AI Tools Detect Early-Stage Cancer Using Simple Blood Test

Early cancer detection remains a major challenge, particularly in low- and middle-income countries, where access to advanced imaging, specialized laboratories, and trained oncologists is limited. Many cancers are diagnosed only at advanced stages, when treatment options are fewer, and survival rates are lower. A new artificial intelligence (AI)-based approach now suggests that early-stage cancers could be detected using a simple blood test, even with basic laboratory infrastructure.

Researchers at the Indraprastha Institute of Information Technology, Delhi (IIIT-Delhi, New Delhi, India) have developed an affordable and scalable pan-cancer blood test that utilizes AI to detect cancer-associated molecular signals. The approach is based on analyzing tumor-educated platelets, blood components whose molecular profiles are altered in the presence of cancer, including at early stages.

Tumor-educated platelets carry cancer-induced molecular information that can be detected from a small blood sample. Using machine learning algorithms, the AI model identifies cancer-specific patterns within these platelet signals and distinguishes between multiple cancer types. The system is designed to function without high-end sequencing platforms or specialized expertise, making it suitable for widespread deployment in resource-limited healthcare settings.

According to the research team, the AI-driven test is capable of detecting cancers at Stage I and II, when treatment outcomes are generally more favorable. Because the method uses routine blood components and computational analysis rather than invasive biopsies or expensive imaging, it offers a cost-effective alternative to conventional diagnostics. The researchers emphasize that the approach is intended to complement, rather than replace, existing diagnostic pathways.

In parallel, the team is applying AI and big-data analytics to single-cell genomics to understand why cancers often survive therapy and recur. By analyzing cellular heterogeneity within tumors, the researchers are mapping how different malignant and immune cell subtypes interact and respond to treatment. Their “cell algebra” framework allows virtual manipulation of cell populations to identify the most treatment-resistant cancer cells and prioritize therapeutic strategies.

The broader research program also includes AI-driven prediction of patient-specific drug responses by integrating genomics, chemical data, imaging, pathology, and clinical records. These tools could support more precise treatment selection and reduce trial-and-error approaches in oncology. With India’s large patient population, expanding biobanks, and growing digital health infrastructure, the researchers see strong potential for national and global impact.

“Our lab develops scalable AI and big-data algorithms to analyze massive single-cell genomics data, allowing us to decode cancer cell heterogeneity,” said IIIT-Delhi Professor Debarka Sengupta, who led the research. “By modelling how different malignant and immune cell subtypes interact and survive treatment, we can identify the most lethal cancer cells within a tumor. Our ‘cell algebra’ approach enables virtual addition and subtraction of cell types, offering a new way to prioritize therapies and understand why a single drug cannot eliminate all cancer cells, opening a new window into cancer biology and clinical management.”

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