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Published on: December 15, 2014
Prediction of Microinvasion in Breast Ductal Carcinoma in Situ Using Conventional Ultrasound Combined with
Tingting Wu1, Jing Chen2, Sihui Shao2
1Department of Ultrasound, Shanghai General Hospital of Nanjing Medical University, Shanghai, China; Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background:
To develop and validate a model based on conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) features to preoperatively predict microinvasion in breast ductal carcinoma in situ (DCIS).
Patients And Methods:
Data from 163 patients with DCIS who underwent CUS and CEUS from the internal hospital was retrospectively collected and randomly apportioned into training and internal validation sets in a ratio of 7:3. External validation set included 56 patients with DCIS from the external hospital. Univariate and multivariate logistic regression analysis were performed to determine the independent risk factors associated with microinvasion. These factors were used to develop predictive models. The performance was evaluated through calibration, discrimination, and clinical utility.
Results:
Multivariate analysis indicated that centripetal enhancement direction (odds ratio [OR], 13.268; 95% confidence interval [CI], 3.687-47.746) and enhancement range enlarged on CEUS (OR, 4.876; 95% CI, 1.470-16.181), lesion size of ≥20 mm (OR, 3.265; 95% CI, 1.230-8.669) and calcification detected on CUS (OR, 5.174; 95% CI, 1.903-14.066) were independent risk factors associated with microinvasion. The nomogram incorporated the CUS and CEUS features achieved favorable discrimination (AUCs of 0.850, 0.848, and 0.879 for the training, internal and external validation datasets), with good calibration. The nomogram outperformed the CUS model and CEUS model (all P < .05). Decision curve analysis confirmed that the predictive nomogram was clinically useful.
Conclusion:
The nomogram based on CUS and CEUS features showed promising predictive value for the preoperative identification of microinvasion in patients with DCIS.
