ArticleCommunications medicine2025
Development and multicenter validation of on-site breast cancer diagnosis using paper spray ionization miniature mass spectrometry.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundConventional histopathological examination for breast core needle biopsy diagnosis is time-consuming and labor-intensive, leading to delayed medical treatments and increased psychological burden for patients. A rapid and reliable diagnostic method is needed to assist routine pathological diagnosis.
methodsWe developed a miniature mass spectrometry platform coupled with paper spray ionization (MiniMaP) for rapid breast cancer diagnosis. This platform enables direct molecular analysis of biopsy samples without sample preparation. A machine learning model was trained to differentiate benign and malignant samples based on molecular profiles. The platform's performance was further evaluated in a 22-month multicenter validation study.
resultsHere we show that the machine learning model trained on molecular profiles achieves 88% accuracy in distinguishing breast cancer from benign samples. The model identifies 60 molecular features as potential biomarkers. Additionally, MiniMaP is implemented for on-site analysis in a hospital setting, enabling breast cancer diagnosis within 5 min. The platform maintains consistent accuracy (84%) across 540 biopsy samples over the 22-month validation period.
conclusionsOur results demonstrate that the MiniMaP platform enables rapid breast cancer diagnosis and maintains consistent performance in long-term multicenter validation. It holds promise for assisting clinical breast cancer diagnosis by providing instant diagnostic reports to support timely medical decisions and improve medical care.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.