한빛사 논문
Jouha Min1,*, Lip Ket Chin1,2,*, Juhyun Oh1, Christian Landeros1,3, Claudio Vinegoni1, Jeeyeon Lee4, Soo Jung Lee5, Jee Young Park6, Ai-Qun Liu2, Cesar M. Castro1,7, Hakho Lee1,8,†, Hyungsoon Im1,8,† and Ralph Weissleder1,8,9,†
1Center for Systems Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
2School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
3Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
4Department of Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea.
5Department of Oncology/Hematology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea.
6Department of Pathology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea.
7Cancer Center, Massachusetts General Hospital, Boston, MA 02114, USA.
8Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.
9Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
†Corresponding author.
*These authors contributed equally to the manuscript.
Abstract
Rapid, automated, point-of-care cellular diagnosis of cancer remains difficult in remote settings due to lack of specialists and medical infrastructure. To address the need for same-day diagnosis, we developed an automated image cytometry system (CytoPAN) that allows rapid breast cancer diagnosis of scant cellular specimens obtained by fine needle aspiration (FNA) of palpable mass lesions. The system is devoid of moving parts for stable operations, harnesses optimized antibody kits for multiplexed analysis, and offers a user-friendly interface with automated analysis for rapid diagnoses. Through extensive optimization and validation using cell lines and mouse models, we established breast cancer diagnosis and receptor subtyping in 1 hour using as few as 50 harvested cells. In a prospective patient cohort study (n = 68), we showed that the diagnostic accuracy was 100% for cancer detection and the receptor subtyping accuracy was 96% for human epidermal growth factor receptor 2 and 93% for hormonal receptors (ER/PR), two key biomarkers associated with breast cancer. A combination of FNA and CytoPAN offers faster, less invasive cancer diagnoses than the current standard (core biopsy and histopathology). This approach should enable the ability to more rapidly diagnose breast cancer in global and remote settings.
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