Share：
Share this content in WeChat
X
[Chinese][PDF]4581
• Clinical Article •
Development and validation of a predictive model for the diagnosis of breast MRI masses based on the Kaiser score
YI Xi  WANG Yueai  LIU Fang  YANG Yu  CHEN Xiaoqiong  ZENG Yuli 

Cite this article as: YI X, WANG Y A, LIU F, et al. Development and validation of a predictive model for the diagnosis of breast MRI masses based on the Kaiser score[J]. Chin J Magn Reson Imaging, 2023, 14(5): 96-103. DOI:10.12015/issn.1674-8034.2023.05.018.


[Abstract] Objective To construct and externally validate a diagnostic prediction model for breast masses based on the Kaiser score of dynamic contrast-enhanced MRI (DCE-MRI) for diagnostic prediction of the risk of malignancy of masses on breast MRI.Materials and Methods We collected 199 lesions (from 199 patients) and 86 lesions (from 81 patients, including 5 patients with 2 lesions) from the Tianxinge Branch of Hunan Provincial People's Hospital from May 2020 to March 2021 and from the Mawangdui Branch of Hunan Provincial People's Hospital from September 2019 to December 2020, who underwent preoperative breast DCE-MRI and were confirmed by surgical or puncture pathology. Using the data from Tianxinge Branch as the training set and the data from Mawangdui Branch as the validation set. Imaging parameters collected included: the amount of fibroglandular tissue (FGT), background parenchymal enhancement (BPE), lesion size, mass characteristics (shape, margins, internal enhancement features), time-signal intensity curve (TIC), breast edema status, maximum intensity projection (MIP) sign, associated features (including nipple retraction, nipple invasion, skin retraction, skin thickening, skin invasion, axillary lymph node enlargement, pectoral muscle invasion, chest wall invasion, structural distortion), and Kaiser score based on the Kaiser score flow chart. Clinical parameters included age, gender, presence of pain, palpable mass, skin erythema, nipple discharge, orange peel appearance, and dimple sign. The least absolute shrinkage and selection operator (LASSO) was used to select predictor variables. Multivariate logistic regression was used to construct the prediction model, which was presented as a nomogram. The receiver operating characteristic (ROC) curve, DeLong test, net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare the diagnostic performance of the Kaiser score-based breast mass diagnostic prediction model (hereinafter referred to as "breast mass diagnostic model") and Kaiser score; calibration curves were plotted to assess the calibration of the breast mass diagnostic model; decision curve analysis (DCA) was used to evaluate the clinical validity of them.Results LASSO regression showed that "age" "MIP sign" and " associated features" were effective predictors in addition to those used in the Kaiser score; In the training set, the AUCs of the breast mass diagnostic model and Kaiser score were 0.944 and 0.890, with statistically significant differences (P＜0.05), and in the validation set, the AUCs of the breast mass diagnostic model and Kaiser score were 0.941 and 0.874, with statistically significant differences (P＜0.05). Furthermore, NRI and IDI showed that the breast mass diagnostic model had a better diagnostic performance for breast masses than the Kaiser score, and the difference was statistically significant (P＜0.05); the calibration curve showed that the breast mass diagnostic model was well calibrated; DCA indicated that the breast mass diagnostic model had high clinical application value.Conclusions The Kaiser score-based diagnostic model for breast masses can be used for preoperative prediction of the probability of malignancy of breast masses. Its diagnostic performance for breast masses is better than the classic Kaiser score.
[Keywords] breast cancer；benign breast lesions；breast；Kaiser score；prediction model；nomogram；magnetic resonance imaging；dynamic contrast-enhanced

YI Xi1, 2   WANG Yueai1*   LIU Fang1   YANG Yu3   CHEN Xiaoqiong1   ZENG Yuli2  

1 Department of Ultrasound Imaging, the First Hospital of Hunan University of Chinese Medicine, Changsha 410007, China

2 Department of Radiology, Hunan Provincial People's Hospital (the First Affiliated Hospital of Hunan Normal University), Changsha 410016, China

3 Department of Radiology, the First Hospital of Hunan University of Chinese Medicine, Changsha 410007, China

Corresponding author: Wang YA, E-mail: 497375291@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Scientific Research Project of Hunan Provincial Department of Education (No. 21C0236); Science and Health Joint Fund of Natural Science Foundation of Hunan Province (No. 2022JJ70114).
Received  2022-12-08
Accepted  2023-04-23
DOI: 10.12015/issn.1674-8034.2023.05.018
Cite this article as: YI X, WANG Y A, LIU F, et al. Development and validation of a predictive model for the diagnosis of breast MRI masses based on the Kaiser score[J]. Chin J Magn Reson Imaging, 2023, 14(5): 96-103. DOI:10.12015/issn.1674-8034.2023.05.018.

[1]
LEI S Y, ZHENG R S, ZHANG S W, et al. Global patterns of breast cancer incidence and mortality: a population-based cancer registry data analysis from 2000 to 2020[J]. Cancer Commun (Lond), 2021, 41(11): 1183-1194. DOI: 10.1002/cac2.12207">10.1002/cac2.12207">10.1002/cac2.12207.
[2]
SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660">10.3322/caac.21660">10.3322/caac.21660.
[3]
LI J, CHEN C, NIE J J, et al. Changes in the disease burden of breast cancer along with attributable risk factors in China from 1990 to 2019 and its projections: an analysis of the global burden of disease study 2019[J]. Cancer Med, 2023, 12(2): 1888-1902. DOI: 10.1002/cam4.5006">10.1002/cam4.5006">10.1002/cam4.5006.
[4]
YU X H, ZHANG J R, JIANG H R, et al. Disease burden of cervical cancer and breast cancer in Chinese women in 1990 and 2019[J]. Chin J Public Health, 2022, 38(5): 534-538. DOI: 10.11847/zgggws1136776">10.11847/zgggws1136776">10.11847/zgggws1136776.
[5]
ARNOLD M, MORGAN E, RUMGAY H, et al. Current and future burden of breast cancer: global statistics for 2020 and 2040[J]. Breast, 2022, 66: 15-23. DOI: 10.1016/j.breast.2022.08.010">10.1016/j.breast.2022.08.010">10.1016/j.breast.2022.08.010.
[6]
WILKINSON L, GATHANI T. Understanding breast cancer as a global health concern[J/OL]. Br J Radiol, 2022, 95(1130): 20211033 [2022-12-07]. https://pubmed.ncbi.nlm.nih.gov/34905391/. DOI: 10.1259/bjr.20211033">10.1259/bjr.20211033">10.1259/bjr.20211033.
[7]
PARK A R, CHAE E Y, CHA J H, et al. Preoperative breast MRI in women 35 years of age and younger with breast cancer: benefits in surgical outcomes by using propensity score analysis[J]. Radiology, 2021, 300(1): 39-45. DOI: 10.1148/radiol.2021204124">10.1148/radiol.2021204124">10.1148/radiol.2021204124.
[8]
MANN R M, CHO N, MOY L. Breast MRI: state of the art[J]. Radiology, 2019, 292(3): 520-536. DOI: 10.1148/radiol.2019182947">10.1148/radiol.2019182947">10.1148/radiol.2019182947.
[9]
COMSTOCK C E, GATSONIS C, NEWSTEAD G M, et al. Comparison of abbreviated breast MRI vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening[J]. JAMA, 2020, 323(8): 746-756. DOI: 10.1001/jama.2020.0572">10.1001/jama.2020.0572">10.1001/jama.2020.0572.
[10]
WOITEK R, SPICK C, SCHERNTHANER M, et al. A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions[J]. Eur Radiol, 2017, 27(9): 3799-3809. DOI: 10.1007/s00330-017-4755-6">10.1007/s00330-017-4755-6">10.1007/s00330-017-4755-6.
[11]
BALTZER P A, DIETZEL M, KAISER W A. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography[J]. Eur Radiol, 2013, 23(8): 2051-2060. DOI: 10.1007/s00330-013-2804-3">10.1007/s00330-013-2804-3">10.1007/s00330-013-2804-3.
[12]
AN Y Y, LIU C, ZHANG H X, et al. Diagnostic value of Kaiser score for BI-RADS 4 lesions on MRI[J]. Zhejiang Med J, 2021, 43(5): 511-515. DOI: 10.12056/j.issn.1006-2785.2021.43.5.2020-3139">10.12056/j.issn.1006-2785.2021.43.5.2020-3139">10.12056/j.issn.1006-2785.2021.43.5.2020-3139.
[13]
MENG L S, ZHAO X, GUO J X, et al. Evaluation of the differentiation of benign and malignant breast lesions using synthetic relaxometry and the Kaiser score[J/OL]. Front Oncol, 2022, 12: 964078 [2022-12-07]. https://pubmed.ncbi.nlm.nih.gov/36303839/. DOI: 10.3389/fonc.2022.964078">10.3389/fonc.2022.964078">10.3389/fonc.2022.964078.
[14]
ISTOMIN A, MASARWAH A, VANNINEN R, et al. Diagnostic performance of the Kaiser score for characterizing lesions on breast MRI with comparison to a multiparametric classification system[J/OL]. Eur J Radiol, 2021, 138: 109659 [2022-12-07]. https://pubmed.ncbi.nlm.nih.gov/33752000/. DOI: 10.1016/j.ejrad.2021.109659">10.1016/j.ejrad.2021.109659">10.1016/j.ejrad.2021.109659.
[15]
ZHOU H Y, CHEN T W, ZHANG X M. Asymmetric increase in breast vascularity and adjacent vessel sign in differential diagnosis of benign and malignant breast lesions[J]. Chin J Med Imaging Technol, 2016, 32(6): 900-904. DOI: 10.13929/j.1003-3289.2016.06.021">10.13929/j.1003-3289.2016.06.021">10.13929/j.1003-3289.2016.06.021
[16]
ÇETINKAYA E, YILDIZ Ş, OTÇU H, et al. The value of adjacent vessel sign in malignant breast tumors[J]. Diagn Interv Radiol, 2022, 28(5): 463-469. DOI: 10.5152/dir.2022.211228">10.5152/dir.2022.211228">10.5152/dir.2022.211228.
[17]
KRESOVICH J K, XU Z L, O'BRIEN K M, et al. Methylation-based biological age and breast cancer risk[J]. J Natl Cancer Inst, 2019, 111(10): 1051-1058. DOI: 10.1093/jnci/djz020">10.1093/jnci/djz020">10.1093/jnci/djz020.
[18]
BALTZER P A T, KRUG K B, DIETZEL M. Evidence-based and structured diagnosis in breast MRI using the Kaiser score[J]. Rofo, 2022, 194(11): 1216-1228. DOI: 10.1055/a-1829-5985">10.1055/a-1829-5985">10.1055/a-1829-5985.
[19]
PAN J L, LI X H, CHEN X J, et al. A comparison of the Kaiser score and apparent diffusion coefficient mapping in the assessment of breast lesions[J]. Chin J Magn Reson Imaging, 2022, 13(6): 108-111, 116. DOI: 10.12015/issn.1674-8034.2022.06.021">10.12015/issn.1674-8034.2022.06.021">10.12015/issn.1674-8034.2022.06.021
[20]
MENG L S, ZHAO X, LU L, et al. A comparative assessment of MR BI-RADS 4 breast lesions with Kaiser score and apparent diffusion coefficient value[J/OL]. Front Oncol, 2021, 11: 779642 [2022-12-07]. https://pubmed.ncbi.nlm.nih.gov/34926290/. DOI: 10.3389/fonc.2021.779642">10.3389/fonc.2021.779642">10.3389/fonc.2021.779642.
[21]
DIETZEL M, KRUG B, CLAUSER P, et al. A multicentric comparison of apparent diffusion coefficient mapping and the Kaiser score in the assessment of breast lesions[J]. Invest Radiol, 2021, 56(5): 274-282. DOI: 10.1097/RLI.0000000000000739">10.1097/RLI.0000000000000739">10.1097/RLI.0000000000000739.
[22]
LIU X W, WANG Y, WEI D M, et al. Age-period-cohort analysis of trends of breast cancer incidence and mortality among Chinese females from 1990 to 2019[J]. Chin Gen Pract, 2023(1): 34-41.
[23]
KIELY M, TSE L A, KOKA H, et al. Age-related DNA methylation in paired normal and tumour breast tissue in Chinese breast cancer patients[J]. Epigenetics, 2021, 16(6): 677-691. DOI: 10.1080/15592294.2020.1819661">10.1080/15592294.2020.1819661">10.1080/15592294.2020.1819661.
[24]
MARINO N, GERMAN R, PODICHETI R, et al. Aberrant epigenetic and transcriptional events associated with breast cancer risk[J/OL]. Clin Epigenetics, 2022, 14(1): 21 [2022-12-07]. https://pubmed.ncbi.nlm.nih.gov/35139887/. DOI: 10.1186/s13148-022-01239-1">10.1186/s13148-022-01239-1">10.1186/s13148-022-01239-1.
[25]
CHEN Z W, ZHAO Y F, LIU H R, et al. Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling[J]. Eur Radiol, 2022, 32(10): 6608-6618. DOI: 10.1007/s00330-022-08899-w">10.1007/s00330-022-08899-w">10.1007/s00330-022-08899-w.
[26]
ANDICA C, HAGIWARA A, HORI M, et al. Review of synthetic MRI in pediatric brains: basic principle of MR quantification, its features, clinical applications, and limitations[J]. J De Neuroradiol, 2019, 46(4): 268-275. DOI: 10.1016/j.neurad.2019.02.005">10.1016/j.neurad.2019.02.005">10.1016/j.neurad.2019.02.005.
[27]
MATSUDA M, TSUDA T, EBIHARA R, et al. Enhanced masses on contrast-enhanced breast: differentiation using a combination of dynamic contrast-enhanced MRI and quantitative evaluation with synthetic MRI[J]. J Magn Reson Imaging, 2021, 53(2): 381-391. DOI: 10.1002/jmri.27362">10.1002/jmri.27362">10.1002/jmri.27362.
[28]
MENG T B, HE N, HE H Q, et al. The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI[J/OL]. Cancer Imaging, 2020, 20(1): 88 [2022-12-07]. https://pubmed.ncbi.nlm.nih.gov/33317609/. DOI: 10.1186/s40644-020-00365-4">10.1186/s40644-020-00365-4">10.1186/s40644-020-00365-4.
[29]
GAO W B, ZHANG S Q, GUO J X, et al. Investigation of synthetic relaxometry and diffusion measures in the differentiation of benign and malignant breast lesions as compared to BI-RADS[J]. J Magn Reson Imaging, 2021, 53(4): 1118-1127. DOI: 10.1002/jmri.27435">10.1002/jmri.27435">10.1002/jmri.27435.

PREV Clinical value of arterial spin labeling imaging in the diagnosis of early parotid gland injury in Sjögren,s syndrome
NEXT The value of muti-parametric MRI in different molecular subtypes of primary breast cancer
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn