{"doi":"10.1016/j.phro.2024.100602","title":"Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery","abstract":"Background and purpose: Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map (\"forward models\"), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images (\"inverse model\"), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS). Materials and methods: Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients. Results: Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model. Conclusions: A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.","journal":"Physics and Imaging in Radiation Oncology","year":2024,"id":475875,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9363,"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":577583,"name":"Tuğçe Kütük","orcid":"0000-0003-0416-8612","position":1,"is_corresponding":false},{"id":103870,"name":"Mahmoud A. Abdalah","orcid":"0000-0002-0573-4121","position":2,"is_corresponding":false},{"id":723895,"name":"Olya Stringfield","orcid":null,"position":3,"is_corresponding":false},{"id":503495,"name":"Harshan Ravi","orcid":"0000-0003-4118-4240","position":4,"is_corresponding":false},{"id":831862,"name":"Matthew N. Mills","orcid":"0000-0002-8483-105X","position":5,"is_corresponding":false},{"id":1067092,"name":"Jasmine A. Graham","orcid":null,"position":6,"is_corresponding":false},{"id":680259,"name":"Kujtim Latifi","orcid":"0000-0002-7968-2571","position":7,"is_corresponding":false},{"id":795061,"name":"Wilfrido Moreno","orcid":"0000-0001-7003-1177","position":8,"is_corresponding":false},{"id":325113,"name":"Kamran A. Ahmed","orcid":"0000-0003-3331-1529","position":9,"is_corresponding":false},{"id":503500,"name":"Natarajan Raghunand","orcid":"0000-0002-8893-7687","position":10,"is_corresponding":false},{"id":795059,"name":"Shraddha Pandey","orcid":"0000-0003-4119-5142","position":0,"is_corresponding":true}],"reference_count":63,"raw_metadata":null,"created_at":"2026-07-19T02:06:21.071690Z","pmid":"39040435","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":[]}