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.
Finding a lump in the breast does not always mean cancer, but waiting for test results can be an anxious and stressful experience. Current diagnostic methods for breast cancer rely on examining tissue samples under a microscope, which typically takes 1–2 weeks for a final report. In this study, we developed the MiniMaP platform, a small mass spectrometer designed for rapid breast cancer diagnosis, capable of quickly analyzing the molecular composition of tissue samples. This method requires no complex sample preparation and can provide results within 5 min. We trained a computational model to distinguish between cancerous and non-cancerous samples and further evaluated its robustness in a hospital setting. Our results suggest that MiniMaP could help doctors diagnose breast cancer faster, allowing patients to receive timely treatment and reducing the anxiety of waiting for results.
© 2025. The Author(s).