Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers.
1/5 보강
[BACKGROUND] Accurate and noninvasive breast cancer grading and therapy monitoring remain critical challenges in oncology.
APA
Parwekar P, Agrawal KK, et al. (2026). Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers.. Cancer biotherapy & radiopharmaceuticals, 10849785251383328. https://doi.org/10.1177/10849785251383328
MLA
Parwekar P, et al.. "Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers.." Cancer biotherapy & radiopharmaceuticals, 2026, pp. 10849785251383328.
PMID
41027412
Abstract
[BACKGROUND] Accurate and noninvasive breast cancer grading and therapy monitoring remain critical challenges in oncology. Traditional methods often rely on invasive histopathological assessments or imaging-only techniques, which may not fully capture the molecular and morphological intricacies of tumor response.
[METHOD] This article presents a novel, noninvasive framework for breast cancer analysis and therapy monitoring that combines two parallel mechanisms: (1) a dual-stream convolutional neural network (CNN) processing high-intensity ultrasound images, and (2) a biomarker-aware CNN stream utilizing patient-specific breast cancer biomarkers, including carbohydrate antigen 15-3, carcinoembryonic antigen, and human epidermal growth factor receptor 2 levels. The imaging stream extracts spatial and morphological features, while the biomarker stream encodes quantitative molecular indicators, enabling a multimodal understanding of tumor characteristics. The outputs from both streams are fused to predict the cancer grade (G1-G3) with high reliability.
[RESULTS] Experimental evaluation on a cohort of pre- and postchemotherapy patients demonstrated the effectiveness of the proposed approach, achieving an overall grading accuracy of 97.8%, with an area under the curve of 0.981 for malignancy classification. The model also enables quantitative post-therapy analysis, revealing an average tumor response improvement of 41.3% across the test set, as measured by predicted regression in grade and changes in biomarker-imaging correlation.
[CONCLUSIONS] This dual-parallel artificial intelligence strategy offers a promising noninvasive alternative to traditional histopathological and imaging-alone methods, supporting real-time cancer monitoring and personalized treatment evaluation. The integration of high-resolution imaging with biomolecular data significantly enhances diagnostic depth, paving the way for intelligent, patient-specific breast cancer management.
[METHOD] This article presents a novel, noninvasive framework for breast cancer analysis and therapy monitoring that combines two parallel mechanisms: (1) a dual-stream convolutional neural network (CNN) processing high-intensity ultrasound images, and (2) a biomarker-aware CNN stream utilizing patient-specific breast cancer biomarkers, including carbohydrate antigen 15-3, carcinoembryonic antigen, and human epidermal growth factor receptor 2 levels. The imaging stream extracts spatial and morphological features, while the biomarker stream encodes quantitative molecular indicators, enabling a multimodal understanding of tumor characteristics. The outputs from both streams are fused to predict the cancer grade (G1-G3) with high reliability.
[RESULTS] Experimental evaluation on a cohort of pre- and postchemotherapy patients demonstrated the effectiveness of the proposed approach, achieving an overall grading accuracy of 97.8%, with an area under the curve of 0.981 for malignancy classification. The model also enables quantitative post-therapy analysis, revealing an average tumor response improvement of 41.3% across the test set, as measured by predicted regression in grade and changes in biomarker-imaging correlation.
[CONCLUSIONS] This dual-parallel artificial intelligence strategy offers a promising noninvasive alternative to traditional histopathological and imaging-alone methods, supporting real-time cancer monitoring and personalized treatment evaluation. The integration of high-resolution imaging with biomolecular data significantly enhances diagnostic depth, paving the way for intelligent, patient-specific breast cancer management.