{"doi":"10.3390/cancers13051128","title":"Nongaussian Intravoxel Incoherent Motion Diffusion Weighted and Fast Exchange Regime Dynamic Contrast-Enhanced-MRI of Nasopharyngeal Carcinoma: Preliminary Study for Predicting Locoregional Failure","abstract":"The aim of the present study was to identify whether the quantitative metrics from pre-treatment (TX) non-Gaussian intravoxel incoherent motion (NGIVIM) diffusion weighted (DW-) and fast exchange regime (FXR) dynamic contrast enhanced (DCE)-MRI can predict patients with locoregional failure (LRF) in nasopharyngeal carcinoma (NPC). Twenty-nine NPC patients underwent pre-TX DW- and DCE-MRI on a 3T MR scanner. DW imaging data from primary tumors were fitted to monoexponential (ADC) and NGIVIM (D, D*, f, and K) models. The metrics Ktrans, ve, and τi were estimated using the FXR model. Cumulative incidence (CI) analysis and Fine-Gray (FG) modeling were performed considering death as a competing risk. Mean ve values were significantly different between patients with and without LRF (p = 0.03). Mean f values showed a trend towards the difference between the groups (p = 0.08). Histograms exhibited inter primary tumor heterogeneity. The CI curves showed significant differences for the dichotomized cutoff value of ADC ≤ 0.68 × 10−3 (mm2/s), D ≤ 0.74 × 10−3 (mm2/s), and f ≤ 0.18 (p < 0.05). τi ≤ 0.89 (s) cutoff value showed borderline significance (p = 0.098). FG’s modeling showed a significant difference for the K cutoff value of ≤0.86 (p = 0.034). Results suggest that the role of pre-TX NGIVIM DW- and FXR DCE-MRI-derived metrics for predicting LRF in NPC than alone.","journal":"Cancers","year":2021,"id":200995,"datarank":0.31191623125197543,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.0,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9573,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":490683,"name":"Linda Chen","orcid":"0000-0001-7078-163X","position":1,"is_corresponding":false},{"id":307645,"name":"Jung Hun Oh","orcid":"0000-0001-8791-2755","position":2,"is_corresponding":false},{"id":512992,"name":"Kaveh Zakeri","orcid":"0000-0003-3921-480X","position":3,"is_corresponding":false},{"id":255796,"name":"Vaios Hatzoglou","orcid":"0000-0003-0252-0694","position":4,"is_corresponding":false},{"id":255798,"name":"C. Jillian Tsai","orcid":"0000-0003-0400-2655","position":5,"is_corresponding":false},{"id":255810,"name":"Nancy Y. Lee","orcid":"0000-0003-3044-9522","position":6,"is_corresponding":false},{"id":255808,"name":"Amita Shukla‐Dave","orcid":"0000-0001-7456-3197","position":7,"is_corresponding":false},{"id":255789,"name":"Ramesh Paudyal","orcid":"0000-0003-0302-211X","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:50:52.535403Z","pmid":"33800762","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":[]}