ArticleBMC bioinformatics2014
Novel image markers for non-small cell lung cancer classification and survival prediction.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 72 citations in OpenAlex.
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- Lung cancer diagnosis based on weighted convolutional neural network using gene data expression.Scientific reports · 2024Article
- Improving the robustness and stability of a machine learning model for breast cancer prognosis through the use of multi-modal classifiers.Scientific reports · 2023Article
- Identification of h-TERT Promoter Mutations in Germline DNA from North Indian Lung Carcinoma Patients.Indian journal of clinical biochemistry : IJCB · 2023Article
- Progress on deep learning in digital pathology of breast cancer: a narrative review.Gland surgery · 2022Review
- A deep learning-based framework for lung cancer survival analysis with biomarker interpretation.BMC bioinformatics · 2020Article
- Towards pixel-to-pixel deep nucleus detection in microscopy images.BMC bioinformatics · 2019Article
- Deep learning with multimodal representation for pancancer prognosis prediction.Bioinformatics (Oxford, England) · 2019Article
- Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features.The British journal of radiology · 2019Article
- Statistical representation models for mutation information within genomic data.BMC bioinformatics · 2019Article
- Article
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- Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome.Scientific reports · 2018Article
- Deep learning based tissue analysis predicts outcome in colorectal cancer.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.