Cancer
AI enhances cervical cancer detection
Researchers are using AI to enhance detection accuracy and efficiency, paving the way for a transformation in cervical cancer screening.
With the aid of deep learning algorithms, a new study is seeking to address the critical need for more effective screening tests, especially in low- and middle-income countries where traditional methods often fall short.
This innovative approach promises to alleviate the global burden of cervical cancer by improving early detection and treatment, offering a beacon of hope for millions of women worldwide.
Cervical cancer remains a major health threat for women globally, with the highest incidence in developing nations. Despite the availability of preventive measures, challenges such as limited healthcare resources and inadequate screening programs continue to undermine global efforts to eliminate the disease.
The World Health Organization (WHO) has set an ambitious target to screen 70% of women aged 35 to 45 by 2030, a goal deemed essential to reduce mortality rates. However, achieving this requires innovative solutions that are both effective and scalable, particularly in regions where access to healthcare is restricted.
The new review delves into AI’s transformative potential in cervical cancer screening, focusing on its role in medical image recognition to identify abnormal cytology and neoplastic lesions.
By harnessing deep learning algorithms, AI is now able to replicate human-like interpretation of medical images, resulting in more accurate detection of cervical cancer. The study highlights how AI can automate the segmentation and classification of cytology images, which is vital for early diagnosis.
Additionally, it explores AI’s potential to enhance colposcopy, a procedure traditionally hampered by subjective interpretation and reliance on highly skilled professionals. By integrating AI into this process, the review envisions more objective and efficient screenings.
AI’s role in risk prediction models is also discussed, where clinical data is used to predict the progression of high-risk HPV infections and cervical cancer development. These models, powered by machine learning, offer a personalized approach to screening, reducing unnecessary referrals and allowing for better risk stratification.
Dr. Youlin Qiao, lead author of the study, said: “AI has the ability to revolutionise cervical cancer screening by offering automated, objective, and unbiased detection of both cancerous and precancerous conditions.
“This technology is particularly vital for bridging the healthcare gap in underserved regions.”
The implications of AI-powered cervical cancer screening are profound. Beyond improving detection rates and efficiency, this technology could also expand access to screening services in remote or resource-limited areas.
If adopted globally, AI-assisted screening could significantly reduce misdiagnoses, improve healthcare delivery, and move the world closer to the goal of eliminating cervical cancer by the century’s end.
Despite its promise, several hurdles must be addressed for AI to achieve widespread clinical integration including data standardisation, such as stablishing global platforms for standardised and annotated datasets to ensure diverse and high-quality training data, as well as ethical integration, addressing transparency, privacy, and accountability concerns to build trust among clinicians and patients.
Further challenges include, model interpretability, enhancing AI’s explainability to foster confidence and seamless adoption in clinical workflows, and validation across contexts, conducting robust external validation studies and equipping clinicians with the necessary training to use AI tools effectively.
By tackling these challenges, AI-driven cervical cancer screening could redefine global healthcare, offering a powerful tool in the fight against one of the most preventable cancers.
AI may help speed breast cancer diagnosis for high-risk women after abnormal mammograms, a study suggests.
Women with abnormal mammograms often wait weeks to learn whether they have breast cancer.
Researchers at UC San Francisco and UC Berkeley said an AI-guided workflow could help reduce that wait by quickly identifying those most likely to have the disease. Some women could move from imaging to evaluation, and sometimes biopsy, in a single day.
Dr Maggie Chung, first author of the study, said: “This is a really an exciting time.
“This moves us closer to personalised care, where we can tailor a plan so that each patient gets the right intervention at the right time.”
The study used an open-source AI model called Mirai.
The model was trained on hundreds of thousands of mammograms linked to patients’ cancer outcomes.
A mammogram is an X-ray scan of the breast used to look for signs of cancer. A biopsy involves taking a small tissue sample to test for disease.
The AI tool is designed to detect subtle patterns in screening mammograms and predict a woman’s cancer risk.
Researchers at UC San Francisco and UC Berkeley applied the model to more than 4,100 screening mammograms at Zuckerberg San Francisco General Hospital and Trauma Center.
Mirai identified 525 women, about 12.7 per cent of screened patients, as high risk.
Those patients could receive an interpretation of their mammograms immediately after the scan and have additional diagnostic imaging for suspicious areas on the same day.
Some women who needed biopsies were also able to have them on the same day.
The researchers said Mirai reduced the wait time for diagnostic evaluation from several weeks to about an hour.
For women who were ultimately diagnosed with breast cancer, it reduced the average wait for biopsy from more than two months to fewer than 10 days.
The researchers stressed that Mirai does not replace radiologists or make diagnoses on its own.
Instead, it acts as a triage tool to help physicians identify the patients who can benefit most from accelerated care.
The team analysed more than 114,000 archival mammograms before launching the programme, to ensure the model would capture enough high-risk patients without overloading the clinic with too many expedited evaluations.
The researchers said they hope AI will support a more personalised approach to breast cancer screening tailored to each patient’s breast cancer risk.
Chung said: “Right now, many women follow the same screening schedule but their individual risk can be very different.
“AI risk assessment gives us the chance to identify the women most likely to benefit from expedited care and get them what they need.”
Adam Yala, senior author of the study and a data scientist at UC Berkeley, said: “This is a powerful example of how AI can be a collaborative partner for physicians.
“It shows how we can improve care when we bring clinicians and data scientists together to design these systems.”
A genomic test may help some women with breast cancer avoid chemotherapy, with near-identical outcomes in an international trial.
The findings suggest patients with a low test score could be treated with hormone therapy alone without increasing the risk of their cancer returning.
Researchers said the results could support more personalised treatment decisions and spare some women the side-effects of chemotherapy.
Prof Rob Stein, the trial’s chief investigator and a professor of breast oncology at UCL, said: “Optima addresses a longstanding challenge in breast cancer care: identifying who truly benefits from chemotherapy and who does not.
“Our findings show that many patients can safely avoid chemotherapy without compromising their outcomes.
“These results mark an important and significant step toward more personalised treatment.
“The trial has successfully used tumour biology to guide decisions rather than relying solely on traditional clinical features.”
Breast cancer treatment usually involves surgery to remove tumours. Chemotherapy is then often recommended if doctors believe there is a risk the disease will return.
Chemotherapy can cause side-effects including hair loss, rashes, nausea, insomnia and fatigue. Some women may also face longer-term consequences such as infertility, cognitive impairment or early menopause.
The Optima trial followed more than 4,000 patients with newly diagnosed breast cancer in the UK, Norway, Sweden, Australia, New Zealand and Thailand.
The trial was led by University College London.
One woman who took part in the trial told the Guardian that being able to skip chemotherapy felt “like Christmas”. Nine years after being diagnosed, taking the test and skipping chemotherapy, she is healthy and enjoying a full and active life.
The trial tested whether a genomic test could identify which patients need chemotherapy and which could safely avoid it.
The Prosigna test, made by diagnostics company Veracyte, analyses the activity of 50 genes in tumour tissue. It identifies the molecular subtype of the cancer and gives a score estimating the risk of breast cancer returning in the next 10 years.
The randomised trial involved 4,429 patients aged 40 or over with hormone-positive breast cancer. Hormone-positive breast cancer grows in response to hormones such as oestrogen or progesterone. It is the most common form of breast cancer, accounting for up to 80 per cent of cases globally.
Participants were assigned to one of two groups. In the standard treatment group, patients received chemotherapy followed by hormone therapy.
In the second group, patients had their tumours analysed using the genomic test. Those with a high score received chemotherapy and hormone therapy. Those with a low score received hormone therapy alone.
Radiotherapy and other treatments were given as usual in both groups.
In the second group, outcomes were very similar whether chemotherapy was given or not. Five years after treatment, 95 per cent of patients who had chemotherapy and hormone therapy were alive and free from breast cancer recurrence, while 94 per cent of those who skipped chemotherapy were also alive and recurrence-free.
The findings suggest chemotherapy offered little or no additional benefit for patients with low test scores.
Some men also took part in the study, but researchers said there were too few to draw firm conclusions for this group.
The trial received funding from the National Institute for Health and Care Research, Veracyte and cancer charities.
Prof Iain MacPherson, a co-chief investigator and professor of breast oncology at the University of Glasgow, said: “Optima provides robust, practice-changing evidence that we can safely reduce the use of chemotherapy for many patients with hormone-sensitive breast cancer.
“These findings represent a major step forward in delivering more personalised, precise care, ensuring that treatment decisions are driven by what will genuinely improve outcomes for patients, while avoiding unnecessary toxicity.
“The potential impact for both patients and health services is substantial.”
The FDA has delayed approval of camizestrant while it reviews new analyses submitted by AstraZeneca after advisers voted against the breast cancer drug.
The US regulator had been considering whether to approve the oral treatment after a phase 3 switching study in a specific group of breast cancer patients.
Camizestrant is an oral SERD, or selective oestrogen receptor degrader. These drugs are designed to block and break down oestrogen receptors that can help some breast cancers grow.
AstraZeneca filed for approval based on the phase 3 Serena-6 trial, which tested a treatment-switching approach.
Patients in the study received an aromatase inhibitor and a CDK4/6 inhibitor. Aromatase inhibitors lower oestrogen levels, while CDK4/6 inhibitors are targeted cancer drugs that help slow cancer cell growth.
After detecting an ESR1 mutation, investigators switched the aromatase inhibitor to camizestrant.
An ESR1 mutation is a change in a gene linked to the oestrogen receptor. It can make some breast cancers less responsive to standard hormone treatments.
AstraZeneca said switching to camizestrant was linked to a 56 per cent increase in progression-free survival.
Progression-free survival measures how long a patient lives without their disease getting worse.
However, the FDA raised questions about the study design.
An FDA advisory committee later voted six to three that AstraZeneca had failed to show camizestrant provides a clinically meaningful benefit.
The vote was a setback for the company’s hopes of approval, although the FDA can go against advisory committee recommendations.
After the setback, AstraZeneca submitted additional analyses requested by the FDA.
The company said the analyses include data on circulating tumour DNA clearance linked to longer-term efficacy outcomes.
Circulating tumour DNA refers to fragments of genetic material from cancer cells that can be found in the blood.
AstraZeneca is expected to share the data next week at the American Society of Clinical Oncology annual meeting.
The FDA has now delayed its ruling while it reviews the additional information. AstraZeneca did not provide a new decision date.
Three-month delays are typical and, during the second Trump administration, have been common.
After budget cuts reduced its workforce, the FDA delayed rulings on assets including Bayer’s Lynkuet, Biohaven’s troriluzole and Sanofi’s tolebrutinib. The FDA reportedly blamed a “heavy workload and limited resources” for one delay.
The agency has continued to delay rulings this year, with Biogen, Savara and Travere Therapeutics among the companies to say the FDA has extended reviews of their drugs.
Like AstraZeneca, those three companies faced delays after submitting additional information that the agency needed time to review.
If the additional analyses address the regulator’s concerns, AstraZeneca could still secure approval for a drug it has estimated could generate peak sales of more than US$5bn.
Guggenheim Securities analysts recently described the Serena-6 study as “a limited commercial opportunity in our and [AstraZeneca’s] view”.
AstraZeneca is also running two adjuvant studies and a trial in a first-line setting as it seeks to position camizestrant across different stages of breast cancer care.
Adjuvant treatment is given after primary treatment, such as surgery, to reduce the risk of cancer returning. First-line treatment is the first therapy given for a disease.
Roche reported the failure of its rival oral SERD in first-line breast cancer in March, but AstraZeneca executives have argued that their trial designs and drug candidate are different.
Last week, Europe’s Committee for Medicinal Products for Human Use issued a positive opinion on camizestrant.
The drug is expected to be marketed as Etcamah in Europe.
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