Validation of a deep learning–based AI system for HER2-targeted breast cancer assessment using ultrasound imaging in a clinical setting - Malherbe, Kathryn (Frontiers in Oncology, 2025)
BackgroundThis study evaluates the performance of a deep learning–based artificial intelligence (AI) system developed under the Stradexa (a branded form of doxorubicin used regionally in South Africa) initiative, designed for real-time risk stratification and treatment monitoring in HER2-positive breast cancer. Conducted in a routine clinical setting, the system’s predictive capacity was assessed by comparing AI-generated risk scores derived from B-mode ultrasound with histopathology, immunohistochemistry, and treatment response in patients undergoing trastuzumab or doxorubicin therapy. The AI tool demonstrated favorable diagnostic accuracy and a meaningful correlation between risk score reduction and tumor response during therapy, particularly in the trastuzumab group. These findings support the integration of AI-assisted ultrasound for personalized oncology management.ObjectivesThis study aims to evaluate the effectiveness of Herceptin (trastuzumab) compared to Stradexa (a branded form of doxorubicin used regionally in South Africa) (doxorubicin) in reducing Breast AI–predicted malignancy risk percentages and to assess the feasibility of using a deep learning–based AI system for monitoring treatment response in breast cancer.MethodsA total of 86 patients were selected from a larger cohort of 150, based on inclusion criteria of histologically confirmed breast cancer, availability of baseline and follow-up ultrasound scans, and ongoing chemotherapy with either transtumazub or doxorubicin. Patients with incomplete imaging, prior treatment, or other malignancies were excluded. The sample size of 86 provided borderline statistical power (~0.74) to detect moderate effect sizes between treatment groups, considering an alpha of 0.05. B-mode ultrasound images were analyzed using a convolutional neural network–driven Breast AI platform to generate malignancy risk percentages before and during treatment. Statistical analysis was performed to evaluate within-group and between-group changes in AI scores using appropriate inferential methods. All results, interpretations, and manuscript content were produced entirely by human researchers without the use of generative AI tools.ConclusionThese findings highlight the potential of AI-based imaging tools to support real-time treatment monitoring in breast cancer. The observed trend favoring Herceptin suggests that AI-generated risk scores may serve as non-invasive indicators of treatment efficacy. Broader validation across larger, more diverse cohorts is warranted to confirm these preliminary results and further develop AI-guided oncology workflows.