A Nomogram Integrating Clinical and Ultrasonographic Features for Preoperative Differentiation of Invasive Ductal Carcinoma and Invasive Lobular Carcinoma of the Breast

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Abstract

Background/Objectives: To develop and validate a preoperative nomogram incorporating clinical and ultrasonographic features for the non-invasive differentiation of invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) of the breast. Methods: Preoperative clinical information and ultrasonographic features of patients with pathologically confirmed IDC and ILC were retrospectively collected. A total of 803 patients (600 with IDC and 203 with ILC) were enrolled and randomly allocated to training and validation sets in an 8:2 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors for differentiation. These predictors were subsequently incorporated into a nomogram and a corresponding weight plot. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), while calibration was evaluated using calibration curves. Clinical net benefit was determined through decision curve analysis (DCA). Results: Significant differences were noted between IDC and ILC in multiple clinical and ultrasonographic characteristics (p < 0.05). Multivariate logistic regression analysis in the training set identified lesion margin, shape, depth, menopausal status, palpability, lesion classification, and internal echo as independent predictors of ILC. Notably, our constructed nomogram exhibited favorable predictive performance, calibration, and clinical utility in both the training and validation sets. Conclusions: A nomogram incorporating seven independent predictors, namely lesion margin, shape, depth, menopausal status, palpability, lesion classification, and internal echo, was developed and validated. This nomogram enables individualized and quantitative prediction of the preoperative probability of ILC and may serve as a non-invasive adjunct to support surgical decision-making.

Abstract as published, via PubMed.