Multi-site observational evaluation of Breast AI™-supported ultrasound risk stratification in breast cancer assessment pathways
- 1Malherbe Imaging Inc., Diagnostic Imaging, Pretoria, South Africa
- 2Department of Immunology, University of Witwatersrand, Johannesburg, South Africa
- 3Department of Family Medicine, University of Pretoria, Pretoria, South Africa
Abstract
Purpose: To evaluate the diagnostic performance metrics, referral patterns, and real-world implementation characteristics associated with a Breast AI™-supported ultrasound assessment pathway across multiple heterogeneous healthcare settings. Materials and methods: This prospective multi-site observational study included 1,129 women undergoing routine or symptom-driven breast assessment across five healthcare facilities in South Africa between April 2023 and April 2025. Breast AI™ was used as an adjunctive ultrasound-based clinical decision-support tool alongside standard clinical assessment and breast ultrasound imaging. Referral outcomes and histopathological diagnoses were recorded where clinically indicated. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated using predefined dichotomised Breast AI™ risk categories. Site-level comparisons were assessed using chi-square analysis. Results: Among 1,129 clinical assessments, 417 patients underwent referral for further diagnostic evaluation, with 405 histopathologically confirmed malignancies identified. Referral-associated malignancy rates remained relatively consistent across participating healthcare sites, with no statistically significant site-level differences observed (χ² = 1.67, p = 0.795). Diagnostic performance analysis demonstrated a sensitivity of 99.3% (95% CI: 97.8–99.8), specificity of 69.8% (95% CI: 66.3–73.1), PPV of 64.7% (95% CI: 61.2–68.0), and NPV of 99.4% (95% CI: 98.3–99.8). Higher Breast AI™ risk classifications were more frequently associated with invasive ductal carcinoma and invasive lobular carcinoma, whereas approximately 30% of ductal carcinoma in situ cases were classified within the low-risk category. The median interval between Breast AI™ assessment and histopathological confirmation was 18 days (IQR: 10–32 days). Conclusion: In this prospective observational multi-site cohort, Breast AI™-supported ultrasound assessment demonstrated high observed negative predictive value and reproducible risk stratification across heterogeneous healthcare environments. However, interpretation of diagnostic accuracy metrics is limited by differential histopathological verification and the absence of long-term interval cancer follow-up among low-risk patients. The findings support the feasibility of integrating AI-supported ultrasound assessment into resource-variable clinical settings, while highlighting the need for future comparative, longitudinal, and health-economic evaluation studies
Related
- Geriatric Assessment-Based Stratification to Evaluate Treatment Intensity, Quality of Life, and Survival in Older Patients With Metastatic Colorectal Cancer: Protocol for a Prospective Observational Study.
- Feasibility and Operator-Reported Utility of Transesophageal Lung Ultrasound As an Adjunct to Resuscitative Transesophageal Echocardiography in the Evaluation of Shock: A Multicenter Observational Study
- Preoperative ultrasound assessment of gastric volume in diabetic patients scheduled for elective surgery: a prospective, observational, comparative cohort study.
- Core needle biopsy washout biomarkers combined with ultrasound for preoperative axillary risk stratification in breast cancer: a retrospective nomogram study with temporal validation
- Artificial intelligence-based prostate cancer risk stratification via large-scale ultrasound images paired with histopathology
BibTeX
@article{malherbe2026multisite,
title = {{Multi-site observational evaluation of Breast AI™-supported ultrasound risk stratification in breast cancer assessment pathways}},
author = {Kathryn Malherbe and Carol A. Benn and Gerhard Botha},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357975},
url = {https://doi.org/10.1371/journal.pone.0357975},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi-site observational evaluation of Breast AI™-supported ultrasound risk stratification in breast cancer assessment pathways
AU - Kathryn Malherbe
AU - Carol A. Benn
AU - Gerhard Botha
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0357975
UR - https://doi.org/10.1371/journal.pone.0357975
AB - Purpose: To evaluate the diagnostic performance metrics, referral patterns, and real-world implementation characteristics associated with a Breast AI™-supported ultrasound assessment pathway across multiple heterogeneous healthcare settings. Materials and methods: This prospective multi-site observational study included 1,129 women undergoing routine or symptom-driven breast assessment across five healthcare facilities in South Africa between April 2023 and April 2025. Breast AI™ was used as an adjunctive ultrasound-based clinical decision-support tool alongside standard clinical assessment and breast ultrasound imaging. Referral outcomes and histopathological diagnoses were recorded where clinically indicated. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated using predefined dichotomised Breast AI™ risk categories. Site-level comparisons were assessed using chi-square analysis. Results: Among 1,129 clinical assessments, 417 patients underwent referral for further diagnostic evaluation, with 405 histopathologically confirmed malignancies identified. Referral-associated malignancy rates remained relatively consistent across participating healthcare sites, with no statistically significant site-level differences observed (χ² = 1.67, p = 0.795). Diagnostic performance analysis demonstrated a sensitivity of 99.3% (95% CI: 97.8–99.8), specificity of 69.8% (95% CI: 66.3–73.1), PPV of 64.7% (95% CI: 61.2–68.0), and NPV of 99.4% (95% CI: 98.3–99.8). Higher Breast AI™ risk classifications were more frequently associated with invasive ductal carcinoma and invasive lobular carcinoma, whereas approximately 30% of ductal carcinoma in situ cases were classified within the low-risk category. The median interval between Breast AI™ assessment and histopathological confirmation was 18 days (IQR: 10–32 days). Conclusion: In this prospective observational multi-site cohort, Breast AI™-supported ultrasound assessment demonstrated high observed negative predictive value and reproducible risk stratification across heterogeneous healthcare environments. However, interpretation of diagnostic accuracy metrics is limited by differential histopathological verification and the absence of long-term interval cancer follow-up among low-risk patients. The findings support the feasibility of integrating AI-supported ultrasound assessment into resource-variable clinical settings, while highlighting the need for future comparative, longitudinal, and health-economic evaluation studies
SN - 1932-6203
ER -