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Habitat Radiomics on Ultrasound Predicts Pathological Upgrade of Ductal Carcinoma In Situ
1Department of Ultrasound, Baoding No.1 Central Hospital, Baoding, Hebei, China (Z.X., X.L.); Graduate School of Chengde Medical University (Z.X.).
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.
