By Dr. Marco V. Benavides Sánchez.
In the relentless quest to detect cancer earlier and more accurately, researchers have turned their attention to a tiny but telling clue that floats within our bloodstream: circulating tumor cells (CTCs). These are cancerous cells that break away from a primary tumor and enter the circulatory system, offering a rare but powerful window into early diagnosis, disease progression, and potential therapeutic targets.
The challenge? CTCs are incredibly rare—often just a handful of cells among billions of normal blood cells—and they closely resemble non-cancerous cells, making detection extraordinarily difficult. Now, a team of researchers led by Xuan Zhang and Lei Cao has developed a novel artificial intelligence (AI) system that can detect these elusive cells using common histological staining methods. Their breakthrough, published in the journal Artificial Intelligence in Medicine (Zhang et al., 2025), marks the first time that AI has been used to identify CTCs directly from hematoxylin and eosin (H&E)-stained slide images.
This innovation could revolutionize cancer diagnostics and significantly enhance the precision and speed of early detection—where timing often means the difference between life and death.
What Are Circulating Tumor Cells and Why Do They Matter?
CTCs are malignant cells that detach from a tumor and travel through the bloodstream, potentially seeding metastases in other organs. Their presence is a strong indicator that a tumor exists somewhere in the body—even before it becomes symptomatic or visible via imaging. Tracking CTCs allows physicians to monitor cancer progression, evaluate treatment responses, and assess recurrence risks (Alix-Panabières & Pantel, 2014; Micalizzi et al., 2017).
However, spotting these cells is a daunting task. In a standard 10 mL blood sample, there may be fewer than 10 CTCs, interspersed with billions of normal blood cells. Compounding this problem is their similarity to CTC-like cells—benign or ambiguous cells that visually mimic cancer cells. Current methods typically involve fluorescent labeling and manual examination, both of which are time-consuming, labor-intensive, and prone to human error.
The AI Solution: CMD
The research team’s new model is called CMD, short for Cell-interacting and Multi-correcting Detector. It is a deep learning-based tool specifically designed to tackle the most pressing challenges in CTC detection:
- Identifying rare cancer cells within a vast sea of normal ones.
- Distinguishing CTCs from lookalike non-cancerous cells.
What sets CMD apart from previous attempts is its two novel, task-specific modules:
1. Self-Attention Module
This component allows the AI to “pay attention” to specific areas of an image that contain suspicious or abnormal cells. Inspired by attention mechanisms from natural language processing, this module enables CMD to compare a cell to its surrounding cells—much like a pathologist would when scanning a slide.
It doesn’t treat each cell in isolation. Instead, it analyzes their features in the context of neighboring cells, identifying which ones stand out based on shape, structure, or staining properties.
2. Hard Sample Mining Sampler
In the world of AI, “hard samples” are cases that are tricky to classify—borderline cells that could go either way. This module zeroes in on those difficult cases, helping the system to iteratively learn from its own mistakes. By focusing on ambiguous examples, CMD gradually refines its ability to correctly distinguish real CTCs from confusing lookalikes.
This layered correction strategy is what makes CMD particularly powerful in a clinical setting, where variability in slides and cell morphology is inevitable.
Real-World Testing: A Multi-Center Validation
The CMD system was tested on a large dataset of 1,247 annotated H&E-stained slide images from multiple medical centers. These real-world samples provided a rigorous benchmark for evaluating performance.
The results were impressive. CMD significantly outperformed existing object detection algorithms commonly used for abnormal cell identification. Notably, its performance remained consistent across images from different clinical sites—a key indicator of its robustness and generalizability.
The team also conducted ablation studies (removing one component at a time) to verify the individual contribution of each module. Both the self-attention and hard-sample mining modules were proven to enhance detection accuracy independently, confirming that CMD’s design is not only innovative but also functionally effective.
Usability and Open Access
Another major strength of the CMD system is its practicality. It relies on H&E staining, a ubiquitous and inexpensive method used in pathology labs worldwide. This gives CMD a distinct advantage over techniques that require expensive fluorescent markers or specialized imaging equipment.
Furthermore, the researchers have made the CMD source code publicly available on GitHub: https://github.com/zx333445/CMD. This openness paves the way for other research groups and clinical institutions to test, refine, and potentially integrate CMD into diagnostic workflows.
The Road Ahead
While CMD is a promising advance, there are still hurdles to clear before it becomes a routine tool in hospitals. More extensive validation studies involving diverse patient populations and cancer types are essential. Additionally, integration into clinical decision-making systems will require regulatory approvals and careful design to ensure interpretability and safety.
Yet the implications are profound. Imagine a world where a simple blood sample, analyzed by AI, could provide oncologists with early warnings of cancer—even before a tumor is visible on a scan. CMD brings us one step closer to that future.
Conclusion
The development of CMD by Zhang, Cao, and their colleagues represents a milestone in the application of artificial intelligence to medicine. By emulating the diagnostic strategies of expert pathologists and enhancing them with computational precision, CMD transforms the way we approach early cancer detection.
In an era where personalized and predictive medicine is rapidly becoming the norm, tools like CMD offer not just innovation, but hope—a chance to catch cancer before it spreads, and to tailor treatments with unprecedented accuracy.
For further reading:
- Alix-Panabières, C., & Pantel, K. (2014). Challenges in circulating tumour cell research. Nature Reviews Cancer, 14(9), 623–631. https://doi.org/10.1038/nrc3820
- Micalizzi, D. S., Maheswaran, S., & Haber, D. A. (2017). A conduit to metastasis: circulating tumor cell biology. Genes & Development, 31(18), 1827–1840.
- Zhang, X., Lai, R., Bai, L., Ji, J., Qin, R., Jiang, L., … & Cao, L. (2025). A cell-interacting and multi-correcting method for automatic circulating tumor cells detection. Artificial Intelligence in Medicine, 103164. https://doi.org/10.1016/j.artmed.2025.103164
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