Related Experiment Video
Updated: Sep 26, 2026
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Preoperative Prediction of Ductal Carcinoma In Situ Upstaging Using Machine Learning-Based Peritumoral Breast MRI
K Ruwani M Fernando1, Issam El Naqa1, Mahmoud Abdalah2
1Department of Machine Learning, H. Lee Moffitt Cancer Center, Tampa, Florida, USA.
Background:
A subset of biopsy-confirmed ductal carcinoma in situ (DCIS) cases can be upstaged to invasive breast cancer at surgery. Preoperative identification of upstaging risk is essential for treatment planning.
Purpose:
To develop clinical and breast MRI radiomics models to preoperatively predict upstaging in DCIS.
Study Type:
Retrospective and prospective.
Population:
Three hundred forty-five women (median age, 59 years [IQR, 49-67]) with 362 DCIS lesions diagnosed via core-needle biopsy (training: n = 216, internal testing: n = 53, external testing: n = 93).
Field Strength/Sequences:
1.5 T 3D T1-weighted spoiled gradient-echo MRI: pre-contrast non-fat-suppressed and dynamic contrast-enhanced (DCE) fat-suppressed sequences.
Assessments:
Clinical and radiomics features were extracted from radiologist-segmented lesions on DCE breast MRI. Seven machine learning algorithms were evaluated across clinical-only (demographic and clinicopathologic variables), radiomics-only (whole-lesion/peritumoral), and combined models, using nested cross-validation and internal and external testing. Feature importance was assessed using Shapley additive explanation (SHAP).
Statistical Tests:
Mann-Whitney, Chi-square, DeLong tests; area under the receiver operating characteristic curve (AUC), sensitivity, negative predictive value (NPV).
Results:
The clinical model achieved AUC 0.70 (95% CI: 0.51-0.89), with 36% sensitivity and 84% NPV. A multilayer perceptron integrating clinical and peritumoral radiomics non-significantly increased AUC to 0.75 (95% CI: 0.58-0.92; ΔAUC = +0.05; False Discovery Rate p-adj = 0.65), improving sensitivity to 82% (Δ = +46%) and NPV to 92% (Δ = +8%). On external testing, AUC was 0.63 (95% CI: 0.50-0.76) with no significant change over the clinical baseline (ΔAUC = -0.03; FDR p-adj = 0.857) but demonstrated superior sensitivity (68%; Δ = +30%) and NPV (84%; Δ = +3%). SHAP identified peritumoral center-of-mass shift as the dominant predictor.
Data Conclusion:
Clinical-peritumoral modeling did not significantly improve discrimination over the clinical baseline but showed higher NPV and sensitivity, suggesting the tumor microenvironment may provide complementary information for upstaging risk stratification.
Technical Efficacy:
Stage 2.
