ArticleClinica chimica acta; international journal of clinical chemistry2022
Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers.
Article in Clinica chimica acta; international journal of clinical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Biomarkers Suitable for Early Detection of Intrathoracic Cancers in Primary Care: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- Leveraging Commercially Available Protein Assays as Biomarkers for Lung Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Longitudinal Analysis of CYFRA 21-1 Levels in Patients with Pulmonary Nodules: Differential Trajectories Between Benign and Malignant Cases and Impact of Tumor Resection.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Predictive Factors and Nomogram for Malignant Pulmonary Nodules (≤ 1 cm).Canadian respiratory journal · 2026Article
- Identification of PIWI-interacting RNAs based models for lung adenocarcinoma early detection: a multicenter cohort study.Molecular biomedicine · 2025Article
- Biomarkers for the diagnosis of indeterminate pulmonary nodules: are we there yet?Journal of thoracic disease · 2025Review
- Radiomic 'Stress Test': exploration of a deep learning radiomic model in a high-risk prospective lung nodule cohort.BMJ open respiratory research · 2025Article
- Multiple Indeterminate pulmonary nodules (IPNs) as independent prognostic indicators in pediatric osteosarcoma: A ten-year retrospective study.Journal of bone oncology · 2025Article
- Optimizing Biomarker Models for Biologically Heterogeneous Cancers: A Nested Model Approach for Lung Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Potential for trans-pulmonary tumor markers in the early diagnosis of lung cancer: a case report.BMC pulmonary medicine · 2024Article
- Article
- Genetic susceptibility loci of lung cancer are associated with malignant risk of pulmonary nodules and improve malignancy diagnosis based on CEA levels.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2023Article
- Review
- Circulating proteome for pulmonary nodule malignancy.Journal of the National Cancer Institute · 2023Article
- Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundNon-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer.
methodsProtein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index.
resultsAge, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy.
conclusionsA combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.
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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.