["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 is 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 lung cancer patients.", "We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for non-small cell lung cancer patients. Tissue pathological images were analyzed for 523 adenocarcinoma (ADC) and 511 squamous cell carcinoma (SCC) patients from The Cancer Genome Atlas (TCGA) 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 tissue hematoxylin and eosin staining (H&E) pathological images 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: predicted high-risk group vs low-risk group, HR = 2.34 (1.12-4.91), p=0.024 for ADC patients and HR = 2.22 (1.15-4.27), p=0.017 for SCC patients, after adjusting for age, gender, smoking status and pathologic tumor stage.'] Does comprehensive Computational Pathological Image Analysis predict Lung Cancer Prognosis?