Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.
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.



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.



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.



The results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer.
Department of Bioinformatics, University of Texas Southwestern Medical Center at Dallas, Texas.
Xin
X
Luo
Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center at Dallas, Texas.
Xiao
X
Zang
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.
Lin
L
Yang
Department of Computer Sciences and Engineering, University of Texas at Arlington, Arlington, Texas.
Junzhou
J
Huang
Department of Biostatistics, University of Florida, Gainesville, Florida.
Faming
F
Liang
Department of Translational Molecular Pathology, University of Texas M. D. Anderson Cancer Center, Houston, Texas.
Jaime
J
Rodriguez-Canales
Department of Translational Molecular Pathology, University of Texas M. D. Anderson Cancer Center, Houston, Texas.
Ignacio I
II
Wistuba
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.
Adi
A
Gazdar
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.
Yang
Y
Xie
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.
Guanghua
G
Xiao
D000230:Adenocarcinoma / Q000473:pathology* / Q000628:therapy; D000368:Aged; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology* / Q000628:therapy; D002294:Carcinoma, Squamous Cell / Q000473:pathology* / Q000628:therapy; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000473:pathology* / Q000628:therapy; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009367:Neoplasm Staging; D011379:Prognosis; D015996:Survival Rate
Lung adenocarcinoma; Lung squamous cell carcinoma; Morphological features; Pathological image; Prognosis; Statistical modeling