{"doi":"10.18383/j.tom.2019.00025","title":"Retrospective Correction of ADC for Gradient Nonlinearity Errors in Multicenter Breast DWI Trials: ACRIN6698 Multiplatform Feasibility Study","abstract":"The presented analysis of multisite, multiplatform clinical oncology trial data sought to enhance quantitative utility of the apparent diffusion coefficient (ADC) metric, derived from diffusion-weighted magnetic resonance imaging, by reducing technical interplatform variability owing to systematic gradient nonlinearity (GNL). This study tested the feasibility and effectiveness of a retrospective GNL correction (GNC) implementation for quantitative quality control phantom data, as well as in a representative subset of 60 subjects from the ACRIN 6698 breast cancer therapy response trial who were scanned on 6 different gradient systems. The GNL ADC correction based on a previously developed formalism was applied to trace-DWI using system-specific gradient-channel fields derived from vendor-provided spherical harmonic tables. For quantitative DWI phantom images acquired in typical breast imaging positions, the GNC improved interplatform accuracy from a median of 6% down to 0.5% and reproducibility of 11% down to 2.5%. Across studied trial subjects, GNC increased low ADC (<1 µm2/ms) tumor volume by 16% and histogram percentiles by 5%–8%, uniformly shifting percentile-dependent ADC thresholds by ∼0.06 µm2/ms. This feasibility study lays the grounds for retrospective GNC implementation in multiplatform clinical imaging trials to improve accuracy and reproducibility of ADC metrics used for breast cancer treatment response prediction.","journal":"Tomography","year":2020,"id":71554,"datarank":0.44166584687496613,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.0,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9475,"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":295233,"name":"David C. Newitt","orcid":"0000-0002-1735-6682","position":1,"is_corresponding":false},{"id":378242,"name":"Ghoncheh Amouzandeh","orcid":"0000-0002-9423-7003","position":2,"is_corresponding":false},{"id":296771,"name":"Lisa J. Wilmes","orcid":null,"position":3,"is_corresponding":false},{"id":378243,"name":"Ek T. Tan","orcid":"0000-0003-2847-9378","position":4,"is_corresponding":false},{"id":378244,"name":"Luca Marinelli","orcid":"0000-0001-7775-5952","position":5,"is_corresponding":false},{"id":379672,"name":"Ajit Devaraj","orcid":null,"position":6,"is_corresponding":false},{"id":378245,"name":"Johannes Peeters","orcid":"0000-0001-6639-0863","position":7,"is_corresponding":false},{"id":379673,"name":"Shivraman Giri","orcid":null,"position":8,"is_corresponding":false},{"id":379674,"name":"Axel vom Endt","orcid":null,"position":9,"is_corresponding":false},{"id":242424,"name":"Nola M. Hylton","orcid":"0000-0002-6747-1662","position":10,"is_corresponding":false},{"id":294006,"name":"Savannah C. Partridge","orcid":"0000-0001-6370-9111","position":11,"is_corresponding":false},{"id":246586,"name":"Thomas L. Chenevert","orcid":"0000-0003-1476-4274","position":12,"is_corresponding":false},{"id":336781,"name":"Dariya Malyarenko","orcid":"0000-0003-0403-1501","position":0,"is_corresponding":true}],"reference_count":21,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T21:44:25.469842Z","pmid":"32548284","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":[]}