TY - JOUR
T1 - Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis
AU - Luo, Xin
AU - Zang, Xiao
AU - Yang, Lin
AU - Huang, Junzhou
AU - Liang, Faming
AU - Rodriguez-Canales, Jaime
AU - Wistuba, Ignacio I.
AU - Gazdar, Adi
AU - Xie, Yang
AU - Xiao, Guanghua
N1 - Publisher Copyright:
© 2016 International Association for the Study of Lung Cancer
PY - 2017/3/1
Y1 - 2017/3/1
N2 - 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.
AB - 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.
KW - Lung adenocarcinoma
KW - Lung squamous cell carcinoma
KW - Morphological features
KW - Pathological image
KW - Prognosis
KW - Statistical modeling
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U2 - 10.1016/j.jtho.2016.10.017
DO - 10.1016/j.jtho.2016.10.017
M3 - Article
C2 - 27826035
AN - SCOPUS:85015306784
SN - 1556-0864
VL - 12
SP - 501
EP - 509
JO - Journal of Thoracic Oncology
JF - Journal of Thoracic Oncology
IS - 3
ER -