ArticleChest2023
The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation.
Article in Chest, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 10 citations in OpenAlex.
- Machine learning models predict survival in unresectable stage III non-small cell lung cancer: Surveillance, Epidemiology, and End Results and Chinese cohort study.Journal of thoracic disease · 2026Article
- Article
- Theoretical Clinical Utility of Advanced Practice Provider Use of an Artificial Intelligence Radiomics-based Tool for Pulmonary Nodule Evaluation and Management.CHEST pulmonary · 2026Article
- Evaluation of Lung Cancer Probability Models and Guideline Recommendations in Settings With a High Prevalence of Cancer.CHEST pulmonary · 2026Article
- Use of the TREAT 2.0 Model to Create a Web-Based Lung Nodule Calculator for High-Risk Patients.Annals of thoracic surgery short reports · 2026Article
- SGLT2 inhibitor use reduces progression and surgical intervention of persistent pulmonary nodules.Lung cancer (Amsterdam, Netherlands) · 2026Article
- Cost-effectiveness analysis of artificial intelligence-assisted risk stratification of indeterminate pulmonary nodules.PloS one · 2026Article
- Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model.Cancers · 2025Article
- Validation of a High-Specificity Blood Autoantibody Test to Detect Lung Cancer in Pulmonary Nodules.CHEST pulmonary · 2025Article
- The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort.CHEST pulmonary · 2024Article
- Advancing NSCLC pathological subtype prediction with interpretable machine learning: a comprehensive radiomics-based approach.Frontiers in medicine · 2024Article
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Authors and funding
18 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundAppropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation. RESEARCH QUESTION: Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models? STUDY DESIGN AND
methodsClinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots.
resultsTwo-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23.
interpretationThe TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.
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