Dual-region MRI radiomic analysis indicates increased risk in high-risk breast lesions: bridging intratumoral and peritumoral radiomics for precision decision-making

Abstract

Objective

To evaluate the clinical utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived clinicoradiological characteristics and intratumoral/peritumoral radiomic features in predicting pathological upgrades (malignant transformation) in high-risk breast lesions.

Materials and methods

Retrospectively collected the data of 174 patients with high-risk breast lesions who underwent preoperative breast MRI examinations and were confirmed by biopsy pathology in Shenzhen People’s Hospital between January 1, 2019 and January 1, 2024. The dataset was randomly divided into a training set (n = 121) and a test set (n = 53) at a ratio of 7:3. Initially, during the second stage of DCE-MRI, the region of interest (ROI) was delineated along the maximum cross-section of the lesion, and then automatically expanded outward by 3 mm, 5 mm, and 7 mm as the peritumoral ROIs. The intratumoral, each peritumoral, and the combined intratumoral and peritumoral radiomic models were established respectively. Independent risk factors predictive of malignant upgrades in high-risk lesions were identified through univariate and multivariable logistic regression analyses, which were subsequently incorporated as clinical and imaging characteristics. Finally, a combined model was established by integrating the intratumoral and peritumoral radiomic features with the clinical and imaging features. The performance of each model was analyzed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC) was calculated.

Results

The peritumoral 3 mm radiomics model achieved the highest diagnostic performance among all the peritumoral models, with the AUC values of 0.704 and 0.654 for the training and test sets, respectively. In the training set, the combined model showed the highest diagnostic performance (AUC = 0.883), which was superior to that of the clinical and imaging features model (AUC = 0.745, P = 0.003), the intratumoral radiomics model (AUC = 0.791, P = 0.027), the peritumoral 3 mm radiomics model (AUC = 0.704, P = 0.001), and the combined intratumoral and peritumoral radiomic model (AUC = 0.830, P = 0.004). In the test set, the combined model also showed the highest diagnostic performance (AUC = 0.851). The combined model constructed by integrating the intratumoral and peritumoral radiomics features with the clinical and imaging features had the best diagnostic performance, with the sensitivity, specificity, and accuracy of 79.4%, 82.7%, and 81.8% in the training set, and 72.7%, 85.7%, and 83.0% in the test set, respectively.

Conclusion

The combined predictive model, which integrates intratumoral and peritumoral radiomic features with clinical and imaging data, exhibited strong diagnostic performance and a clinically applicable nomogram was constructed to stratify individualized upgrade risk, assisting clinicians in making more precise decisions.