Ilmu Komputer & AI editorial
Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence—A Comprehensive Review of Novel Literature
The core problem
Innovation
Liquid biopsy platforms, particularly ctDNA sequencing and methylation profiling, demonstrate increasing clinical utility across the CRC care continuum from population screening to post-surgical minimal residual disease (MRD) detection and real-time therapy monitoring. AI-driven analytical frameworks, including Random Forest, Convolutional Neural Networks, LSTM models, and more recently Large Language Models (LLMs), substantially augment the sensitivity and specificity of liquid biopsy interpretation, enabling multimodal data integration. For example, ctDNA analysis has shown high concordance with tissue genotyping for key mutations such as KRAS, NRAS, and BRAF, and methylation signatures have achieved high accuracy in early CRC detection. AI models have improved the detection of MRD with limits of detection below 0.01% variant allele frequency. The integration of AI with liquid biopsy data has also facilitated the prediction of treatment response and resistance mechanisms, with several studies reporting area under the curve (AUC) values exceeding 0.90 for ML-based classifiers. The table below summarizes key performance metrics from selected studies.
| Biomarker | AI Method | Perf
Why it matters
The convergence of liquid biopsy technology and AI-driven analytics represents a paradigm shift toward precision oncology in CRC. AI algorithms can integrate multimodal data—genomic, transcriptomic, proteomic, and clinical—to provide a holistic view of tumor dynamics. However, remaining challenges include analytical standardization, model explainability, regulatory harmonization, and equitable access. The lack of standardized protocols for sample collection, processing, and sequencing can lead to variability in results, hindering clinical implementation. AI models, particularly deep learning, often operate as black boxes, raising concerns about trust and accountability in clinical decision-making. Regulatory frameworks are still evolving to address the unique challenges of AI-based diagnostics. Moreover, equitable access to these technologies is crucial to avoid exacerbating health disparities. Future integration of federated learning frameworks and LLM-based clinical decision support tools will be essential for responsible clinical translation. Federated learning allows model training across multiple institutions without sharing raw data, addressing privacy concerns. LLMs can synthesize vast amounts of literature and patient data to assist oncologists in real-time decision-making. The following Mermaid diagram illustrates the workflow of liquid biopsy combined with AI for CRC management:
Mathematically, the integration of AI with liquid biopsy can be represented as a function that maps high-dimensional biomarker data to clinical outcomes:
where represents the multimodal biomarker data, are the model parameters learned from training data, and is the error term. The goal is to minimize the loss function
Who should read this
Opening member content…