{"doi":"10.3390/cancers13153908","title":"Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas","abstract":"The present study aimed to investigate the correlation at pre-treatment (TX) between quantitative metrics derived from multimodality imaging (MMI), including 18F-FDG-PET/CT, 18F-FMISO-PET/CT, DW- and DCE-MRI, using a community detection algorithm (CDA) in head and neck squamous cell carcinoma (HNSCC) patients. Twenty-three HNSCC patients with 27 metastatic lymph nodes underwent a total of 69 MMI exams at pre-TX. Correlations among quantitative metrics derived from FDG-PET/CT (SUL), FMSIO-PET/CT (K1, k3, TBR, and DV), DW-MRI (ADC, IVIM [D, D*, and f]), and FXR DCE-MRI [Ktrans, ve, and τi]) were investigated using the CDA based on a “spin-glass model” coupled with the Spearman’s rank, ρ, analysis. Mean MRI T2 weighted tumor volumes and SULmean values were moderately positively correlated (ρ = 0.48, p = 0.01). ADC and D exhibited a moderate negative correlation with SULmean (ρ ≤ −0.42, p < 0.03 for both). K1 and Ktrans were positively correlated (ρ = 0.48, p = 0.01). In contrast, Ktrans and k3max were negatively correlated (ρ = −0.41, p = 0.03). CDA revealed four communities for 16 metrics interconnected with 33 edges in the network. DV, Ktrans, and K1 had 8, 7, and 6 edges in the network, respectively. After validation in a larger population, the CDA approach may aid in identifying useful biomarkers for developing individual patient care in HNSCC.","journal":"Cancers","year":2021,"id":195672,"datarank":0.32958368660043297,"base_score":2.1972245773362196,"endowment":2.1972245773362196,"self_citation_contribution":0.32958368660043297,"citation_network_contribution":0.0,"self_endowment_contribution":0.32958368660043297,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9581,"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":255788,"name":"Milan Grkovski","orcid":"0000-0001-7228-9497","position":1,"is_corresponding":false},{"id":307645,"name":"Jung Hun Oh","orcid":"0000-0001-8791-2755","position":2,"is_corresponding":false},{"id":255787,"name":"Heiko Schöder","orcid":"0000-0002-5170-4185","position":3,"is_corresponding":false},{"id":662737,"name":"David Aramburu Núñez","orcid":null,"position":4,"is_corresponding":false},{"id":255796,"name":"Vaios Hatzoglou","orcid":"0000-0003-0252-0694","position":5,"is_corresponding":false},{"id":87007,"name":"Joseph O. Deasy","orcid":"0000-0002-9437-266X","position":6,"is_corresponding":false},{"id":255807,"name":"John L. Humm","orcid":"0000-0003-4245-5591","position":7,"is_corresponding":false},{"id":255810,"name":"Nancy Y. Lee","orcid":"0000-0003-3044-9522","position":8,"is_corresponding":false},{"id":255808,"name":"Amita Shukla‐Dave","orcid":"0000-0001-7456-3197","position":9,"is_corresponding":false},{"id":255789,"name":"Ramesh Paudyal","orcid":"0000-0003-0302-211X","position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:50:07.691929Z","pmid":"34359810","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":[]}