Dual-Parallel Artificial Intelligence Framework for Breast Cancer Grading via High-Intensity Ultrasound and Biomarkers
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.
Authors
- Ankit Vidyarthi (ORCID: https://orcid.org/0000-0002-8026-4246)
- Shilpa Gundagatti (ORCID: https://orcid.org/0000-0002-8641-1753)
- Krishna Kant Agrawal (ORCID: https://orcid.org/0000-0001-5788-9136)
- Jabir Ali (ORCID: https://orcid.org/0000-0001-5383-1535)
- Pritee Parwekar (ORCID: https://orcid.org/0000-0002-2439-1507)
- Tanveer Ahmed (ORCID: https://orcid.org/0000-0002-8729-5707)
- Dharmveer Singh Rajpoot
Institutions
- Central University of Punjab (IN)
- Jaypee Institute of Information Technology (IN)
- Galgotias University (IN)
- Symbiosis International University (IN)
- Bennett University (IN)
- GITAM University (IN)
Publication Details
- Journal
- Cancer Biotherapy and Radiopharmaceuticals
- Published
- 2025-09-30
- DOI
- https://doi.org/10.1177/10849785251383328
- Citations
- 1
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 2.48