Completed Public Health & Healthcare NIHR-supported project Digestion, Kidneys & Other Organs

Uterine Sarcomas: Identifying barriers to early diagnosis

In plain English

AI plain-English summary

Uterine sarcoma patients are being diagnosed too late, often after their cancer has already been misdiagnosed as a benign fibroid or a different uterine cancer. This matters because uterine sarcomas are rare, aggressive tumours with poor survival rates, and delays in diagnosis directly reduce a patient’s chances of effective treatment. The problem is that doctors and referral systems currently lack clear, evidence-based steps to distinguish sarcomas from more common conditions early on. This study will analyse real-world NHS data from patients already diagnosed with uterine sarcoma, mapping exactly where in the diagnostic pathway delays and misdiagnoses occur. If the research succeeds, it will pinpoint specific, actionable steps—such as which symptoms should trigger faster imaging referrals or which test results warrant immediate specialist review—that could be targeted by awareness campaigns and clinical guidelines. The practical impact would be a shorter, more accurate diagnostic process for future patients, reducing the number of women who are sent home with a fibroid diagnosis while a sarcoma grows untreated. This is applied health services research with a direct, near-term goal: improving survival by fixing a broken diagnostic pathway.

View original technical description
This study will analyse real-world data from uterine sarcoma patients diagnosed through the English healthcare system to explore barriers to diagnosis and identify steps in the referral and diagnostic pathways that could be targeted to improve the time-to-diagnosis and reduce misdiagnosis as fibroids or endometrial cancer. The results will be used to identify steps in the pathway that could be targeted for future studies and awareness interventions to improve diagnosis, reduce misdiagnosis and improve survival.

Researchers

Esther Moss (Principal Investigator)

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Original classification

Cancer

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.