ArticleScientific reports2017
Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 3 of them syntheses that pooled it.
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Who cites it
85 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence in Lymphoma Histopathology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
- Health State Utility Values in Early-Stage Non-small Cell Lung Cancer: A Systematic Literature Review.PharmacoEconomics - open · 2023Pooled it
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- Association Between Postoperative Skeletal Muscle Loss and Disease-Free Survival in Node-Negative NSCLC ≤ 4 cm Without Adjuvant Therapy: A 5-Years Trend Analysis.Annals of surgical oncology · 2026Article
- A deep learning framework to iDentify prOgnostically releVant cancEr Regions (DOVER) within whole slide histopathology images.Cancer letters · 2026Article
- Prognostic value of nuclear features based on tumor-associated collagen signatures in breast cancer.NPJ breast cancer · 2025Article
- 3D spatial sampling to quantify morphologic heterogeneity in isocitrate dehydrogenase-wildtype glioblastoma.Journal of neuropathology and experimental neurology · 2025Article
- Pathology image-based predictive model for individual survival time of early-stage lung adenocarcinoma patients.Scientific reports · 2025Article
- Pathomics-based machine learning models for optimizing LungPro navigational bronchoscopy in peripheral lung lesion diagnosis: a retrospective study.Biomedical engineering online · 2025Article
- The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?Diagnostics (Basel, Switzerland) · 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
- Computationally integrating radiology and pathology image features for predicting treatment benefit and outcome in lung cancer.NPJ precision oncology · 2025Article
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- Predicting Postoperative Lung Cancer Recurrence and Survival Using Cox Proportional Hazards Regression and Machine Learning.Cancers · 2024Article
- Extreme wrinkling of the nuclear lamina is a morphological marker of cancer.NPJ precision oncology · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Histomorphometric Analysis of Oral Squamous Cell Carcinoma, Apparently Normal Adjacent Mucosa and Epithelial Dysplasia.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.Scientific reports · 2024Article
- Human-machine interaction in computational cancer pathology.ESMO real world data and digital oncology · 2024Review
25 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence. This model was then validated on two independent sets of early stage NSCLC patients, Cohort #2 (n = 119) and Cohort #3 (n = 116). The model yielded an accuracy of 81% for prediction of recurrence in the training Cohort #1, 82% and 75% in the validation Cohorts #2 and #3 respectively. A multivariable Cox proportional hazard model of Cohort #2, incorporating gender and traditional prognostic variables such as nodal status and stage indicated that the computer extracted histomorphometric score was an independent prognostic factor (hazard ratio = 20.81, 95% CI: 6.42-67.52, P < 0.001).
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