{"doi":"10.1148/ryai.210174","title":"Toward Reduction in False-Positive Thyroid Nodule Biopsies with a Deep Learning–based Risk Stratification System Using US Cine-Clip Images","abstract":"Purpose To develop a deep learning–based risk stratification system for thyroid nodules using US cine images. Materials and Methods In this retrospective study, 192 biopsy-confirmed thyroid nodules (175 benign, 17 malignant) in 167 unique patients (mean age, 56 years ± 16 [SD], 137 women) undergoing cine US between April 2017 and May 2018 with American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS)–structured radiology reports were evaluated. A deep learning–based system that exploits the cine images obtained during three-dimensional volumetric thyroid scans and outputs malignancy risk was developed and compared, using fivefold cross-validation, against a two-dimensional (2D) deep learning–based model (Static-2DCNN), a radiomics-based model using cine images (Cine-Radiomics), and the ACR TI-RADS level, with histopathologic diagnosis as ground truth. The system was used to revise the ACR TI-RADS recommendation, and its diagnostic performance was compared against the original ACR TI-RADS. Results The system achieved higher average area under the receiver operating characteristic curve (AUC, 0.88) than Static-2DCNN (0.72, P = .03) and tended toward higher average AUC than Cine-Radiomics (0.78, P = .16) and ACR TI-RADS level (0.80, P = .21). The system downgraded recommendations for 92 benign and two malignant nodules and upgraded none. The revised recommendation achieved higher specificity (139 of 175, 79.4%) than the original ACR TI-RADS (47 of 175, 26.9%; P < .001), with no difference in sensitivity (12 of 17, 71% and 14 of 17, 82%, respectively; P = .63). Conclusion The risk stratification system using US cine images had higher diagnostic performance than prior models and improved specificity of ACR TI-RADS when used to revise ACR TI-RADS recommendation. Keywords: Neural Networks, US, Abdomen/GI, Head/Neck, Thyroid, Computer Applications–3D, Oncology, Diagnosis, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022","journal":"Radiology Artificial Intelligence","year":2022,"id":245492,"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":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9418,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":881251,"name":"Tara Kapoor","orcid":null,"position":1,"is_corresponding":false},{"id":880532,"name":"Minhaj Nur Alam","orcid":"0000-0003-3095-2232","position":2,"is_corresponding":false},{"id":880533,"name":"Alfiia Galimzianova","orcid":"0000-0002-2901-6423","position":3,"is_corresponding":false},{"id":880534,"name":"Saad Syed","orcid":"0000-0001-7239-3905","position":4,"is_corresponding":false},{"id":880535,"name":"Mete U. Akdogan","orcid":"0000-0003-1520-1481","position":5,"is_corresponding":false},{"id":881252,"name":"Emel Alkım","orcid":null,"position":6,"is_corresponding":false},{"id":339875,"name":"Andrew L. Wentland","orcid":"0000-0003-1736-8218","position":7,"is_corresponding":false},{"id":286223,"name":"Nikhil Madhuripan","orcid":"0000-0002-3690-5261","position":8,"is_corresponding":false},{"id":880536,"name":"Daniel Goff","orcid":"0000-0003-2109-1696","position":9,"is_corresponding":false},{"id":881253,"name":"Victoria Barbee","orcid":null,"position":10,"is_corresponding":false},{"id":318539,"name":"Natasha Sheybani","orcid":"0000-0002-1137-058X","position":11,"is_corresponding":false},{"id":11042,"name":"Hersh Sagreiya","orcid":"0000-0002-2909-6793","position":12,"is_corresponding":false},{"id":280795,"name":"Daniel L. Rubin","orcid":"0000-0001-5057-4369","position":13,"is_corresponding":false},{"id":880537,"name":"Terry S. Desser","orcid":"0000-0001-7983-9738","position":14,"is_corresponding":false},{"id":880531,"name":"Rikiya Yamashita","orcid":"0000-0002-2686-2333","position":0,"is_corresponding":true}],"reference_count":32,"raw_metadata":null,"created_at":"2026-07-19T00:23:39.284121Z","pmid":"35652118","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":[]}