Clinical Value of Nomogram Model based on Multimodality Ultrasound Image Characteristics Differentiating Benign and Malignant Breast Masses.

Introduction: Finding a convenient, accurate, and non-invasive method to differentiate between benign and malignant breast masses is especially important for clinical practice, and this study aimed to explore the clinical value of Nomogram model based on multimodality ultrasound image characteristics and clinical baseline data for detecting benign and malignant breast masses.

Methods: A retrospective analysis of the clinical data and ultrasound imaging characteristics of 132 patients with breast masses. Data were randomly divided into a training set (92 cases) and a validation set (40 cases) in a ratio of 7:3. Logistic regression was applied to the training set data to analyze risk factors related to malignant breast masses and to construct a Nomogram model. Clinical applicability of the model was evaluated and validated.

Results: In training set, ROC cure analysis results showed that AUC of Nomogram model constructed with CA15-3, CA125, E_max, E_sd, Ratio of Elastic Moduli, Elasticity Scoring, blurry boundaries, irregular shape, penetrating vessels, and stiff rim sign was 1.00 (95%CI: 0.99-1.00), Hosmer- Lemeshow goodness-of-fit test result showed predicted curve closely aligns with ideal curve, and DCA showed that Nomogram model exhibited high net benefits across multiple thresholds. The clinical applicability of the Nomogram model was also confirmed with consistent results in the validation set.

Discussion: In this study, we constructed a Nomogram model using risk factors associated with malignant breast masses, and the model showed good clinical applicability in distinguishing benign and malignant breast masses. However, this study is a single-center study, and the sample size of the dataset is relatively small, which, to some extent, limits the breadth and depth of validation.

Conclusion: The Nomogram model built on multimodal ultrasound imaging features and clinical data demonstrates a strong discriminative ability for malignant breast masses, allowing patients to achieve a significant net benefit.