Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis

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.

Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images

“…Manual analysis involves assessments of features such as cellular morphology, nuclear structure, or tissue architecture, and such pre-specified image features have been inputted into support vector machines or random forests for tumor subtype classification and survival outcome analysis, e.g. (Luo et al 2017;Yu et al 2016;Mousavi et al 2015). However, pre-specified features may not generalize well across tumor types, so recent studies have focused on fully-automated approaches using convolutional neural networks (CNNs), bypassing the feature specification step.…”

Comprehensive analysis of lung cancer pathology images to discover tumor shape features that predict survival outcome

“…Tumor tissue slide scanning is becoming part of routine clinical practice for the acquisition of high resolution tumor histological details. In recent years, several computer algorithms for hematoxylin and eosin (H&E) stained pathology image analysis have been developed to aid pathologists in objective clinical diagnosis and prognosis [3][4][5][6][7] . Examples include an algorithm to extract stromal features 8 and an algorithm to assess cellular heterogeneity 6 as a prognostic factor in breast cancer.…”

“…More recently, studies have shown that morphological features are associated with patient prognosis in lung cancer as well 4,5,7 . Deep learning methods, such as convolution neural networks (CNNs), have been widely used in image segmentation, object classification and recognition [9][10][11] and are now being adapted in biomedical image analysis to facilitate cancer diagnosis.…”

Upregulated mrna expression of mtor in surgically resected early stage non-small cell lung carcinoma: a potential molecular targeted therapy

“…Smoking is the leading risk factor for developing lung cancer, but not all of the smokers develop cancer, suggests that environmental and genetic factors contributes to the risk of cancer development [3]. Although new techniques are developed to detect lung cancer at an early stage, the prognosis is still poor [4]. Hence, development of new therapeutic strategies is still a need for achieving better survival rates.…”