{"doi":"10.1148/ryai.210268","title":"Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis","abstract":"Purpose To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I–III/IV–VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis. Materials and Methods For this Health Insurance Portability and Accountability Act–compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0–F4, 2000–2016). Dataset 2 consisted of patients who had cirrhosis from other causes who underwent liver biopsy (Ishak 0–6, 2001–2021). Whole liver, LSVR, and spleen volumes were measured with contrast-enhanced CT by radiologists and the DL model. Areas under the receiver operating characteristic curve (AUCs) for diagnosing advanced fibrosis (≥METAVIR F2 or Ishak 3) and cirrhosis (≥METAVIR F4 or Ishak 5) were calculated. Multivariable models were built on dataset 1 and tested on datasets 1 (hold out) and 2. Results Datasets 1 and 2 consisted of 406 patients (median age, 50 years [IQR, 44–56 years]; 297 men) and 207 patients (median age, 50 years [IQR, 41–57 years]; 147 men), respectively. In dataset 1, the prediction of cirrhosis was similar between the manual versus automated measurements for spleen volume (AUC, 0.86 [95% CI: 0.82, 0.9] vs 0.85 [95% CI: 0.81, 0.89]; significantly noninferior, P < .001) and LSVR (AUC, 0.83 [95% CI: 0.78, 0.87] vs 0.79 [95% CI: 0.74, 0.84]; P < .001). The best performing multivariable model achieved AUCs of 0.94 (95% CI: 0.89, 0.99) and 0.79 (95% CI: 0.71, 0.87) for cirrhosis and 0.8 (95% CI: 0.69, 0.91) and 0.71 (95% CI: 0.64, 0.78) for advanced fibrosis in datasets 1 and 2, respectively. Conclusion The CT-based DL model performed similarly to radiologists. LSVR and splenic volume were predictive of advanced fibrosis and cirrhosis. Keywords: CT, Liver, Cirrhosis, Computer Applications-Detection/Diagnosis Supplemental material is available for this article. © RSNA, 2022","journal":"Radiology Artificial Intelligence","year":2022,"id":241305,"datarank":1.299095965831866,"base_score":3.7612001156935624,"endowment":3.7612001156935624,"self_citation_contribution":0.5641800173540344,"citation_network_contribution":0.7349159484778316,"self_endowment_contribution":0.5641800173540344,"citer_contribution":0.7349159484778316,"corpus_percentile":84.1107758954127,"corpus_rank":2055,"citation_count":42,"citer_count":29,"citers_with_citation_signal":22,"citers_with_endowment":22,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8748,"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":333814,"name":"Daniel C. Elton","orcid":"0000-0003-0249-1387","position":1,"is_corresponding":false},{"id":856248,"name":"Alexander H. Yang","orcid":"0000-0001-5975-5131","position":2,"is_corresponding":false},{"id":255441,"name":"Christopher Koh","orcid":"0000-0002-4755-5607","position":3,"is_corresponding":false},{"id":225662,"name":"David E. Kleiner","orcid":"0000-0003-3442-4453","position":4,"is_corresponding":false},{"id":311381,"name":"Meghan G. Lubner","orcid":"0000-0003-3788-2871","position":5,"is_corresponding":false},{"id":246773,"name":"Perry J. Pickhardt","orcid":"0000-0002-5534-8202","position":6,"is_corresponding":false},{"id":104554,"name":"Ronald M. Summers","orcid":"0000-0001-8081-7376","position":7,"is_corresponding":false},{"id":290735,"name":"Sungwon Lee","orcid":"0000-0002-1684-2996","position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":null,"created_at":"2026-07-19T00:22:56.227656Z","pmid":"36204530","pmcid":"PMC9530761","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":[]}