AUTHOR=Chen Pu , Chen Xu Run , Chen Nan , Zhang Lan , Zhang Li , Zhu Jianfeng , Pan Baishen , Wang Beili , Guo Wei 

TITLE=Detection of Metastatic Tumor Cells in the Bone Marrow Aspirate Smears by Artificial Intelligence (AI)-Based Morphogo System

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.742395

DOI=10.3389/fonc.2021.742395

ISSN=2234-943X

ABSTRACT=Introduction: Metastatic carcinomas of bone marrow (MCBM) are characterized as tumors of non-hematopoietic origin spreading to the bone marrow through blood or lymphatic circulation. The diagnosis is critical for tumor staging, treatment selection and prognostic risk stratification. However, the identification of metastatic carcinoma cells on bone marrow aspiration smears is technically challenging by conventional microscopic screening. 
Objective: The aim of this research is to develop an automatic recognition system using deep learning algorithms applied to bone marrow cells image analysis. The system takes advantage of the artificial intelligence (AI)-based method in recognizing metastatic atypical cancer clusters and promoting rapid diagnosis.
Methods: We retrospectively reviewed metastatic non-hematopoietic malignancies in bone marrow aspirate smears collected from 60 cases of patients admitted to Zhongshan Hospital. High resolution digital microscope images were scanned and automatically analysed by Morphogo AI based system. Morphogo was trained and validated based on images of 20748 tumor cell clusters from the randomly selected 50 MCBM patients. Furthermore, 5469 pre-classified cancer cell cluster images from the remaining 10 MCBM patients were tested for the recognition performance between Morphogo and experienced pathologists. 
Results: Morphogo exhibited a sensitivity of 56.61%, a specificity of 91.48%, and an accuracy of 82.41% in the recognition of metastatic cancer cells. Morphogo’s classification result was in agreement with the conventional standard in the diagnosis of metastatic cancer clusters, with a Kappa value of 0.513. The test results between Morphogo and pathologists H1, H2 and H3 agreement demonstrated a reliability coefficient of 0.827. The area under the curve (AUC) for Morphogo to diagnose the cancer cell clusters was 0.865.
Conclusion: In patients with clinical history of cancer, the Morphogo system was validated as a useful screening tool in the identification of metastatic cancer cells in the bone marrow aspirate smears. It has potential clinical application in the diagnostic assessment of metastatic cancers for staging.