Observational studyChest2021
Predicting Lymph Node Metastasis in Non-small Cell Lung Cancer: Prospective External and Temporal Validation of the HAL and HOMER Models.
Observational study in Chest, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Rates of Nodal Upstaging following Systematic Endobronchial Ultrasound Lymph Node Staging in Non-Metastatic Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis.Respiration; international review of thoracic diseases · 2026Pooled it
- Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.BMC medical informatics and decision making · 2024Trial
- Single-cell and spatial transcriptomic profiling of POU5F1 in Lung Adenocarcinoma: Dynamics, spatial niche, and prognosis via multi-algorithm ML.Translational oncology · 2026Article
- Machine learning models using serum gastric biomarkers for the non-invasive prediction of atrophic gastritis: a comparative study.Frontiers in medicine · 2026Article
- Development and validation of an interpretable machine learning model for prediction of occult lymph node metastasis in clinical stage T1 lung adenocarcinoma.Translational lung cancer research · 2025Article
- Global trends and academic landscapes of ultrasound applications in lung cancer research: a bibliometric analysis (2000-2024).Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Deep Learning-Driven Multimodal Integration of miRNA and Radiomic for Lung Cancer Diagnosis.Biosensors · 2025Review
- Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis.Journal of translational medicine · 2025Article
- Mediastinal staging of nonsmall cell lung cancer: what's new?Breathe (Sheffield, England) · 2025Review
- The Impact of Preoperative Invasive Nodal Staging on Unexpected Mediastinal Upstaging in Early-Stage Non-small Cell Lung Cancer.Annals of surgical oncology · 2025Article
- Mediastinal staging lymph node probability map in non-small cell lung cancer.Respiratory research · 2025Article
- Nicotinamide N-methyltransferase negatively regulates metastasis-promoting property of cancer-associated fibroblasts in lung adenocarcinoma.Cancer communications (London, England) · 2025Article
- Machine learning predictive models and risk factors for lymph node metastasis in non-small cell lung cancer.BMC pulmonary medicine · 2024Article
- Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2024Article
- SLCO4A1, as a novel prognostic biomarker of non‑small cell lung cancer, promotes cell proliferation and migration.International journal of oncology · 2024Article
- Article
- An accurate prediction of negative lymph node metastasis with consideration of glucose metabolism in early-stage non-small cell lung cancer.General thoracic and cardiovascular surgery · 2024Article
- Review
- Efficacy of radiomics model based on the concept of gross tumor volume and clinical target volume in predicting occult lymph node metastasis in non-small cell lung cancer.Frontiers in oncology · 2023Article
- Systematic endoscopic staging of mediastinum to determine impact on radiotherapy for locally advanced lung cancer (SEISMIC): protocol for a prospective single arm multicentre interventional study.BMC pulmonary medicine · 2022Article
Corrections and comments
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Authors and funding
44 authors at 9 institutions in 2 countries.
Funding
Abstract
backgroundTwo models, the Help with the Assessment of Adenopathy in Lung cancer (HAL) and Help with Oncologic Mediastinal Evaluation for Radiation (HOMER), were recently developed to estimate the probability of nodal disease in patients with non-small cell lung cancer (NSCLC) as determined by endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). The objective of this study was to prospectively externally validate both models at multiple centers. RESEARCH QUESTION: Are the HAL and HOMER models valid across multiple centers? STUDY DESIGN AND
methodsThis multicenter prospective observational cohort study enrolled consecutive patients with PET-CT clinical-radiographic stages T1-3, N0-3, M0 NSCLC undergoing EBUS-TBNA staging. HOMER was used to predict the probability of N0 vs N1 vs N2 or N3 (N2|3) disease, and HAL was used to predict the probability of N2|3 (vs N0 or N1) disease. Model discrimination was assessed using the area under the receiver operating characteristics curve (ROC-AUC), and calibration was assessed using the Brier score, calibration plots, and the Hosmer-Lemeshow test.
resultsThirteen centers enrolled 1,799 patients. HAL and HOMER demonstrated good discrimination: HAL ROC-AUC = 0.873 (95%CI, 0.856-0.891) and HOMER ROC-AUC = 0.837 (95%CI, 0.814-0.859) for predicting N1 disease or higher (N1|2|3) and 0.876 (95%CI, 0.855-0.897) for predicting N2|3 disease. Brier scores were 0.117 and 0.349, respectively. Calibration plots demonstrated good calibration for both models. For HAL, the difference between forecast and observed probability of N2|3 disease was +0.012; for HOMER, the difference for N1|2|3 was -0.018 and for N2|3 was +0.002. The Hosmer-Lemeshow test was significant for both models (P = .034 and .002), indicating a small but statistically significant calibration error.
interpretationHAL and HOMER demonstrated good discrimination and calibration in multiple centers. Although calibration error was present, the magnitude of the error is small, such that the models are informative.
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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.