ArticleJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer2017
Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis.
Article in Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 2 of them syntheses that pooled it.
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107 citing papers in PubMed, 2 syntheses or guidelines pooled it, 207 citations in OpenAlex.
- Prognostic potential of lipid profiling in cancer patients: a systematic review of mass spectrometry-based studies.Lipids in health and disease · 2024Pooled it
- Decreased sphingomyelin (t34:1) is a candidate predictor for lung squamous cell carcinoma recurrence after radical surgery: a case-control study.BMC cancer · 2021Pooled it
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
- Hierarchical structure-guided high-dimensional multi-view clustering.Journal of multivariate analysis · 2026Article
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- A deep learning model for multiclass lung cancer classification using multimodal data fusion.Discover oncology · 2025Article
- Preoperative CT-based artificial intelligence-derived quantitative parameters and imaging features for predicting the invasiveness of histologically confirmed subcentimeter adenocarcinomatous nodules: a two-center study.Quantitative imaging in medicine and surgery · 2025Article
- Using spatial proteomics to enhance cell type assignments in histology images.Cell reports methods · 2025Article
- Ordinal Sparse Neural Networks for Modeling Gene- and Imaging-Environment Interactions.Statistics in medicine · 2025Article
- Artificial Intelligence-Based Model Exploiting Hematoxylin and Eosin Images to Predict Rare Gene Mutations in Patients With Lung Adenocarcinoma.JCO clinical cancer informatics · 2025Article
- Structure-Preserving Histopathological Stain Normalization via Attention-Guided Residual Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Pathological omics prediction of early and advanced colon cancer based on artificial intelligence model.Discover oncology · 2025Article
- Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.World journal of clinical oncology · 2025Review
- Accurate prediction of disease-free and overall survival in non-small cell lung cancer using patient-level multimodal weakly supervised learning.NPJ precision oncology · 2025Article
- Review
- An update on applications of digital pathology: primary diagnosis; telepathology, education and research.Diagnostic pathology · 2025Review
- Integration of histopathological image features and multi-dimensional omics data in predicting molecular features and survival in glioblastoma.Frontiers in medicine · 2025Article
- Predicting the risk of relapsed or refractory in patients with diffuse large B-cell lymphoma via deep learning.Frontiers in oncology · 2025Article
- Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer.British journal of cancer · 2024Article
- Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm.Medical molecular morphology · 2024Article
47 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
Abstract
introductionPathological 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.
methodWe 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.
resultsWe 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.
conclusionsThe results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer.
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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.