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Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology.
Chen, Xun; Wu, Zhouqiao; He, Yexuan; Hao, Zhe; Wang, Qi; Zhou, Keji; Zhou, Wanhui; Wang, Pu; Shan, Fei; Li, Zhongwu; Ji, Jiafu; Fan, Yubo; Li, Ziyu; Yue, Shuhua.
Affiliation
  • Chen X; Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
  • Wu Z; School of Engineering Medicine, Beihang University, 100191, Beijing, China.
  • He Y; Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China.
  • Hao Z; Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
  • Wang Q; Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
  • Zhou K; Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China.
  • Zhou W; Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
  • Wang P; Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
  • Shan F; Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
  • Li Z; Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China.
  • Ji J; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital and Institute, 100142, Beijing, China.
  • Fan Y; Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 100142, Beijing, China.
  • Li Z; Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191, Beijing, China.
  • Yue S; School of Engineering Medicine, Beihang University, 100191, Beijing, China.
Adv Sci (Weinh) ; 10(21): e2300961, 2023 07.
Article in En | MEDLINE | ID: mdl-37114845
ABSTRACT
Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Peritoneal Neoplasms / Stomach Neoplasms Type of study: Diagnostic_studies / Guideline / Prognostic_studies Limits: Humans Language: En Journal: Adv Sci (Weinh) Year: 2023 Type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Peritoneal Neoplasms / Stomach Neoplasms Type of study: Diagnostic_studies / Guideline / Prognostic_studies Limits: Humans Language: En Journal: Adv Sci (Weinh) Year: 2023 Type: Article Affiliation country: China