Xin Luo1, Xiao Zang2, Lin Yang3, Junzhou Huang4, Faming Liang5, Jaime Rodriguez-Canales6, Ignacio I Wistuba6, Adi Gazdar7, Yang Xie8, Guanghua Xiao9. 1. Department of Bioinformatics, University of Texas Southwestern Medical Center at Dallas, Texas. 2. Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center at Dallas, Texas. 3. Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center at Dallas, Texas; Department of Pathology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China. 4. Department of Computer Sciences and Engineering, University of Texas at Arlington, Arlington, Texas. 5. Department of Biostatistics, University of Florida, Gainesville, Florida. 6. Department of Translational Molecular Pathology, University of Texas M. D. Anderson Cancer Center, Houston, Texas. 7. Department of Pathology, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas; Hamon Center for Therapeutic Oncology, University of Texas Southwestern Medical Center at Dallas, Dallas, Texas. 8. Department of Bioinformatics, University of Texas Southwestern Medical Center at Dallas, Texas; Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center at Dallas, Texas. 9. Department of Bioinformatics, University of Texas Southwestern Medical Center at Dallas, Texas; Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center at Dallas, Texas. Electronic address: guanghua.xiao@utsouthwestern.edu.
Abstract
INTRODUCTION: Pathological examination of histopathological slides is a routine clinical procedure for lung cancer diagnosis and prognosis. Although the classification of lung cancer has been updated to become more specific, only a small subset of the total morphological features are taken into consideration. The vast majority of the detailed morphological features of tumor tissues, particularly tumor cells' surrounding microenvironment, are not fully analyzed. The heterogeneity of tumor cells and close interactions between tumor cells and their microenvironments are closely related to tumor development and progression. The goal of this study is to develop morphological feature-based prediction models for the prognosis of patients with lung cancer. METHOD: We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for patients with NSCLC. Tissue pathological images were analyzed for 523 patients with adenocarcinoma (ADC) and 511 patients with squamous cell carcinoma (SCC) from The Cancer Genome Atlas lung cancer cohorts. The features extracted from the pathological images were used to develop statistical models that predict patients' survival outcomes in ADC and SCC, respectively. RESULTS: We extracted 943 morphological features from pathological images of hematoxylin and eosin-stained tissue and identified morphological features that are significantly associated with prognosis in ADC and SCC, respectively. Statistical models based on these extracted features stratified NSCLC patients into high-risk and low-risk groups. The models were developed from training sets and validated in independent testing sets: a predicted high-risk group versus a predicted low-risk group (for patients with ADC: hazard ratio = 2.34, 95% confidence interval: 1.12-4.91, p = 0.024; for patients with SCC: hazard ratio = 2.22, 95% confidence interval: 1.15-4.27, p = 0.017) after adjustment for age, sex, smoking status, and pathologic tumor stage. CONCLUSIONS: The results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer.
INTRODUCTION: Pathological examination of histopathological slides is a routine clinical procedure for lung cancer diagnosis and prognosis. Although the classification of lung cancer has been updated to become more specific, only a small subset of the total morphological features are taken into consideration. The vast majority of the detailed morphological features of tumor tissues, particularly tumor cells' surrounding microenvironment, are not fully analyzed. The heterogeneity of tumor cells and close interactions between tumor cells and their microenvironments are closely related to tumor development and progression. The goal of this study is to develop morphological feature-based prediction models for the prognosis of patients with lung cancer. METHOD: We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for patients with NSCLC. Tissue pathological images were analyzed for 523 patients with adenocarcinoma (ADC) and 511 patients with squamous cell carcinoma (SCC) from The Cancer Genome Atlas lung cancer cohorts. The features extracted from the pathological images were used to develop statistical models that predict patients' survival outcomes in ADC and SCC, respectively. RESULTS: We extracted 943 morphological features from pathological images of hematoxylin and eosin-stained tissue and identified morphological features that are significantly associated with prognosis in ADC and SCC, respectively. Statistical models based on these extracted features stratified NSCLCpatients into high-risk and low-risk groups. The models were developed from training sets and validated in independent testing sets: a predicted high-risk group versus a predicted low-risk group (for patients with ADC: hazard ratio = 2.34, 95% confidence interval: 1.12-4.91, p = 0.024; for patients with SCC: hazard ratio = 2.22, 95% confidence interval: 1.15-4.27, p = 0.017) after adjustment for age, sex, smoking status, and pathologic tumor stage. CONCLUSIONS: The results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer.
Authors: Andrew H Beck; Ankur R Sangoi; Samuel Leung; Robert J Marinelli; Torsten O Nielsen; Marc J van de Vijver; Robert B West; Matt van de Rijn; Daphne Koller Journal: Sci Transl Med Date: 2011-11-09 Impact factor: 17.956
Authors: Alain C Borczuk; Fang Qian; Angeliki Kazeros; Jennifer Eleazar; Adel Assaad; Joshua R Sonett; Mark Ginsburg; Lyall Gorenstein; Charles A Powell Journal: Am J Surg Pathol Date: 2009-03 Impact factor: 6.394
Authors: Yinyin Yuan; Henrik Failmezger; Oscar M Rueda; H Raza Ali; Stefan Gräf; Suet-Feung Chin; Roland F Schwarz; Christina Curtis; Mark J Dunning; Helen Bardwell; Nicola Johnson; Sarah Doyle; Gulisa Turashvili; Elena Provenzano; Sam Aparicio; Carlos Caldas; Florian Markowetz Journal: Sci Transl Med Date: 2012-10-24 Impact factor: 17.956
Authors: Lee A D Cooper; Jun Kong; David A Gutman; William D Dunn; Michael Nalisnik; Daniel J Brat Journal: Lab Invest Date: 2015-01-19 Impact factor: 5.662
Authors: Kun-Hsing Yu; Ce Zhang; Gerald J Berry; Russ B Altman; Christopher Ré; Daniel L Rubin; Michael Snyder Journal: Nat Commun Date: 2016-08-16 Impact factor: 14.919
Authors: Andrew G Nicholson; Kathleen Torkko; Patrizia Viola; Edwina Duhig; Kim Geisinger; Alain C Borczuk; Kenzo Hiroshima; Ming S Tsao; Arne Warth; Sylvie Lantuejoul; Prudence A Russell; Erik Thunnissen; Alberto Marchevsky; Mari Mino-Kenudson; Mary Beth Beasley; Johan Botling; Sanja Dacic; Yasushi Yatabe; Masayuki Noguchi; William D Travis; Keith Kerr; Fred R Hirsch; Lucian R Chirieac; Ignacio I Wistuba; Andre Moreira; Jin-Haeng Chung; Teh Ying Chou; Lukas Bubendorf; Gang Chen; Giuseppe Pelosi; Claudia Poleri; Frank C Detterbeck; Wilbur A Franklin Journal: J Thorac Oncol Date: 2017-11-07 Impact factor: 15.609
Authors: Frederick M Howard; James Dolezal; Sara Kochanny; Jefree Schulte; Heather Chen; Lara Heij; Dezheng Huo; Rita Nanda; Olufunmilayo I Olopade; Jakob N Kather; Nicole Cipriani; Robert L Grossman; Alexander T Pearson Journal: Nat Commun Date: 2021-07-20 Impact factor: 14.919