ArticleScientific reports2018
Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it, 148 citations in OpenAlex.
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
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- Integration of Histologic Pattern Classifiers and Graph Convolutional Networks for Prognostic Prediction in Lung Adenocarcinoma.JTO clinical and research reports · 2026Article
- Malignant vs. Non-malignant Annotations on TCGA Breast Cancer Whole Slide Images for AI Analysis.Scientific data · 2026Article
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- Estimating major pathological response in non-small cell lung cancer patients with post-neoadjuvant therapy using MMT-net.Scientific reports · 2026Article
- Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.Statistics in medicine · 2026Article
- Biological feature-based machine learning in histopathological images: a systematic review.Journal of pathology informatics · 2026Review
- Inferring tumor immune microenvironment -related risk states from pretreatment H&E pathomics and clinical biomarkers to predict checkpoint inhibitor pneumonitis in advanced NSCLC: a multicenter multimodal study.Frontiers in immunology · 2026Article
- Leveraging Artificial Intelligence for Clinical Study Matching: Key Threads for Interweaving Data Science and Implementation Science.JMIR formative research · 2025Article
- Morphological analysis of tumor microenvironment in HER2-positive breast cancer: predicting response to neoadjuvant chemotherapy on histopathological images.Breast cancer research : BCR · 2025Article
- Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.Scientific reports · 2025Article
- Article
- A promptable CT foundation model for solid tumor evaluation.NPJ precision oncology · 2025Article
- Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.Scientific reports · 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
- A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.Cancer research · 2024Article
- Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.Nature communications · 2024Article
- Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.Cancers · 2024Review
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
9 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
Pathology images capture tumor histomorphological details in high resolution. However, manual detection and characterization of tumor regions in pathology images is labor intensive and subjective. Using a deep convolutional neural network (CNN), we developed an automated tumor region recognition system for lung cancer pathology images. From the identified tumor regions, we extracted 22 well-defined shape and boundary features and found that 15 of them were significantly associated with patient survival outcome in lung adenocarcinoma patients from the National Lung Screening Trial. A tumor region shape-based prognostic model was developed and validated in an independent patient cohort (n = 389). The predicted high-risk group had significantly worse survival than the low-risk group (p value = 0.0029). Predicted risk group serves as an independent prognostic factor (high-risk vs. low-risk, hazard ratio = 2.25, 95% CI 1.34-3.77, p value = 0.0022) after adjusting for age, gender, smoking status, and stage. This study provides new insights into the relationship between tumor shape and patient prognosis.
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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.