Comparison of Amritsar scoring system using quantitative contrast enhanced ultrafast magnetic resonance mammography and diffusion technique with conventional Kaiser scoring system
DOI:
https://doi.org/10.18203/2349-2902.isj20223594Keywords:
Breast cancer, Diffusion imaging, MRI mammographyAbstract
Background: Magnetic resonance imaging (MRI) mammography has been recommended as a problem solving tool in patients with breast lump for which newer imaging protocols like abbreviated, ultrafast MRI and diffusion MRI are now available. For interpretation lexicons like BIRADS and Kaiser score system are available but there is a scope for improvement of results and need for newer lexicons.
Methods: Retrospective study of 175 patients of breast lump who had MRI mammography was done. The lesions were labelled as malignant by Kaiser score system (KSS) and a newer scoring system “Amritsar score system” (AMSS). Final diagnoses was confirmed by histological examination with hormone and Her2neu receptor studies. Statistical analysis was done for correlation, sensitivity, specificity and accuracy along with area under curves and the results compared.
Results: Study comprised 32/175 patients with malignant nodules. Mean age of 47.2 (range: 44.2-49.6) years with mean nodule size of 2.2 cm (range 1.8-5.5 cm). ADC and Ktrans, Kep, TTE, MS and IAUC60 showed high correlation with size of malignant nodule. Sensitivity of detection was 87.4%, 87.5%, 88.6%, 71.8% and 80% respectively for ADC, Ktrans, Kep, TTE, MS and IAUC60 while specificities were 94, 88.7, 88.7% 90% and 90% respectively. The sensitivity, specificity and accuracy for KSS and AMSS were 62.5%, 88.9%, 72% and 96.5%, 90% and 94% respectively.
Conclusions: AMSS is more accurate than KSS and improves the sensitivity and specificity of cancer detection. Ktrans and ADC imaging parameters not only show high sensitivity for cancer detection but also have a good correlation with the size and nuclear grade to be used as imaging biomarkers.
References
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 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.
Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227-36.
Pisano ED, Gatsonis C, Hendrick E, Yaffe M, Baum JK, Acharyya S, et al. Diagnostic performance of digital versus film mammography for breast-cancer screening. N Engl J Med. 2005;353(17):1773-83.
Berg WA, Blume JD, Cormack JB, Mendelson EB, Lehrer D, Böhm-Vélez M, et al. Combined screening with ultrasound and mammography versus mammography alone in women at elevated risk of breast cancer. JAMA. 2008;299(18):2151-63.
Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology. 2002;225(1):165-75.
Mandelson MT, Oestreicher N, Porter PL, White D, Finder CA, Taplin SH, et al. Breast density as a predictor of mammographic detection: comparison of interval-and screen-detected cancers. J Nat Cancer Inst. 2000;92(13):1081-7.
Buchberger W, Geiger-Gritsch S, Knapp R, Gautsch K, Oberaigner W. Combined screening with mammography and ultrasound in a population-based screening program. Eur J Radiol. 2018;101:24-9.
Kuhl C, Weigel S, Schrading S, Arand B, Bieling H, König R, et al. Prospective multicenter cohort study to refine management recommendations for women at elevated familial risk of breast cancer: the EVA trial. J Clin Oncol. 2010;28(9):1450-7.
Morrow M. Magnetic resonance imaging for screening, diagnosis, and eligibility for breast-conserving surgery: promises and pitfalls. Surg Oncol Clin N Am. 2010;19(3):475-92.
Baltzer PA, Dietzel M, Kaiser WA. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol. 2013;23(8):2051-60.
Istomin A, Masarwah A, Okuma H, Sutela A, Vanninen R, Sudah M. A multiparametric classification system for lesions detected by breast magnetic resonance imaging. Eur J Radiol. 2020;132:109322.
D’Orsi C, Bassett L, Feig S. Breast imaging reporting and data system (BI-RADS). Breast imaging atlas, 4th edn. American College of Radiology, Reston; 2018.
Istomin A, Masarwah A, Vanninen R, Okuma H, Sudah M. Diagnostic performance of the Kaiser score for characterizing lesions on breast MRI with comparison to a multiparametric classification system. Eur J Radiol. 2021;138:109659.
Yang Z, Chen X, Zhang T, Cheng F, Liao Y, Chen X, et al. Quantitative multiparametric mri as an imaging biomarker for the prediction of breast cancer receptor status and molecular subtypes. Women Cancer. 2021.
Leithner D, Mayerhoefer ME, Martinez DF, Jochelson MS, Morris EA, Thakur SB, et al. Non-invasive assessment of breast cancer molecular subtypes with multiparametric magnetic resonance imaging radiomics. J Clin Med. 2020;9(6):1853.
Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)- weighted MRI of a diffusible tracer: standardized quantities and symbols. J Magn Reson Imag. 1999;10(3):223-32
Park SH, Choi HY, Hahn SY. Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla. J Magn Reson Imag. 2015; 41(1):175-82.
Clauser P, Mann R, Athanasiou A, Prosch H, Pinker K, Dietzel M, et al. A survey by the European Society of Breast Imaging on the utilisation of breast MRI in clinical practice. Eur Radiol. 2018;28(5):1909-18.
Wengert GJ, Pipan F, Almohanna J, Bickel H, Polanec S, Kapetas P, et al. Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI. Eur Radiol. 2020;30(3):1451-9.
Yau EJ, Gutierrez RL, DeMartini WB, Eby PR, Peacock S, Lehman CD. The utility of breast MRI as a problem‐solving tool. Breast J. 2011;17(3):273-80.
Spick C, Szolar DH, Preidler KW, Tillich M, Reittner P, Baltzer PA. Breast MRI used as a problem-solving tool reliably excludes malignancy. Eur J Radiol. 2015;84(1):61-4.
Acr bi-rads® mammography. American College of Radiology. Available from: https://www.acr.org/ Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads. Accessed on 1 December 2021.
Baltzer PA, Dietzel M, Kaiser WA. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol. 2013;23(8):2051-60.
Jajodia A, Sindhwani G, Pasricha S, Prosch H, Puri S, Dewan A, et al. Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography. Eur J Radiol. 2021;134;1-7.
Koo HR, Cho N, Song IC, Kim H, Chang JM, Yi A, et al. Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers. J Magn Reson Imag. 2012;36(1):145-51.
Boulogianni G, Chryssogonidis I, Drevelegas A. Diffusion weighted MRI and spectroscopy in invasive carcinoma of the breast at 3Tesla. Correlation with dynamic contrast enhancement and pathologic findings. Hippokratia. 2016;20(3):192-7.
Kim SH, Ji YK, Yun JK, Kang BJ, An YY, Lee AW, et al. Enhancement parameters on dynamic contrast enhanced breast MRI: do they correlate with prognostic factors and subtypes of breast cancers? Magn Reson Imag. 2015;33(1):72-80.
Liu F, Wang M, Li H. Role of perfusion parameters on DCE-MRI and ADC values on DWMRI for invasive ductal carcinoma at 3.0 Tesla. World J Surg Oncol. 2018;16(239):2-12.
Kinkel K, Helbich TH, Esserman LJ, Barclay J, Schwerin EH, Sickles EA, et al. Dynamic high-spatial-resolution MR imaging of suspicious breast lesions: diagnostic criteria and interobserver variability. Am JRoentgenol. 2000;175(1):35-43.
