{"doi":"10.3174/ajnr.a7477","title":"Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins","abstract":"BACKGROUND AND PURPOSE: Currently, contrast-enhancing margins on T1WI are used to guide treatment of gliomas, yet tumor invasion beyond the contrast-enhancing region is a known confounding factor. Therefore, this study used postmortem tissue samples aligned with clinically acquired MRIs to quantify the relationship between intensity values and cellularity as well as to develop a radio-pathomic model to predict cellularity using MR imaging data. MATERIALS AND METHODS: This single-institution study used 93 samples collected at postmortem examination from 44 patients with brain cancer. Tissue samples were processed, stained with H&E, and digitized for nuclei segmentation and cell density calculation. Pre- and postgadolinium contrast T1WI, T2 FLAIR, and ADC images were collected from each patient's final acquisition before death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed-effects models were used to assess the relationship between single-image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 × 5 voxel tiles from each image, with a two-thirds to one-third train-test split for validation. RESULTS: ) and identified regions of hypercellularity beyond the contrast-enhancing region. CONCLUSIONS: A radio-pathomic model for cellularity trained with tissue samples acquired at postmortem examination is able to identify regions of hypercellular tumor beyond traditional imaging signatures.","journal":"American Journal of Neuroradiology","year":2022,"id":243767,"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":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9592,"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":388113,"name":"Allison Lowman","orcid":"0000-0002-2520-8145","position":1,"is_corresponding":false},{"id":820761,"name":"Michael Brehler","orcid":"0000-0002-4663-6587","position":2,"is_corresponding":false},{"id":821104,"name":"Fitzgerald Kyereme","orcid":null,"position":3,"is_corresponding":false},{"id":820762,"name":"Savannah Duenweg","orcid":"0000-0003-4010-7737","position":4,"is_corresponding":false},{"id":876281,"name":"Jonathan H. Sherman","orcid":"0000-0001-9482-3988","position":5,"is_corresponding":false},{"id":388112,"name":"Sean D. McGarry","orcid":"0000-0003-4937-2780","position":6,"is_corresponding":false},{"id":876839,"name":"E. Cochran","orcid":null,"position":7,"is_corresponding":false},{"id":392577,"name":"Jennifer Connelly","orcid":"0000-0002-6122-8573","position":8,"is_corresponding":false},{"id":392579,"name":"Wade M. Mueller","orcid":"0000-0003-1281-5250","position":9,"is_corresponding":false},{"id":876282,"name":"Mohit Agarwal","orcid":"0000-0001-6399-4098","position":10,"is_corresponding":false},{"id":388116,"name":"Anjishnu Banerjee","orcid":"0000-0002-6898-9148","position":11,"is_corresponding":false},{"id":364387,"name":"Peter S. LaViolette","orcid":"0000-0002-9602-6891","position":12,"is_corresponding":false},{"id":388117,"name":"Samuel Bobholz","orcid":"0000-0003-1525-7418","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T00:23:20.901576Z","pmid":"35422419","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":[]}