TY - JOUR
T1 - ConvPath
T2 - A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network
AU - Wang, Shidan
AU - Wang, Tao
AU - Yang, Lin
AU - Yang, Donghan M.
AU - Fujimoto, Junya
AU - Yi, Faliu
AU - Luo, Xin
AU - Yang, Yikun
AU - Yao, Bo
AU - Lin, Shin Yi
AU - Moran, Cesar
AU - Kalhor, Neda
AU - Weissferdt, Annikka
AU - Minna, John
AU - Xie, Yang
AU - Wistuba, Ignacio I.
AU - Mao, Yousheng
AU - Xiao, Guanghua
N1 - Funding Information:
This work was supported by the National Institutes of Health [1R01GM115473, 5R01CA152301, 5P30CA142543, 5P50CA070907, 5P30CA016672 and 1R01CA172211]; and the Cancer Prevention and Research Institute of Texas [RP120732]. The funders had no role in study design, data collection, data analysis, interpretation, writing of the manuscript.
Funding Information:
Pathology images and clinical data in the NLST and TCGA datasets that support the findings of this study are available online in the NLST ( https://biometry.nci.nih.gov/cdas/nlst/ ) and The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD, https://wiki.cancerimagingarchive.net/display/Public/TCGA-LUAD ). Data in the SPORE and CHCAMS datasets that support the findings of this study are available from the UT Lung SPORE Tissue bank and the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CHCAMS), China, separately, but restrictions apply to the availability of these data. 2
Publisher Copyright:
© 2019 The Authors
PY - 2019/12
Y1 - 2019/12
N2 - Background: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key “hallmarks of cancer”. However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone. Methods: In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/. Findings: The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a “spatial map” of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage. Interpretation: The analysis pipeline developed in this study could convert the pathology image into a “spatial map” of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.
AB - Background: The spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key “hallmarks of cancer”. However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone. Methods: In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/. Findings: The overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a “spatial map” of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage. Interpretation: The analysis pipeline developed in this study could convert the pathology image into a “spatial map” of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.
KW - Cell distribution and interaction
KW - Convolutional neural network
KW - Deep learning
KW - Lung adenocarcinoma
KW - Pathology image
KW - Prognosis
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U2 - 10.1016/j.ebiom.2019.10.033
DO - 10.1016/j.ebiom.2019.10.033
M3 - Article
C2 - 31767541
AN - SCOPUS:85076241278
SN - 2352-3964
VL - 50
SP - 103
EP - 110
JO - EBioMedicine
JF - EBioMedicine
ER -