Development and multicenter validation of on-site breast cancer diagnosis using paper spray ionization miniature mass spectrometry.

Background: Conventional 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.

Methods: We 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.

Results: Here 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.

Conclusions: Our 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.