Integrating surface-enhanced Raman scattering with machine learning: Pioneering a comprehensive diagnostic and therapeutic platform for cancer management
Heng-Zhou He , La Zhang , Yi-Lin Wen , Yan-Yang Wang , Jun-Yan Zhang , Fei-Hao Yao , Ji-Yao Yu , Jing-Xian Wu , Qi-Ling Peng , Ning Jiang
Interdisciplinary Medicine ›› 2025, Vol. 3 ›› Issue (5) : e20250037
Integrating surface-enhanced Raman scattering with machine learning: Pioneering a comprehensive diagnostic and therapeutic platform for cancer management
The integration of Surface-Enhanced Raman Scattering (SERS) with machine learning heralds a transformative era in cancer management, offering a non-invasive, expedited, and comprehensive approach for early diagnosis, targeted therapy, and continuous monitoring. As SERS penetrates the molecular intricacies of cancerous tissues, its conjunction with advanced machine learning algorithms enhances diagnostic accuracy, enabling the discernment of subtle biochemical cues critical for early-stage detection and precise therapeutic targeting, and holds promise for establishing a systematic platform for cancer from diagnosis to therapy. This review explores the synergistic potential of these technologies advocating for their expanded application across the diagnostic spectra and images to revolutionize the therapeutic landscape of cancer. By harnessing this integrated approach, we propose the development of an intelligent platform that promises to refine cancer management, thereby redefining oncological diagnostics and care.
biomarker detection / cancer diagnostics / machine learning / nanomedicine / surface-enhanced Raman scattering (SERS)
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2025 The Author(s). Interdisciplinary Medicine published by Wiley-VCH GmbH on behalf of Nanfang Hospital, Southern Medical University.
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