European journal of radiology open · 2026
DCE-MRI kinetic heterogeneity facilitates the discrimination of benignity and malignancy of breast in combination with the DWI and morphological features.
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Wang Ruobing, Chen Yanhong, Luo Chen, Wang Lijun, Luo Ran, Zhan Ling, Zhang Zhengwei, Wang Minmin, Yin Qiufeng, Zhang Zhongyang, Yan Lijun, Zhang Yuzhen, Wang Dengbin, Liu Huanhuan
Аннотация
PURPOSE: This study aimed to validate the diagnostic value of kinetic heterogeneity (KH) across multiple cohorts and to develop a nomogram integrating KH, apparent diffusion coefficient (ADC), and key morphological and clinical features for preoperative discrimination between benign and malignant breast masses.
METHODS: This retrospective study included 501 female patients with 541 confirmed breast mass lesions. Training (n = 280) and internal validation (n = 119) cohorts were collected (2018-2019). Temporal validation (n = 101, 2024) and independent test cohorts (n = 41, 2025-2026), from a different scanner system) were also included. KH was computed using voxel-wise time-intensity curve analysis. Independent predictors were identified using logistic regression to construct a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, and compared with KH-, ADC-, and combined models.
RESULTS: Age, lesion margin, ADC, and KH were independent predictors. The nomogram achieved AUCs of 0.897 (95% confidence interval [CI]: 0.838-0.955) and 0.909 (95% CI: 0.811-1.000) in the temporal validation and independent test cohorts, outperforming the ADC + KH (0.855, 0.890), ADC (0.829, 0.848), and KH (0.803, 0.757) models. Sensitivities were 93.8% and 94.1%, with negative predictive values (NPVs) of 92.7% and 95.2%, respectively. Calibration and decision curve analysis demonstrated good agreement and positive net benefit across a wide range of threshold probabilities.
CONCLUSION: The nomogram integrating KH, ADC, margin, and age demonstrated superior diagnostic performance in preoperatively discriminating between benign and malignant breast mass lesions.
METHODS: This retrospective study included 501 female patients with 541 confirmed breast mass lesions. Training (n = 280) and internal validation (n = 119) cohorts were collected (2018-2019). Temporal validation (n = 101, 2024) and independent test cohorts (n = 41, 2025-2026), from a different scanner system) were also included. KH was computed using voxel-wise time-intensity curve analysis. Independent predictors were identified using logistic regression to construct a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, and compared with KH-, ADC-, and combined models.
RESULTS: Age, lesion margin, ADC, and KH were independent predictors. The nomogram achieved AUCs of 0.897 (95% confidence interval [CI]: 0.838-0.955) and 0.909 (95% CI: 0.811-1.000) in the temporal validation and independent test cohorts, outperforming the ADC + KH (0.855, 0.890), ADC (0.829, 0.848), and KH (0.803, 0.757) models. Sensitivities were 93.8% and 94.1%, with negative predictive values (NPVs) of 92.7% and 95.2%, respectively. Calibration and decision curve analysis demonstrated good agreement and positive net benefit across a wide range of threshold probabilities.
CONCLUSION: The nomogram integrating KH, ADC, margin, and age demonstrated superior diagnostic performance in preoperatively discriminating between benign and malignant breast mass lesions.