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Updated: Jun 9, 2025
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Radiomic and clinical model for predicting atypical ductal hyperplasia upgrades and potentially reduce unnecessary
Nicole Brunetti1, Cristina Campi2, Giorgia Biddau3
1Department of Radiology, IRCCS - Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genoa, Italy; Department of Experimental Medicine (DIMES), University of Genova, Via L.B. Alberti 2, 16132 Genoa, Italy.
Objective:
To identify patients with atypical ductal hyperplasia (ADH) at low risk of upgrading to carcinoma. This study aims to assess the performance of radiomics combined with clinical factors to predict occult breast cancer among women diagnosed with ADH.
Methods:
This study retrospectively included microcalcification clusters of patients who underwent Mx and VABB with a diagnosis of ADH at a tertiary center from January 2015 to May 2023. Clinical and radiological data (age, cluster size, BI-RADS classification, mammographic density, breast cancer history, residual microcalcifications) were collected. Surgical outcomes were used to determine upgrade. Four logistic regression models were developed to predict the risk of upgrade. The performance was evaluated using the area under the receiver operating characteristic curve (AUC) and performance scores.
Results:
A total of 143 patients with 153 clusters were included. Twelve radiomic features and six clinical factors were selected for model development. The sample was split into 107 training and 46 test cases. Clinical features achieved an AUC of 0.72 (0.60-0.84), radiomic features an AUC of 0.73 (0.61-0.85). Radiomic features with "cluster size" and "age" improved the AUC to 0.79 (0.67-0.91). The best model, incorporating all data, achieved an AUC of 0.82 (0.71-0.92), a specificity of 0.89 (0.75, 0.97), and NPV of 0.92 (0.78-0.98).
Conclusion:
This study demonstrates the potential of radiomic as a valuable tool for reducing unnecessary treatments for patient classified as "low risk of ADH upgrade". Combining radiomic information with clinical data improved the accuracy of risk prediction.
