Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

Abstract

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

“…These include generating and interpreting predictive reports, pinpointing potential tumor locations and types, and managing cancer-related data such as clinical follow-up and treatment options. 213 The integration of AI in cancer research is not only transformative but also necessitates a concerted effort to address inherent biases and technical challenges. 214 As AI continues to evolve, its applications in cancer research, from pathology to drug discovery, promise to significantly enhance our understanding and treatment of cancer.…”

Technology and Future of Multi-Cancer Early Detection

“…Protein profiling, utilizing advanced proteomic technologies, shows promise for early cancer detection [23]. One method involves analyzing circulating proteins in biofluids like blood, urine, or saliva, potentially bearing cancer signatures.…”

Application of AI in detection of breast cancer with laboratory results monitoring

“…Artificial intelligence (AI) is playing a critical role in advancing the identification and analysis of biomarkers and tumor markers in the laboratory setting for breast cancer research including biochemical and molecular biomarkers [16,17]. Biomarkers are biological indicators that can be measured to assess the presence or progression of a disease, while tumor markers are specific proteins or other substances produced by tumor cells that can be detected in blood or tissue samples [18,19].…”