{"doi":"10.3390/bioengineering11050497","title":"Improving the Generalizability of Deep Learning for T2-Lesion Segmentation of Gliomas in the Post-Treatment Setting","abstract":"Although fully automated volumetric approaches for monitoring brain tumor response have many advantages, most available deep learning models are optimized for highly curated, multi-contrast MRI from newly diagnosed gliomas, which are not representative of post-treatment cases in the clinic. Improving segmentation for treated patients is critical to accurately tracking changes in response to therapy. We investigated mixing data from newly diagnosed (n = 208) and treated (n = 221) gliomas in training, applying transfer learning (TL) from pre- to post-treatment imaging domains, and incorporating spatial regularization for T2-lesion segmentation using only T2 FLAIR images as input to improve generalization post-treatment. These approaches were evaluated on 24 patients suspected of progression who had received prior treatment. Including 26% of treated patients in training improved performance by 13.9%, and including more treated and untreated patients resulted in minimal changes. Fine-tuning with treated glioma improved sensitivity compared to data mixing by 2.5% (p < 0.05), and spatial regularization further improved performance when used with TL by 95th HD, Dice, and sensitivity (6.8%, 0.8%, 2.2%; p < 0.05). While training with ≥60 treated patients yielded the majority of performance gain, TL and spatial regularization further improved T2-lesion segmentation to treated gliomas using a single MR contrast and minimal processing, demonstrating clinical utility in response assessment.","journal":"Bioengineering","year":2024,"id":497628,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9453,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":305596,"name":"Francesco Calivá","orcid":"0000-0002-0425-7511","position":1,"is_corresponding":false},{"id":472084,"name":"Pablo F. Damasceno","orcid":"0000-0001-5615-121X","position":2,"is_corresponding":false},{"id":328984,"name":"Tracy Luks","orcid":"0000-0003-0994-9960","position":3,"is_corresponding":false},{"id":345504,"name":"Marisa Lafontaine","orcid":"0000-0002-2352-3299","position":4,"is_corresponding":false},{"id":425439,"name":"Julia Cluceru","orcid":null,"position":5,"is_corresponding":false},{"id":1049281,"name":"Anil Kemisetti","orcid":null,"position":6,"is_corresponding":false},{"id":345508,"name":"Yan Li","orcid":"0000-0003-2145-2869","position":7,"is_corresponding":false},{"id":238091,"name":"Annette M. Molinaro","orcid":"0000-0002-9854-7404","position":8,"is_corresponding":false},{"id":22114,"name":"Valentina Pedoia","orcid":"0000-0002-9745-955X","position":9,"is_corresponding":false},{"id":267240,"name":"Javier Villanueva-Meyer","orcid":"0000-0002-5910-0757","position":10,"is_corresponding":false},{"id":345506,"name":"Janine Lupo","orcid":"0000-0002-0051-6387","position":11,"is_corresponding":false},{"id":1297095,"name":"Jacob Ellison","orcid":"0000-0003-1164-332X","position":0,"is_corresponding":true}],"reference_count":66,"raw_metadata":null,"created_at":"2026-07-19T02:09:34.764412Z","pmid":"38790363","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":[]}