突破可规模化多癌种早期检测（MCED）的障碍

In recent years, MCED via liquid biopsy has risen to prominence for its potential to shift the paradigm of cancer detection from one to many, and from late to early stages. Yet, realizing this promise requires addressing several fundamental challenges, without which MCED risks remaining confined to expensive niche markets rather than becoming a broadly practical clinical tool. These challenges comprise technical, methodological, psychological, and economic factors. While detection at all early stages is challenging, detecting solid cancers at Stage I is significantly more demanding compared to Stage II. For the four most common solid cancers, volumetric measurements suggest that Stage I is roughly 5 to 30 times harder to detect than Stage II. This nonlinear challenge, compounded by the inherent nature of some cancer types, highlights the demanding sensitivity requirements of a true early detection scheme and the limitations faced by many advanced molecular platforms. Specificity poses a parallel challenge. Maintaining high specificity while simultaneously preserving strong Stage I sensitivity is intrinsically difficult because early cancer signals can closely resemble healthy baselines. A compromise in Stage I sensitivity, for instance, could translate to apparent increased specificity. This, in turn, could lead to a perceived exaggeration of the positive predictive value (PPV) if not interpreted with caution. Rigorous cohort design is essential to prevent distortion of accuracy. Careful consideration of age, comorbidities, and related variables is crucial to avoid significant consequences. For example, an older-skewed cohort could falsely inflate the perceived PPV. Similarly, age differences between cancer and healthy groups can mislead metrics, as older individuals' non-cancer signals may falsely raise apparent sensitivity. Psychological factors can hinder cancer screening, with fear and anxiety particularly high in MCED, which targets multiple cancers simultaneously. Employing MCED as a triage rather than a definitive screening test can reduce these concerns, as positive results prompt confirmatory screening. However, while this approach makes MCED an accessible entry point, it would demand high sensitivity, particularly at the earliest cancer stages. Lastly, cost remains a critical consideration. For MCED to achieve widespread adoption, it must be economically viable, ensuring cost-effectiveness, accessible reimbursement pathways, and consequently affordability at the population level. These economic factors will be key to its real-world feasibility. In this work, we present a low-cost, high-sensitivity/specificity MCED leveraging proteomics and machine intelligence, initially targeting breast, lung, prostate, and colon cancers. By addressing the key challenges, we demonstrate how MCED can evolve from a costly niche market to a clinically actionable tool for large-scale early cancer detection.