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BI-RADS 4 Breast Lesions: Could Synthetic MRI Be Helpful for Their Diagnosis?
Shi Yun Sun1, Zhuo Lin Li1, Ying Ying Ding1, Li Sha Nie2, Cheng De Liao1, Yi Fan Liu1, Rui Wang1, Jia Zhang3, and Dong Xue Zhang1
1The Third Affiliated Hospital of Kunming Medical University，Yunnan Cancer Hospital, Kunming, China, 2GE Healthcare, MR Research China, Beijing, China, Beijing, China, 3The Third People's Hospital of Yunnan Province, China, Kunming, China

Synopsis

Synthetic MRI (syMRI) can quantify multiple relaxation parameters at the same time, which might have potential application value in the BI-RADS 4 lesions. 77 breast disease patients who were defined as BI-RADS 4 in the preoperative MRI examination were prospectively enrolled in this study. Before and after contrast injection, all patients underwent routine MRI and syMRI examinations. The result show that relaxation time and ADC values provided by syMRI and DWI are useful in distinguishing breast BI-RADS 4 lesions. The multi parameter model combined with clinical and imaging features can significantly improve the diagnostic ability of BI-RADS 4 breast lesions.

Background

Based on signal intensity, morphology and dynamics contrast imaging, BI-RADS-MRI is widely regarded as the reference standard for MRI interpretation of breast lesions. These standards are subjective and qualitative assessments. The malignant probability of BI-RADS 4 lesions ranges from 2% to 95%. Such a wide range makes a large part of patients receive unnecessary histological biopsy. Therefore, an objective, rapid, and stable quantitative technique is needed to further assist the diagnosis of such patients. Synthetic MRI (syMRI) can quantify multiple relaxation parameters at the same time, which might have potential application value in the BI-RADS 4 lesions.

Purpose

In order to evaluate the value of syMRI in quantitative analysis of breast BI-RADS type 4 lesions, and to develop a method that can diagnose BI-RADS type 4 lesions more efficiently, so as to help patients avoid unnecessary tissue biopsy as much as possible.

Assessment

77 breast disease patients who were defined as BI-RADS 4 in the preoperative MRI examination were retrospectively enrolled in this study.Before and after contrast injection, all patients underwent routine MRI and syMRI examinations on 3.0T MRI scanners. Two methods were used to draw the region of interest (ROI) on the T1 and T2 relaxation maps and measured the relaxation time. Two types of measured values were obtained "Tlocal" and "Ttumor". “T” was used to represent the relaxation time of flat scan, and “T+” was used to represent the relaxation time after enhanced scan. Univariate and multivariate logistic regression analysis, the receiver operating characteristic (ROC) analysis, nomogram, calibration curve and clinical decision curve (DCA), the intraclass correlation analysis and the Bland-Altman analysis.

Field Strength/Sequence

T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and synthetic MRI (syMRI) at 3.0T.

Results

Univariate and multivariable logistic regression analysis showed that age, BMI, menopausal state, ΔT1%tumor and ADClocal were independent variables for differentiating breast cancer from other benign lesions. Combined above variables to construct prediction model A, B and C. The model C and A showed a similar area under the curve (AUC) (AUC =0.950 and 0.899, P =0.124), which were higher than that of model B (AUC =0.807, P =0.002 and 0.047). The DCA indicated that when the threshold probability ranges between 2-10% and 30%-99%, the net benefit of model C was better than other 2 models. The calibration curve of the nomogram providing evidence of good calibration.

Conclusions

The relaxation time and ADC values provided by syMRI and DWI are useful in distinguishing breast BI-RADS 4 types of benign and malignant lesions. The multi parameter model combined with clinical and imaging features can significantly improve the diagnostic ability of BI-RADS 4 breast lesions.

Acknowledgements

Grant Support: The project was supported by a grant from the Department of science and technology of Yunnan Province, China. NO.2018FE001(-066).

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Figures

Table 1 Comparison of clinical and imaging characteristics between benign and malignant groups.

Table 2 Univariate and multivariable logistic regression analysis.

Table 3 Multiple models with a combination of clinical and imaging parameters.

Fig 1. The flowchart of MRI scanning.

Fig 2. ROC curve analysis. The AUC of model C was 0.950, which was significantly higher than Model A (0.899) and Model B (0.807). The sensitivity and specificity of Model C were 88.89% and 93.75% respectively.

Fig 3. Decision curve analysis. The Y-axis indicates net benefits to patients. The colorful lines represent the models, the gray line represents the hypothesis that all patients were breast cancer, and the black horizontal line represents the hypothesis that no patients were breast cancer. As showed in the curve, the net benefit of model C was better than that of the other 2 models between threshold probabilities of 2-10% and 30%-99%.

Fig 4. Nomogram for prediction of breast cancer. The different values for each variable correspond to a point at the top of the graph, while the sum of the points for all the variables corresponds to a total point, draw a line from the total points to the bottom line is the probability of breast cancer.

Fig 5. Calibration curve analysis. The calibration curve was used to show the relationship between the predicted value and the true value. The X-axis represents the predicted probability, while Y-axis represents observed probability. The red line represents the ideal calibration line, and the black line represents the predictive power of the nomogram. The closer the black line is to the red line, the better the predictive power of the model.

Fig 6. Female, 50.5y, postmenopausal state, BMI=23, breast cancer, A~H. Image respectively T1 Mapping (A), T2 Mapping(B), T1 Contrast Mapping (C), T2 Contrast Mapping (D), T2WI(E), ADC(F), T1WI Contrast(G), Nomogram(H). T1local=1376.13ms, T1+local=337.56ms, ΔT1%local=75.51%, T1tumor=1349.77ms, T1+ tumor=323.05ms, ΔT1%tumor=76.07, T2local=83.07ms, T2+local=78.07ms, ΔT2%local=6.02%, T2tumor=83.01ms, T2+tumor=78.03ms, ΔT2%tumor=6.00%, ADClocal=1.15×10-3mm2/s, ADCtumor=1.40×10-3mm2/s. Total Points=202.50. Probability of malignant=71.00%.

Fig 7 Female, 47y, postmenopausal state, BMI=20, fibroadenoma, A~H. Image respectively T1 Mapping (A), T2 Mapping(B), T1 Contrast Mapping (C), T2 Contrast Mapping (D), T2WI(E), ADC(F), T1WI Contrast(G), Nomogram(H). T1local=1257.38ms, T1+ local=345.15ms, ΔT1%local=72.55, T1tumor=1275.43ms, T1+ tumor=315.03ms, ΔT1%tumor=75.30, T2local=89.67ms, T2+ local=85.17ms, ΔT2%local=5.02%,T2tumor=93.01ms, T2+ tumor=87.90ms, ΔT2%tumor=5.50%, ADClocal=1.18×10-3mm2/s, ADCtumor=1.38×10-3mm2/s. Total Points=182.50. Probability of malignant =34.4%.

Proc. Intl. Soc. Mag. Reson. Med. 29 (2021)
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