker for its early diagnosis. The article reviewed the application of neuroimaging in the study of neuroimaging biomarkers of CRCI in breast cancer, in order to provide imaging evidence for revealing its pathophysiological mechanism and early diagnosis.
[Keywords] breast cancer;chemotherapy-related cognitive impairment;neuroimaging biomarkers;magnetic resonance imaging

WANG Lei   ZHOU Fuqing*  

Department of Radiology, the First Affiliated Hospital, Nanchang University, Nanchang 330006, China

Zhou FQ, E-mail:

Conflicts of interest   None.

Received  2021-08-30
Accepted  2021-12-28
DOI: 10.12015/issn.1674-8034.2022.02.027
Cite this article as: Wang L, Zhou FQ. Research progress on neuroimaging biomarkers of chemotherapy-related cognitive impairment in breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(2): 112-115.DOI:10.12015/issn.1674-8034.2022.02.027

Janelsins MC, Heckler CE, Peppone LJ, et al. Cognitive Complaints in Survivors of Breast Cancer After Chemotherapy Compared With Age-Matched Controls: An Analysis From a Nationwide, Multicenter, Prospective Longitudinal Study[J]. J Clin Oncol, 2017, 35(5): 506-514. DOI: 10.1200/JCO.2016.68.5826.
Gutmann DH. Clearing the Fog surrounding Chemobrain[J]. Cell, 2019, 176(1-2): 2-4. DOI: 10.1016/j.cell.2018.12.027.
Zheng RS, Sun KX, Zhang SW, et al. Report of cancer epidemiology in China, 2015[J]. Chin J Oncol, 2019, 41(1): 19-28. DOI: 10.3760/cma.j.issn.0253-3766.2019.01.005.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70(1): 7-30. DOI: 10.3322/caac.21590.
Lange M, Licaj I, Clarisse B, et al. Cognitive complaints in cancer survivors and expectations for support: Results from a web-based survey[J]. Cancer Med, 2019, 8(5): 2654-2663. DOI: 10.1002/cam4.2069.
Li M, Caeyenberghs K. Longitudinal assessment of chemotherapy-induced changes in brain and cognitive functioning: A systematic review[J]. Neurosci Biobehav Rev, 2018, 92: 304-317. DOI: 10.1016/j.neubiorev.2018.05.019.
Jacopo JVB, Mario M, Gabriele M, et al. Oxaliplatin-induced blood brain barrier loosening a new point of view on chemotherapy-induced neurotoxicity[J]. Oncotarget, 2018, 9(34): 23426-23438. DOI: 10.18632/oncotarget.25193.
Taillibert S, Le Rhun E, Chamberlain MC. Chemotherapy-Related Neurotoxicity[J]. Curr Neurol Neurosci Rep, 2016, 16(9): 81. DOI: 10.1007/s11910-016-0686-x.
Pomykala KL, Ganz PA, Bower JE, et al. The association between pro-inflammatory cytokines, regional cerebral metabolism, and cognitive complaints following adjuvant chemotherapy for breast cancer[J]. Brain Imaging Behav, 2013, 7(4): 511-523. DOI: 10.1007/s11682-013-9243-2.
Gibson EM, Nagaraja S, Ocampo A, et al. Methotrexate Chemotherapy Induces Persistent Tri-glial Dysregulation that Underlies Chemotherapy-Related Cognitive Impairment[J]. Cell, 2019, 176(1-2): 43-55. e13. DOI: 10.1016/j.cell.2018.10.049.
Ahles TA, Saykin AJ. Candidate mechanisms for chemotherapy-induced cognitive changes[J]. Nature Reviews Cancer, 2007, 7(3): 192-201. DOI: 10.1038/nrc2073.
Li X, Chen H, Lv Y, et al. Diminished gray matter density mediates chemotherapy dosage-related cognitive impairment in breast cancer patients[J]. Sci Rep, 2018, 8(1): 13801. DOI: 10.1038/s41598-018-32257-w.
Inagaki M, Yoshikawa E, Matsuoka Y, et al. Smaller regional volumes of brain gray and white matter demonstrated in breast cancer survivors exposed to adjuvant chemotherapy[J]. Cancer, 2007, 109(1): 146-156. DOI: 10.1002/cncr.22368.
Chen BT, Sethi SK, Jin T, et al. Assessing brain volume changes in older women with breast cancer receiving adjuvant chemotherapy a brain magnetic resonance imaging pilot study[J]. Breast Cancer Research, 2018, 20(1): 38. DOI: 10.1186/s13058-018-0965-3.
De Ruiter MB, Reneman L, Boogerd W, et al. Late effects of high-dose adjuvant chemotherapy on white and gray matter in breast cancer survivors: converging results from multimodal magnetic resonance imaging[J]. Hum Brain Mapp, 2012, 33(12): 2971-2983. DOI: 10.1002/hbm.21422.
Chen BT, Ye N, Wong CW, et al. Effects of chemotherapy on aging white matter microstructure: A longitudinal diffusion tensor imaging study[J]. J Geriatr Oncol, 2020, 11(2): 290-296. DOI: 10.1016/J.JGO.2019.09.016.
Li TY, Chen VC, Yeh DC, et al. Investigation of chemotherapy-induced brain structural alterations in breast cancer patients with generalized q-sampling MRI and graph theoretical analysis[J]. BMC Cancer, 2018, 18(1): 1211. DOI: 10.1186/s12885-018-5113-z.
Pergolizzi D, Root JC, Pan H, et al. Episodic memory for visual scenes suggests compensatory brain activity in breast cancer patients: a prospective longitudinal fMRI study[J]. Brain Imaging Behav, 2019, 13(6): 1674-1688. DOI: 10.1007/S11682-019-00038-2.
Kesler SR, Kent JS, O'hara R. Prefrontal cortex and executive function impairments in primary breast cancer[J]. Arch Neurol, 2011, 68(11): 1447-53. DOI: 10.1001/archneurol.2011.245.
Tao L, Lin H, Yan Y, et al. Impairment of the executive function in breast cancer patients receiving chemotherapy treatment: a functional MRI study[J]. Eur J Cancer Care (Engl), 2017, 26(6): 1-8. DOI: 10.1111/ecc.12553.
Chen BT, Chen Z, Patel SK, et al. Effect of chemotherapy on default mode network connectivity in older women with breast cancer[J]. Brain Imaging Behav, 2021: 1-11. DOI: 10.1007/S11682-021-00475-Y.
Apple AC, Schroeder MP, Ryals AJ, et al. Hippocampal functional connectivity is related to self-reported cognitive concerns in breast cancer patients undergoing adjuvant therapy[J]. Neuroimage Clin, 2018, 20: 110-118. DOI: 10.1016/j.nicl.2018.07.010.
Shen CY, Chen VC, Yeh DC, et al. Association of functional dorsal attention network alterations with breast cancer and chemotherapy[J]. Sci Rep, 2019, 9(1): 104. DOI: 10.1038/s41598-018-36380-6.
Mo C, Lin H, Fu F, et al. Chemotherapy-induced changes of cerebral activity in resting-state functional magnetic resonance imaging and cerebral white matter in diffusion tensor imaging[J]. Oncotarget, 2017, 8(46): 81273-81284. DOI: 10.18632/oncotarget.18111.
Bekele BM, Luijendijk M, Schagen SB, et al. Fatigue and resting-state functional brain networks in breast cancer patients treated with chemotherapy[J]. Breast Cancer Res Treat, 2021, 189(3): 787-796. DOI: 10.1007/S10549-021-06326-0.
Chen X, He X, Tao L, et al. The attention network changes in breast cancer patients receiving neoadjuvant chemotherapy: Evidence from an arterial spin labeling perfusion study[J]. Sci Rep, 2017, 7: 42684. DOI: 10.1038/srep42684.
Tong T, Lu H, Zong J, et al. Chemotherapy-related cognitive impairment in patients with breast cancer based on MRS and DTI analysis[J]. Breast Cancer, 2020, 27(5): 893-902. DOI: 10.1007/s12282-020-01094-z.
Chen BT, Ghassaban K, Jin T, et al. Subcortical brain iron deposition and cognitive performance in older women with breast cancer receiving adjuvant chemotherapy: A pilot MRI study[J]. Magn Reson Imaging, 2018, 54: 218-224. DOI: 10.1016/j.mri.2018.07.016.
Kesler SR, Rao V, Ray WJ, et al. Probability of Alzheimer's disease in breast cancer survivors based on gray-matter structural network efficiency[J]. Alzheimers Dement (Amst), 2017, 9: 67-75. DOI: 10.1016/j.dadm.2017.10.002.
Hosseini SM, Kesler SR. Multivariate pattern analysis of FMRI in breast cancer survivors and healthy women[J]. J Int Neuropsychol Soc, 2014, 20(4): 391-401. DOI: 10.1017/S1355617713001173.
Askren MK, Jung M, Berman MG, et al. Neuromarkers of fatigue and cognitive complaints following chemotherapy for breast cancer: a prospective fMRI investigation[J]. Breast Cancer Res Treat, 2014, 147(2): 445-455. DOI: 10.1007/s10549-014-3092-6.
Kesler SR. Default mode network as a potential biomarker of chemotherapy-related brain injury[J]. Neurobiol Aging, 2014, 35Suppl 2: S11-S19. DOI: 10.1016/j.neurobiolaging.2014.03.036.
Kesler SR, Wefel JS, Hosseini SM, et al. Default mode network connectivity distinguishes chemotherapy-treated breast cancer survivors from controls[J]. Proc Natl Acad Sci USA, 2013, 110(28): 11600-11605. DOI: 10.1073/pnas.1214551110.
Shen X, Finn ES, Scheinost D, et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity[J]. Nat Protoc, 2017, 12(3): 506-518. DOI: 10.1038/nprot.2016.178.
Henneghan AM, Gibbons C, Harrison RA, et al. Predicting Patient Reported Outcomes of Cognitive Function Using Connectome-Based Predictive Modeling in Breast Cancer[J]. Brain Topogr, 2020, 33(1): 135-142. DOI: 10.1007/s10548-019-00746-4.
Kesler SR, Petersen ML, Rao V, et al. Functional connectome biotypes of chemotherapy-related cognitive impairment[J]. J Cancer Surviv, 2020, 14(4): 483-493. DOI: 10.1007/s11764-020-00863-1.
Preti MG, Bolton TA, Van De Ville D. The dynamic functional connectome: State-of-the-art and perspectives[J]. Neuroimage, 2017, 160: 41-54. DOI: 10.1016/j.neuroimage.2016.12.061.
Yuan YM, Zhang L, Zhang ZG. A review of methods and clinical applications for dynamic functional connectivity analysis based on resting-state functional magnetic resonance imaging[J]. Chin J Magn Reson Imaging, 2018, 9(8): 579-588. DOI: 10.12015/issn.1674-8034.2018.08.005.
Chen VC, Lin TY, Yeh DC, et al. Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning[J]. Magn Reson Med, 2019, 81(5): 3304-3313. DOI: 10.1002/mrm.27607.
Chen VC, Lin TY, Yeh DC, et al. Functional and Structural Connectome Features for Machine Learning Chemo-Brain Prediction in Women Treated for Breast Cancer with Chemotherapy[J]. Brain Sci, 2020, 10(11): E851. DOI: 10.3390/brainsci10110851.
Kesler SR, Rao A, Blayney DW, et al. Predicting Long-Term Cognitive Outcome Following Breast Cancer with Pre-Treatment Resting State fMRI and Random Forest Machine Learning[J]. Front Hum Neurosci, 2017, 11: 555. DOI: 10.3389/fnhum.2017.00555.
Steven AJ, Zhuo J, Melhem ER. Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain[J]. AJR Am J Roentgenol, 2014, 202(1): W26-W33. DOI: 10.2214/AJR.13.11365.
Gong ZB, Chen HH, Liu SF, et al. Research progress of magnetic resonance diffusion spectrum imaging in the nervous system[J]. Chin J Magn Reson Imaging, 2020, 11(9): 809-812, 816. DOI: 10.12015/issn.1674-8034.2020.09.020.
Dai Z, Yan C, Wang Z, et al. Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)[J]. Neuroimage, 2012, 59(3): 2187-2195. DOI: 10.1016/j.neuroimage.2011.10.003.
De Vos F, Koini M, Schouten TM, et al. A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease[J]. Neuroimage, 2018, 167: 62-72. DOI: 10.1016/j.neuroimage.2017.11.025.

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