MRI can accurately diagnose breast cancer during lactation.
Breast neoplasms
Lactation
Magnetic resonance imaging
Pregnancy
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Apr 2023
Apr 2023
Historique:
received:
04
05
2022
accepted:
10
10
2022
revised:
27
08
2022
pubmed:
9
11
2022
medline:
21
3
2023
entrez:
8
11
2022
Statut:
ppublish
Résumé
To test the diagnostic performance of breast dynamic contrast-enhanced (DCE) MRI during lactation. Datasets of 198 lactating patients, including 66 pregnancy-associated breast cancer (PABC) patients and 132 controls, who were scanned by DCE on 1.5-T MRI, were retrospectively evaluated. Six blinded, expert radiologists independently read a single DCE maximal intensity projection (MIP) image for each case and were asked to determine whether malignancy was suspected and the background-parenchymal-enhancement (BPE) grade. Likewise, computer-aided diagnosis CAD MIP images were independently read by the readers. Contrast-to-noise ratio (CNR) analysis was measured and compared among four consecutive acquisitions of DCE subtraction images. For MIP-DCE images, the readers achieved the following means: sensitivity 93.3%, specificity 80.3%, positive-predictive-value 70.4, negative-predictive-value 96.2, and diagnostic accuracy of 84.6%, with a substantial inter-rater agreement (Kappa = 0.673, p value < 0.001). Most false-positive interpretations were attributed to either the MIP presentation, an underlying benign lesion, or an asymmetric appearance due to prior treatments. CAD's derived diagnostic accuracy was similar (p = 0.41). BPE grades were significantly increased in the healthy controls compared to the PABC cohort (p < 0.001). CNR significantly decreased by 11-13% in each of the four post-contrast images (p < 0.001). Breast DCE MRI maintains its high efficiency among the lactating population, probably due to a vascular-steal phenomenon, which causes a significant reduction of BPE in cancer cases. Upon validation by prospective, multicenter trials, this study could open up the opportunity for breast MRI to be indicated in the screening and diagnosis of lactating patients, with the aim of facilitating an earlier diagnosis of PABC. • A single DCE MIP image was sufficient to reach a mean sensitivity of 93.3% and NPV of 96.2%, to stress the high efficiency of breast MRI during lactation. • Reduction in BPE among PABC patients compared to the lactating controls suggests that several factors, including a possible vascular steal phenomenon, may affect cancer patients. • Reduction in CNR along four consecutive post-contrast acquisitions highlights the differences in breast carcinoma and BPE kinetics and explains the sufficient conspicuity on the first subtracted image.
Identifiants
pubmed: 36348090
doi: 10.1007/s00330-022-09234-z
pii: 10.1007/s00330-022-09234-z
doi:
Substances chimiques
4-azidobenzylcarazolol
79697-89-5
Contrast Media
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2935-2944Informations de copyright
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
Références
McManaman JL, Neville MC (2003) Mammary physiology and milk secretion. Adv Drug Deliv Rev 55(5):629–641
Meeuwis C, Van De Ven SM, Stapper G et al (2010) Computer-aided detection (CAD) for breast MRI: evaluation of efficacy at 3.0 T. Eur Radiol 20:522–528. https://doi.org/10.1007/s00330-009-1573-5
doi: 10.1007/s00330-009-1573-5
pubmed: 19727750
Vashi R, Hooley R, Butler R et al (2013) Breast imaging of the pregnant and lactating patient: physiologic changes and common benign entities. AJR Am J Roentgenol 200(2):329–336
Vashi R, Hooley R, Butler R et al (2013) Breast imaging of the pregnant and lactating patient: Imaging modalities and pregnancy-associated breast cancer. AJR Am J Roentgenol 200(2):321–328
Geddes DT, Aljazaf KM, Kent JC et al (2012) Blood flow characteristics of the human lactating breast. J Hum Lact. https://doi.org/10.1177/0890334411435414
Sabate JM, Clotet M, Torrubia S et al (2007) Radiologic evaluation of breast disorders related to pregnancy and lactation. Radiographics 27(Suppl 1):S101–S124
Nissan N, Allweis T, Menes T et al (2020) Breast MRI during lactation: effects on tumor conspicuity using dynamic contrast-enhanced (DCE) in comparison with diffusion tensor imaging (DTI) parametric maps. Eur Radiol. https://doi.org/10.1007/s00330-019-06435-x
Nissan N, Bauer E, Efraim E et al (2022) Breast MRI during pregnancy and lactation : clinical challenges and technical advances. Insights Imaging. https://doi.org/10.1186/s13244-022-01214-7
Nissan N, Sorin V, Bauer E, Anaby D, Samoocha D, Yagil Y, Faermann R, Halshtok-Neiman O, Gotlieb M, Sklair-Levy M (2021) MRI of the lactating breast : computer-aided diagnosis false positive rates and background parenchymal enhancement kinetic features. Acad Radiol 29(9):1332–1341
Kieturakis AJ, Wahab RA, Vijapura C, Mahoney MC (2021) Current recommendations for breast imaging of the pregnant and lactating patient. AJR Am J Roentgenol 216(6):1462–1475
diFlorio-Alexander RM, Slanetz PJ, Moy L et al (2018) ACR appropriateness Criteria® breast imaging of pregnant and lactating women. J Am Coll Radiol. https://doi.org/10.1016/j.jacr.2018.09.013
Carmichael H, Matsen C, Freer P et al (2017) Breast cancer screening of pregnant and breastfeeding women with BRCA mutations. Breast Cancer Res Treat 162(2):225–230
Ayyappan AP, Kulkarni S, Crystal P (2010) Pregnancy-associated breast cancer: spectrum of imaging appearances. Br J Radiol. https://doi.org/10.1259/bjr/17982822
Espinosa LA, Daniel BL, Vidarsson L et al (2005) The lactating breast: contrast-enhanced MR imaging of normal tissue and cancer. Radiology. https://doi.org/10.1148/radiol.2372040837
Myers KS, Green LA, Lebron L, Morris EA (2017) Imaging appearance and clinical impact of preoperative breast MRI in pregnancy-associated breast cancer. AJR Am J Roentgenol. https://doi.org/10.2214/AJR.16.17124
Oh SW, Lim HS, Moon SM et al (2017) MR imaging characteristics of breast cancer diagnosed during lactation. Br J Radiol. https://doi.org/10.1259/bjr.20170203
Taron J, Fleischer S, Preibsch H et al (2019) Background parenchymal enhancement in pregnancy-associated breast cancer: a hindrance to diagnosis? Eur Radiol. https://doi.org/10.1007/s00330-018-5721-7
Wang LC, DeMartini WB, Partridge SC et al (2009) MRI-detected suspicious breast lesions: predictive values of kinetic features measured by computer-aided evaluation. AJR Am J Roentgenol 193:826–831. https://doi.org/10.2214/AJR.08.1335
doi: 10.2214/AJR.08.1335
pubmed: 19696298
Song SE, Seo BK, Cho KR et al (2015) Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study. Cancer Imaging. https://doi.org/10.1186/s40644-015-0036-2
Baltzer PAT, Renz DM, Kullnig PE et al (2009) Application of computer-aided diagnosis (CAD) in MR-mammography (MRM). Do we really need whole lesion time curve distribution analysis? Acad Radiol 16:435–442. https://doi.org/10.1016/j.acra.2008.10.007
doi: 10.1016/j.acra.2008.10.007
pubmed: 19268855
Kuhl CK, Schrading S, Strobel K et al (2014) Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection - a novel approach to breast cancer screening with MRI. J Clin Oncol. https://doi.org/10.1200/JCO.2013.52.5386
Ma Y, Liu A, Zhang Y et al (2022) Comparison of background parenchymal enhancement (BPE) on contrast-enhanced cone-beam breast CT (CE-CBBCT) and breast MRI. Eur Radiol. https://doi.org/10.1007/s00330-022-08699-2
Furman-Haran E, Grobgeld D, Nissan N et al (2016) Can diffusion tensor anisotropy indices assist in breast cancer detection? J Magn Reson Imaging. https://doi.org/10.1002/jmri.25292
Landis JR, Koch GG (1977) The Measurement of observer agreement for categorical data. Biometrics. https://doi.org/10.2307/2529310
Moslem S, Ghorbanzadeh O, Blaschke T, Duleba S (2019) Analysing stakeholder consensus for a sustainable transport development decision by the fuzzy AHP and interval AHP. Sustainability. https://doi.org/10.3390/su11123271
Dorrius MD, Jansen-Van Der Weide MC, Van Ooijen PMA et al (2011) Computer-aided detection in breast MRI: a systematic review and meta-analysis. Eur Radiol 21:1600–1608. https://doi.org/10.1007/s00330-011-2091-9
doi: 10.1007/s00330-011-2091-9
pubmed: 21404134
pmcid: 3128262
Bailey KM, Cornnell HH, Ibrahim-Hashim A et al (2014) Evaluation of the “steal” phenomenon on the efficacy of hypoxia activated prodrug th-302 in pancreatic cancer. PLoS One. https://doi.org/10.1371/journal.pone.0113586
Forster J, Harriss-Phillips W, Douglass M, Bezak E (2017) A review of the development of tumor vasculature and its effects on the tumor microenvironment. Hypoxia. https://doi.org/10.2147/hp.s133231
Hughes P, Miranda R, Doyle AJ (2019) MRI imaging of soft tissue tumours of the foot and ankle. Insights Imaging 10(1):60
Leithner D, Helbich TH, Bernard-Davila B et al (2020) Multiparametric 18F-FDG PET/MRI of the breast: are there differences in imaging biomarkers of contralateral healthy tissue between patients with and without breast cancer? J Nucl Med. https://doi.org/10.2967/jnumed.119.230003
Nissan N, Sandler I, Eifer M et al (2020) Physiologic and hypermetabolic breast 18-F FDG uptake on PET/CT during lactation. Eur Radiol. https://doi.org/10.1007/s00330-020-07081-4
Kuhl CK, Mielcareck P, Klaschik S et al (1999) Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 211:101–110. https://doi.org/10.1148/radiology.211.1.r99ap38101
doi: 10.1148/radiology.211.1.r99ap38101
pubmed: 10189459
Bauer E, Levy MS, Domachevsky L et al (2021) Background parenchymal enhancement and uptake as breast cancer imaging biomarkers: a state-of-the-art review. Clin Imaging 83:41–50. https://doi.org/10.1016/j.clinimag.2021.11.021
doi: 10.1016/j.clinimag.2021.11.021
pubmed: 34953310
Giess CS, Yeh ED, Raza S, Birdwell RL (2014) Background parenchymal enhancement at breast MR imaging: Normal patterns, diagnostic challenges, and potential for false-positive and false-negative interpretation. Radiographics 34(1):234–247
Wei Q, Yan YJ, Wu GG et al (2022) The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol 1–10. https://doi.org/10.1007/s00330-021-08452-1
Nissan N, Furman-Haran E, Shapiro-Feinberg M et al (2014) Diffusion-tensor MR imaging of the breast: hormonal regulation. Radiology 271:672–680. https://doi.org/10.1148/radiol.14132084
doi: 10.1148/radiol.14132084
pubmed: 24533873
Nissan N, Furman-Haran E, Feinberg-Shapiro M et al (2014) Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging. J Vis Exp:1–18. https://doi.org/10.3791/52048
Sah RG, Agarwal K, Sharma U et al (2015) Characterization of malignant breast tissue of breast cancer patients and the normal breast tissue of healthy lactating women volunteers using diffusion MRI and in vivo 1H MR spectroscopy. J Magn Reson Imaging 41:169–174. https://doi.org/10.1002/jmri.24507
doi: 10.1002/jmri.24507
pubmed: 24273108
Iima M, Kataoka M, Sakaguchi R et al (2018) Intravoxel incoherent motion (IVIM) and non-Gaussian diffusion MRI of the lactating breast. Eur J Radiol Open 5:24–30. https://doi.org/10.1016/j.ejro.2018.01.003
doi: 10.1016/j.ejro.2018.01.003
pubmed: 29719854
pmcid: 5926247
Nissan N, Furman-Haran E, Shapiro-Feinberg M et al (2017) monitoring in-vivo the mammary gland microstructure during morphogenesis from lactation to post-weaning using diffusion tensor MRI. J Mammary Gland Biol Neoplasia. https://doi.org/10.1007/s10911-017-9383-x
Nissan N, Anaby D, Sklair-Levy M (2019) Breast MRI without contrast is feasible and appropriate during pregnancy. J Am Coll Radiol 16(4 Pt A):408–409
Nissan N, Furman-Haran E, Allweis T et al (2018) Noncontrast breast MRI during pregnancy using diffusion tensor imaging: a feasibility study. J Magn Reson Imaging:1–10. https://doi.org/10.1002/jmri.26228
Liu Z, Liang K, Zhang L et al (2022) Small lesion classification on abbreviated breast MRI: training can improve diagnostic performance and inter-reader agreement. Eur Radiol. https://doi.org/10.1007/s00330-022-08622-9
Kapsner LA, Ohlmeyer S, Folle L et al (2022) Automated artifact detection in abbreviated dynamic contrast-enhanced ( DCE ) MRI-derived maximum intensity projections (MIPs) of the breast. Eur Radiol 32(9):5997–6007