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Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers.

Authors

Parwekar P,Agrawal KK,Ali J,Gundagatti S,Rajpoot DS,Ahmed T,Vidyarthi A

Affiliations (6)

  • GITAM School of Technology, GITAM Univeristy, Hyderabad, India.
  • Galgotias University, Greater Noida, India.
  • Bennett University Noida, Noida, India.
  • Jaypee Institute of Information Technology Noida, Noida, India.
  • Central University of Punjab, Bathinda, Punjab, India.
  • Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, India.

Abstract

<b><i>Background:</i></b> 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. <b><i>Method:</i></b> 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.

Topics

Journal Article

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