{"doi":"10.1371/journal.pone.0240184","title":"Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters?","abstract":"Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based features (outcome variables) over routine ranges of radiation dose, reconstruction kernel, and slice thickness. CT raw data of 23 nodules were reconstructed using 320 acquisition/reconstruction conditions (combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%, and 50% of protocol dose were simulated; reduced-dose and full-dose data were reconstructed using conventional filtered back-projection and iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm). Full-dose/B50f kernel reconstructions underwent expert segmentation for reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI was applied to 40 corresponding images (combinations of 4 doses and 10 kernels). Typical texture analysis metrics (including 5 histogram features, 13 Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level Dependence Matrix, and 3 Neighborhood Gray-Tone Difference Matrix) were computed per ROI. Reconstruction conditions resulting in no significant change in volume, density, or texture metrics were identified as \"compatible pairs\" for a given outcome variable. Our results indicate that as thickness increases, volumetric reproducibility decreases, while reproducibility of histogram- and texture-based features across different acquisition and reconstruction parameters improves. To achieve concomitant reproducibility of volumetric and radiomic results across studies, balanced standardization of the imaging acquisition parameters is required.","journal":"PLoS ONE","year":2020,"id":102874,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":25,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7521,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":501604,"name":"Mutlu Demirer","orcid":"0000-0002-6842-282X","position":1,"is_corresponding":false},{"id":501605,"name":"Kevin J. Little","orcid":"0000-0002-7241-0012","position":2,"is_corresponding":false},{"id":502404,"name":"Chiemezie C. Amadi","orcid":null,"position":3,"is_corresponding":false},{"id":502405,"name":"Gehan F. Ibrahim","orcid":null,"position":4,"is_corresponding":false},{"id":501606,"name":"Thomas O’Donnell","orcid":"0000-0003-3553-1306","position":5,"is_corresponding":false},{"id":502406,"name":"Rainer Grimmer","orcid":null,"position":6,"is_corresponding":false},{"id":501607,"name":"Vikash Gupta","orcid":"0000-0002-6786-9865","position":7,"is_corresponding":false},{"id":501608,"name":"Luciano M. Prevedello","orcid":"0000-0002-6768-6452","position":8,"is_corresponding":false},{"id":427879,"name":"Richard D. White","orcid":"0000-0002-1133-7819","position":9,"is_corresponding":false},{"id":501603,"name":"Barbaros S. Erdal","orcid":"0000-0003-3637-0102","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-18T22:41:53.173268Z","pmid":"33057454","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}