News
Study shows potential of AI model in improving ovarian stimulation decisions in IVF treatment
A groundbreaking study has demonstrated the potential of AI in optimising the ovarian stimulation process for IVF treatment.
The study, led by Dr Chelsea Canon, a reproductive endocrinologist and infertility specialist for RMA of New York, was a multi-center, prospective analysis of AI’s impact on IVF outcomes.
The Alife software, known as Stim Assist, provides physicians with personalised recommendations based on patient-specific characteristics and follicle growth.
This study is the first to prospectively investigate the clinical outcomes of IVF patients when clinicians utilised AI software to assist in determining the optimal starting dose of follicle-stimulating hormone (FSH) and the timing of trigger injection.
“The results of our study are incredibly promising,” said Dr Eric Flisser, one of the lead authors of the study.
“We found a trend towards improved egg yield and a reduction in FSH usage when physicians used Alife’s Stim Assist. This suggests that AI has the potential to refine the starting dose of FSH and narrow down the timing of the trigger injection during ovarian stimulation, ultimately benefiting patients by optimising the number of mature oocytes retrieved and reducing medication costs.”
Dr Alan B Copperman, CEO of RMA of New York and a collaborator of the study, said: “The use of AI in IVF represents a significant advancement in reproductive medicine.
“By leveraging AI to optimise ovarian stimulation decisions, we can potentially improve IVF outcomes and streamline the treatment process for patients.”
The study, which involved 291 patients undergoing IVF treatment at two clinics in the US, underscores the potential of AI to revolutionise the field of assisted reproductive technology.
However, the researchers emphasised the need for further studies to validate these findings and explore the broader application of AI in IVF treatment.
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Diagnosis
Lung cancer drug shows breast cancer potential
Ovarian cancer cells quickly activate survival responses after PARP inhibitor treatment, and a lung cancer drug could help block this, research suggests.
PARP inhibitors are a common treatment for ovarian cancer, particularly in tumours with faulty DNA repair. They stop cancer cells fixing DNA damage, which leads to cell death, but many tumours later stop responding.
Researchers identified a way cancer cells may survive PARP inhibitor treatment from the outset, pointing to a potential way to block that response. A Mayo Clinic team found ovarian cancer cells rapidly switch on a pro-survival programme after exposure to PARP inhibitors. A key driver is FRA1, a transcription factor (a protein that turns genes on and off) that helps cancer cells adapt and avoid death.
The team then tested whether brigatinib, a drug approved for certain lung cancers, could block this response and boost the effect of PARP inhibitors. Brigatinib was chosen because it inhibits multiple signalling pathways involved in cancer cell survival.
In laboratory studies, combining brigatinib with a PARP inhibitor was more effective than either treatment alone. Notably, the effect was seen in cancer cells but not normal cells, suggesting a more targeted approach.
Brigatinib also appeared to act in an unexpected way. Rather than working through the usual DNA repair routes, it shut down two signalling molecules, FAK and EPHA2, that aggressive ovarian cancer cells rely on. FAK and EPHA2 are proteins that relay survival signals inside cells. Blocking both at once weakened the cells’ ability to adapt and resist treatment, making them more vulnerable to PARP inhibitors.
Tumours with higher levels of FAK and EPHA2 responded better to the drug combination. Other data link high levels of these molecules to more aggressive disease, pointing to potential benefit in harder-to-treat cases.
Arun Kanakkanthara, an oncology investigator at Mayo Clinic and a senior author of the study, said: “This work shows that drug resistance does not always emerge slowly over time; cancer cells can activate survival programmes very early after treatment begins.”
John Weroha, a medical oncologist at Mayo Clinic and a senior author of the study, said: “From a clinical perspective, resistance remains one of the biggest challenges in treating ovarian cancer. By combining mechanistic insights from Dr Kanakkanthara’s laboratory with my clinical experience, this preclinical work supports the strategy of targeting resistance early, before it has a chance to take hold. This strategy could improve patient outcomes.”
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