Cancer
AI-powered blood test first to spot earliest sign of breast cancer
A new screening method that combines laser analysis with a type of AI is the first of its kind to identify patients in the earliest stage of breast cancer, a study suggests.
The fast, non-invasive technique reveals subtle changes in the bloodstream that occur during the initial phases of the disease, known as stage 1a, which are not detectable with existing tests.
The researchers say their new method could improve early detection and monitoring of the disease and pave the way for a screening test for multiple forms of cancer.
Standard tests for breast cancer can include a physical examination, x-ray or ultrasound scans or analysis of a sample of breast tissue, known as a biopsy. Existing early detection strategies rely upon screening people based on their age or if they are in at-risk groups.
Using the new method, researchers were able to spot breast cancer at the earliest stage by optimising a laser analysis technique – known as Raman spectroscopy – and combining it with machine learning, a form of AI.
Similar approaches have been trialled to screen for other types of cancer, but the earliest they could detect disease was at stage two, the team says.
The new technique works by first shining a laser beam into blood plasma taken from patients. The properties of the light after it interacts with the blood are then analysed using a device called a spectrometer to reveal tiny changes in the chemical make-up of cells and tissues, which are early indicators of disease.
A machine learning algorithm is then used to interpret the results, identifying similar features and helping to classify samples.
In the pilot study involving 12 samples from breast cancer patients and 12 healthy controls, the technique was 98 per cent effective at identifying breast cancer at stage 1a.
The test could also distinguish between each of the four main subtypes of breast cancer with an accuracy of more than 90 per cent, which could enable patients to receive more effective, personalised treatment, the team says.
Implementing this as a screening test would help identify more people in the earliest stages of breast cancer and improve the chances of treatment being successful, the team says. They aim to expand the work to involve more participants and include tests for early forms of other cancer types.
Blood samples used in the study were provided by the Northern Ireland Biobank and Breast Cancer Now Tissue Bank.
Dr Andy Downes, of the University of Edinburgh’s School of Engineering, who led the study, said: “Most deaths from cancer occur following a late-stage diagnosis after symptoms become apparent, so a future screening test for multiple cancer types could find these at a stage where they can be far more easily treated. Early diagnosis is key to long-term survival, and we finally have the technology required.
“We just need to apply it to other cancer types and build up a database, before this can be used as a multi-cancer test.”
Agilent has FDA approval for a test to identify ovarian cancer patients who may be eligible for immunotherapy.
Agilent’s PD-L1 IHC 22C3 pharmDx is the only FDA-approved companion diagnostic to help identify patients with epithelial ovarian, fallopian tube or primary peritoneal carcinoma whose tumours express PD-L1 and who may be eligible for treatment with KEYTRUDA, Merck’s anti-PD-1 therapy.
A companion diagnostic is a test used alongside a specific treatment to show whether a patient is suitable for that therapy. PD-L1 is a protein on some cancer cells that helps tumours evade the immune system.
These cancers affect the reproductive system and the lining of the abdominal cavity.
The test enables pathologists to assess PD-L1 expression at diagnosis to support treatment decisions in a disease where options remain limited for many.
This is the seventh FDA-approved companion diagnostic indication for PD-L1 IHC 22C3 pharmDx for use with KEYTRUDA.
Nina Green, vice president and general manager of Agilent’s clinical diagnostics division, said: “Delivering effective precision oncology requires close collaboration between diagnostics and therapeutics, and this FDA approval reflects Agilent’s long-standing industry partnership in companion diagnostics.
“We are proud to enable pathologists to identify patients with EOC who may benefit from immunotherapy.
“As the first immuno-oncology approval for this disease, this milestone underscores our commitment to advancing precision medicine and expanding access to innovative cancer treatments worldwide.”
PD-L1 expression with this test was evaluated in the KEYNOTE-B96 clinical trial supporting its use to identify patients who may benefit from KEYTRUDA.
In the US, ovarian cancer caused approximately 12,730 deaths in 2025 and the five-year survival rate was 51.6 per cent between 2015 and 2021.
In addition to these cancer types, the test is indicated in the US to help identify patients with non-small cell lung cancer, oesophageal squamous cell carcinoma, cervical cancer, head and neck squamous cell carcinoma, triple-negative breast cancer and gastric or gastro-oesophageal junction adenocarcinoma who may benefit from treatment with KEYTRUDA.
The test was developed by Agilent with Merck as a companion diagnostic for KEYTRUDA.
Breakthroughs in cancer care don’t only come from large institutions or fully funded labs.
They also come from determined individuals, small teams, early-stage founders, clinicians with an idea, researchers testing a new approach, technologists building smarter tools and advocates redesigning how care is delivered.
If you’re building something that could change how we prevent, detect, treat, manage or live with cancer, the Women’s Cancer Innovation award sponsored by Endomag is for you.
This award is designed to spotlight organisations, technologies and individuals who are moving cancer innovation forward at any meaningful stage.
Innovation doesn’t have to fit one mold
When people hear “cancer innovation,” they often picture a new drug or medical device.
But meaningful progress happens across many areas, including digital health tools, diagnostics and early detection approaches, AI and data platforms, care delivery models, patient support solutions and more.
If your work addresses a real cancer challenge in a new or more effective way, it counts.
And you don’t need to be “finished.” Many companies delay applying for awards until everything feels polished and complete.
But the Femtech World Awards are as much about recognising momentum and potential as they are celebrating outcomes.
Judges and reviewers understand innovation journeys. They are often more interested in clarity of problem, strength of insight, and thoughtful design than in perfect execution.
Progress matters. Direction matters. Impact potential matters.
And finally, if you’re wondering “Is this good enough?” – apply.
Many strong applicants almost don’t apply. The most common hesitation isn’t lack of innovation – it’s self-doubt.
If you’re asking yourself whether your project is too early, your team too small, your work innovative enough, or whether it counts if you’re not a startup, those questions are normal.
They’re also often the very reason you should submit.
These awards exist because great work is sometimes overlooked, underfunded, or under-recognised.
The goal is to surface promising solutions and support the people building them.
Find out more about the Femtech World Awards and enter for free here.
An AI tool cut interval breast cancers by 12 per cent in a Swedish screening trial of more than 105,000 women.
The study also found 27 per cent fewer aggressive breast cancers detected at screening when AI was used.
Interval cancers are cancers found between routine screening appointments because they were missed at the original scan. They are often more dangerous and linked to higher death rates than cancers found at screening.
The MASAI trial is described as the first large randomised study to test whether AI can improve mammography screening, which uses low-dose X-rays to examine breast tissue for signs of cancer.
The AI tool, called Transpara Detection and developed by ScreenPoint Medical, supported radiologists in analysing mammography images.
Earlier results from the same trial showed that Transpara Detection increased cancers found by 29 per cent and reduced radiologist workload by 44 per cent compared with standard double-reading, where two radiologists independently review each scan.
The latest findings indicate higher accuracy with AI support. Sensitivity, the ability to detect cancer, was 6.7 percentage points higher in the AI group while specificity, the ability to rule out healthy cases, was maintained. Results were similar across age groups and breast density levels.
Women screened with AI had 16 per cent fewer invasive interval cancers and 21 per cent fewer large interval cancers than those in the standard screening group.
The system also helps doctors assess risk more precisely by subdividing suspicious findings into BI-RADS 4 categories A, B and C. BI-RADS (Breast Imaging Reporting and Data System) is a standardised scale that guides whether a patient needs closer monitoring, further tests or treatment.
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