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Cancer

AI could help improve early detection of interval breast cancer

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A new study suggests that AI could help detect interval breast cancer — those that develop between routine screenings — before they become more advanced and harder to treat. This could potentially lead to better screening practices, earlier treatment and improved patient outcomes.

The study, led by investigators at the UCLA Health Jonsson Comprehensive Cancer Center and published in the Journal of the National Cancer Institute, found that AI was able to identify “mammographically-visible” types of interval cancers earlier by flagging them at the time of screening.

These include tumours that are visible on mammograms but not detected by radiologists, or have very subtle signs on mammography that are easy to miss because the signs were faint or arguably below the level of detection by the human eye.

Researchers estimate that incorporating AI into screening could help reduce the number of interval breast cancers by 30 per cent.

“This finding is important because these interval cancer types could be caught earlier when the cancer is easier to treat,” said Dr. Tiffany Yu, assistant professor of Radiology at the David Geffen School of Medicine at UCLA and first author of the study.

“For patients, catching cancer early can make all the difference. It can lead to less aggressive treatment and improve the chances of a better outcome.”

 

 

While similar research has been conducted in Europe, this study is among the first to explore the use of AI to detect interval breast cancers in the United States. Researchers point out that there are key differences between the US and European screening practices.

In the US, most mammograms are performed using digital breast tomosynthesis (DBT), often called 3D mammography, and patients are typically screened every year. In contrast, European programmes usually use digital mammography (DM), often called 2D mammography, and screen patients every two to three years.

The retrospective study analysed data from nearly 185,000 past mammograms from 2010 to 2019 that included both DM and DBT. From the data, the team looked at 148 cases where a woman was diagnosed with interval breast cancer.

Radiologists then reviewed these cases to determine why the cancer wasn’t spotted earlier. The new study adapted a European classification system to categorize the interval cancers. They include: Missed reading error, minimal signs–actionable, minimal signs–non–actionable, true interval cancer, occult (which is truly invisible on mammogram), and missed due to a technical error.

Researchers then applied a commercially available AI software called Transpara to the initial screening mammograms performed before the cancer diagnosis to determine if it could detect subtle signs of cancer that were missed by radiologists during initial screenings, or at least flag them as suspicious. The tool scored each mammogram from 1 to 10 for cancer risk. A score of 8 or higher was considered flagged as potentially concerning.

The team found that the AI flagged 76 per cent of the mammograms that had been originally read as normal but were later linked to an interval breast cancer. It flagged 90 per cent of missed reading error cases where the cancer had been visible on the mammogram but missed or misinterpreted by the radiologist.

It caught 89 per cent of minimal-signs-actionable cancers that showed very subtle signs and could reasonably have been acted upon, as well as 72 per cent of those with minimal-signs-non-actionable that were likely too subtle to prompt action.

For cancers that were occult or completely invisible on the mammogram, the AI flagged 69 per cent of cases.

It was somewhat less effective at identifying true interval cancers, those that were not present at the time of screening but developed later, flagging 50 per cent.

“While we had some exciting results, we also uncovered a lot of AI inaccuracy and issues that need to be further explored in real-world settings,” said Dr. Hannah Milch, assistant professor of Radiology at the David Geffen School of Medicine and senior author of the study.

“For example, despite being invisible on mammography, the AI tool still flagged 69 per cent of the screening mammograms that had occult cancers. However, when we looked at the specific areas on the images that the AI marked as suspicious, the AI did not do as good of a job and only marked the actual cancer 22 per cent of the time.”

Larger prospective studies are needed to understand how radiologists would use AI in practice and address key questions, such as how to handle cases where AI flags areas as suspicious that aren’t visible to the human eye, especially when the AI isn’t always accurate in pinpointing the exact location of cancer.

“While AI isn’t perfect and shouldn’t be used on its own, these findings support the idea that AI could help shift interval breast cancers toward mostly true interval cancers,” Yu added.

“It shows potential to serve as a valuable second set of eyes, especially for the types of cancers that are the hardest to catch early. This is about giving radiologists better tools and giving patients the best chance at catching cancer early, which could lead to more lives saved.”

Diagnosis

Women unaware of gynaecological cancers

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Only one per cent of women can name all five gynaecological cancers, new research suggests, as 21 women in the UK die every day of the diseases.

The report also found that 31 per cent of women have put off or avoided seeking medical advice for gynaecological symptoms.

It also found that 43 per cent of women invited for cervical screening said barriers had put them off attending, while 18 per cent of respondents aged 25 to 34 who had been invited had never attended.

The five main gynaecological cancers are womb, also called uterine, ovarian, cervical, vulval and vaginal cancer.

The Lady Garden Foundation said that, while progress has been made since the UK government’s 2022 Women’s Health Strategy aimed to improve gynaecological cancer care, significant challenges remain.

John Butler, medical director and trustee at the Lady Garden Foundation, said: “The fact that only one per cent of the population can name the diseases that directly affect half of us underscores a significant awareness gap, impacting individuals’ ability to recognise vital signs and symptoms or seek timely medical help.

“Addressing this isn’t just about awareness; it’s a critical public health priority. Our collective efforts are essential to ensure the latest commitments announced by this government translate into tangible change that saves lives.”

The report said key reasons for delaying medical advice included difficulty making appointments, embarrassment and, for cervical screening, fear of pain or previous bad experiences.

Women also reported challenges within healthcare interactions, including feeling “not taken seriously”, “dismissed” or “not believed” when seeking gynaecological advice.

Jenny Halpern Prince, chief executive and charity co-founder, said: “We frequently hear reports of women feeling ‘not taken seriously,’ ‘dismissed,’ or ‘not believed’ when seeking gynaecological advice.

“These experiences highlight crucial areas where we can improve patient support and trust within our healthcare system, ensuring women receive the empathetic and effective care they need.”

The Lady Garden Foundation said it aims to increase awareness of both the charity and the five gynaecological cancers.

It also aims to serve as a primary entry point for reliable, stigma-free information, helping people understand their bodies, recognise symptoms and overcome barriers to accessing care.

Its Silent No More Garden was unveiled at the RHS Chelsea Flower Show 2026. Designed by Darren Hawkes, the garden serves as a national call to action, using five sculptures to spark conversations, break long-standing taboos and encourage open dialogue about symptoms and preventative care.

Butler said: “Continued focus and collaborative action are essential to progress.

“The ongoing commitment from the government, alongside societal efforts to break down taboos surrounding gynaecological health, are crucial.

“The Lady Garden Foundation is dedicated to being a beacon of information and support, empowering women with the knowledge they need. We urge everyone to learn the signs, speak up, and help us save lives.”

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Fertility

AI could transform ovarian care through personalisation, study finds

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AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.

A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.

Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.

They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.

The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.

Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.

The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.

In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.

Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.

The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.

However, researchers said challenges remain in translating promising research findings into routine clinical practice.

They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.

Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.

The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.

They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.

“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”

In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.

PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.

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Cancer

Three cancer innovators shortlisted for Femtech World Award

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Femtech World is delighted to reveal the shortlist for this year’s Women’s Cancer Innovation award.

The award, sponsored by Endomag, will honour a groundbreaking innovation dedicated to the prevention, early detection treatment or ongoing care of cancers that uniquely or disproportionately affect women.

Endomag is a medical technology company devoted to improving the global standard of cancer care.

Its Sentimag system, Magseed marker and Magtrace lymphatic tracer are used by thousands of the world’s leading physicians and cancer centres.

After careful review of this year’s submissions, we are delighted to announce the three shortlisted entries for the Women’s Cancer Innovation Award 2026.

Auria is tackling one of the most stubborn problems in breast cancer screening: the 66 per cent of women who simply don’t participate.

Rather than improving existing imaging pathways, Auria is creating an entirely new access layer: a non-invasive, at-home test that detects protein biomarkers for breast cancer in tears.

Auria’s test, a CLIA-certified Lab Developed Test, has been validated across more than 2,000 patients in multiple clinical studies with collaborators including MD Anderson Cancer Center and Stanford University.

It reports a sensitivity of 93 per cent and a negative predictive value of 98 per cent.

Founded on six years of combined research at the University of Barcelona and UC Irvine, The Blue Box has developed a non-invasive, urine-based test that detects breast cancer by analysing volatile organic compound (VOC) signatures – no radiation, no compression, no imaging facility required.

The test achieves a sensitivity of 88.42 per cent, outperforming mammography by 15 per cent overall, and by 30 per cent specifically in women with dense breasts. 

The technology could function as a first-line screening tool in primary care settings, as a complement to mammography for high-density patients, or as an accessible alternative in healthcare systems where imaging infrastructure is limited.

Celbrea is a disposable and affordable thermal screening device that empowers women of all ages to stay on top of monitoring their breast health.

The device aims to add to doctors’ existing standard evaluation protocols with a quick, painless examination. Celbrea does not replace a mammogram but simply provides an additional way to screen for breast disease, including breast cancer.

The device consisting of two disposable pads with photochromic sensors. The pads are self-applied to each breast for 15 minutes.

1188 nano-sensors are embedded within a biocompatible multilayer pad, accurately measuring any temperature differences on the surface of the breast using liquid crystal thermographic technology.

What happens next

The shortlisted entries will now be judge by an Endomag representative who will reveal the winner at a virtual awards event on June 19.

Winners will receive a trophy and will be interviewed by a Femtech World journalist.

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