Habitat Radiomics on Ultrasound Predicts Pathological Upgrade of Ductal Carcinoma In Situ.

RATIONALE AND

OBJECTIVES

To develop and validate an ultrasound-based habitat radiomics model for the preoperative prediction of pathologic upgrade in patients with ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB).

MATERIALS AND

METHODS

This retrospective study included 167 female patients (September 2015-August 2025) diagnosed with DCIS by CNB, randomly divided into training (n = 116) and test (n = 51) cohorts. Clinical risk factors were identified using univariate and multivariable logistic regression. Tumor regions of interest were segmented into three habitat subregions (K = 3) via K-means clustering based on 39 pixel-level features. Four predictive models (clinical, radiomics, habitat, and combined) were constructed using a Random Forest classifier. Performance was evaluated using the receiver operating characteristic curves, DeLong's test, calibration curves, and decision curve analysis (DCA).

RESULTS

Adler blood-flow grade (OR = 1.201) and high Ki-67 expression (OR = 1.469) were independent clinical predictors (P < 0.05). In the test cohort, the Habitat model (AUC = 0.891) significantly outperformed the conventional Radiomics model (AUC = 0.741, P < 0.05). The Combined model (Habitat+Clinical) achieved the highest predictive performance (AUC = 0.925; accuracy = 0.863; specificity = 0.920). The combined model showed excellent calibration (Hosmer-Lemeshow P > 0.05) and provided the greatest net benefit in DCA.

CONCLUSION

An ultrasound-based habitat radiomics model, combined with clinical predictors, provides a robust noninvasive tool for predicting pathologic upgrade in CNB-diagnosed DCIS. This model may assist clinical decision-making and support individualized treatment planning.