Integrative approaches in lung cancer diagnosis: bridging molecular biomarkers and AI driven imaging
The convergence of biomarker research, multi-omics technologies, and AI-driven analytics is reshaping lung cancer diagnostics toward more precise and personalized approaches. However, challenges related to data standardization, interpretability, clinical validation, and ethical considerations must be addressed to enable widespread clinical implementation.
Authors
- Shammy Jindal (ORCID: https://orcid.org/0000-0002-1840-9088)
- Kamya Goyal (ORCID: https://orcid.org/0000-0002-2677-0922)
- Purabi Saha (ORCID: https://orcid.org/0000-0001-5153-7551)
- Vikramdeep Monga (ORCID: https://orcid.org/0000-0003-4004-6241)
- Aarti Passi (ORCID: https://orcid.org/0009-0005-6420-8323)
- Azra Yasmin (ORCID: https://orcid.org/0009-0009-3552-8415)
- Manpreet Kaur
- Ritesh Jha
Institutions
- Central University of Punjab (IN)
- Canadian Institutes of Health Research (CA)
- American Association of Colleges of Pharmacy (US)
- Pharmaceutical Formulations (United States) (US)
Publication Details
- Journal
- Biomarkers
- Published
- 2026-03-14
- DOI
- https://doi.org/10.1080/1354750x.2026.2644329
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00