Novel image markers for non-small cell lung cancer classification and survival prediction

Abstract

BackgroundNon-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.ResultsIn this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated.ConclusionsThe proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers.

Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis

“…Previously, Wang et al 37 identified pathological image markers for ADC versus SCC classification and survival prediction. However, that study was based on only 122 patients with lung cancer.…”

A deep learning-based framework for lung cancer survival analysis with biomarker interpretation

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Lung cancer survival prediction from pathological images and genetic data — An integration study

“…To predict patients' survival, Wang et al [8] proposed an automated framework to find representative markers from histopathlogical images. However, these image markers are unable to provide accurate molecule level information which are also very important for cancer prognosis.…”