{"doi":"10.1101/2022.07.11.22277439","title":"Improved Xerostomia Prediction in Head and Neck Cancer Patients with Dixon Magnetic Resonance Imaging of Glandular Adiposity: Validation of Semi-Quantitative Parotid T1 Signal Intensity Metrics for Biomarker Pre-Qualification","abstract":"Abstract Purpose Parotid whole-gland magnetic resonance (MR) T1 intensity, thresholded at the 90th percentile (T1 P90), has been previously reported to be a candidate MR imaging biomarker (MR-IBM) for improved prediction of xerostomia development after radiotherapy. Although P90 was previously derived from the parotid glands of T1-weighted MRI, in this study, we aim to validate P90 in an external cohort using fat only images reconstructed from a T1 Dixon MRI sequence, as well as determining alternative T1 intensity thresholds for potential qualification as predictive FDA BEST biomarkers of xerostomia development 6 months after radiotherapy (Xero 6m ). Methods MR-IBMs derived from T1 Dixon intensity-normalized scans from 76 head and neck cancer (HNC) patients were extracted from pre-treatment MR images. Scans were normalized to fat tissue, and imaging characteristics were quantified. A reference model and MR-IBM models were created using multivariable logistic regression to predict Xero 6m . External validation was performed using the model coefficients described in a previous study. The area under the curve (AUC) of the resulting models were compared. Stepwise forward feature selection was performed to discover additional MR-IBMs for improved predictions of xerostomia. Results The external validation of a previous model coefficients against our cohort showed decreased performance of the P90 MR-IBM model (AUC of 0.73 (CI 0.61-0.85)). The reference model exhibited improved performance when P90 was incorporated (AUC of 0.78 (CI 0.67-0.89)). Feature selection demonstrated the P10 MR-IBM provided performance improvements (AUC of 0.79 (CI: 0.69-0.90)). Conclusion Our findings validated P90 as predictive biomarker for radiation-induced xerostomia and showed MR-IBMs derived from Dixon sequences can improve Xero 6m prediction when compared to the reference model. Formal biomarker qualification should be considered for T1 sequences/relaxometry via formalized approaches.","journal":"medRxiv","year":2022,"id":303792,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9571,"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":996688,"name":"the MD Anderson Head and Neck Cancer Symptom Working Group","orcid":null,"position":1,"is_corresponding":false},{"id":689226,"name":"Keith L. Sanders","orcid":"0000-0003-0161-5384","position":2,"is_corresponding":false},{"id":810096,"name":"Sam Mulder","orcid":"0000-0002-6981-9925","position":3,"is_corresponding":false},{"id":519105,"name":"Kareem A. Wahid","orcid":"0000-0002-0503-0175","position":4,"is_corresponding":false},{"id":636231,"name":"Brigid A. McDonald","orcid":"0000-0003-4230-1330","position":5,"is_corresponding":false},{"id":266114,"name":"Sara Ahmed","orcid":"0000-0002-1791-8366","position":6,"is_corresponding":false},{"id":636232,"name":"Travis C. Salzillo","orcid":"0000-0001-6271-9879","position":7,"is_corresponding":false},{"id":426705,"name":"Renjie He","orcid":"0000-0001-9166-6286","position":8,"is_corresponding":false},{"id":653657,"name":"Mohamed A. 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Hutcheson","orcid":"0000-0003-3710-5706","position":18,"is_corresponding":false},{"id":351185,"name":"Stephen Y. Lai","orcid":"0000-0001-8301-7286","position":19,"is_corresponding":false},{"id":295555,"name":"Clifton D. Fuller","orcid":"0000-0002-5264-3994","position":20,"is_corresponding":false},{"id":103919,"name":"Lisanne V. van Dijk","orcid":"0000-0002-9515-5616","position":21,"is_corresponding":false},{"id":987721,"name":"Joint Head and Neck Radiotherapy-MRI Development Cooperative","orcid":null,"position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:32:28.468841Z","pmid":null,"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":[]}