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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 


[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:

Conflicts of interest   None.

Received  2022-12-08
Accepted  2023-04-23
DOI: 10.12015/issn.1674-8034.2023.05.018

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