{"id":"https://openalex.org/W7147206319","doi":"https://doi.org/10.48550/arxiv.2603.26820","title":"Toward Actionable Digital Twins for Radiation-Based Imaging and Therapy: Mathematical Formulation, Modular Workflow, and an OpenKBP-Based Dose-Surrogate Prototype","display_name":"Toward Actionable Digital Twins for Radiation-Based Imaging and Therapy: Mathematical Formulation, Modular Workflow, and an OpenKBP-Based Dose-Surrogate Prototype","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7147206319","doi":"https://doi.org/10.48550/arxiv.2603.26820"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26820","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.26820","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064988755","display_name":"Hsin\u2010Hsiung Huang","orcid":"https://orcid.org/0000-0001-7150-7229"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Hsin-Hsiung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132563254","display_name":"Bulent Soykan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Soykan, Bulent","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.4156000018119812,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.4156000018119812,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11359","display_name":"Effects of Radiation Exposure","score":0.15919999778270721,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.08240000158548355,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.8108999729156494},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6288999915122986},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6236000061035156},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6229000091552734},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.4812999963760376},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.41510000824928284},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4097999930381775}],"concepts":[{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.8108999729156494},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6938999891281128},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6236000061035156},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6229000091552734},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.4812999963760376},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.41510000824928284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4147999882698059},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39899998903274536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32820001244544983},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26820","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.26820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26820","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.6449332237243652,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Digital":[0],"twins":[1,32],"for":[2,29,103,182],"radiation-based":[3,34],"imaging":[4,35],"and":[5,18,36,38,55,58,64,97,121,128,140,156,161,187,202],"therapy":[6,37],"are":[7],"most":[8],"useful":[9],"when":[10],"they":[11],"assimilate":[12],"patient":[13],"data,":[14],"quantify":[15],"predictive":[16],"uncertainty,":[17],"support":[19],"clinically":[20],"constrained":[21],"decisions.":[22],"This":[23],"paper":[24],"presents":[25],"a":[26,74,90,113,178],"modular":[27],"framework":[28,49],"actionable":[30],"digital":[31],"in":[33,144],"instantiates":[39],"its":[40],"reproducible":[41,179],"open-data":[42],"component":[43],"using":[44,126],"the":[45,79,94,109,147,151],"\\openkbpfull{}":[46],"benchmark.":[47],"The":[48,171],"couples":[50],"PatientData,":[51],"Model,":[52],"Solver,":[53],"Calibration,":[54],"Decision":[56],"modules":[57],"formalizes":[59],"latent-state":[60],"updating,":[61],"uncertainty":[62,185],"propagation,":[63,186],"chance-constrained":[65],"action":[66],"selection.":[67],"As":[68],"an":[69,83],"initial":[70],"implementation,":[71],"we":[72,116],"build":[73],"GPU-ready":[75],"PyTorch/MONAI":[76],"reimplementation":[77],"of":[78,159],"\\openkbp{}":[80,172],"starter":[81],"pipeline:":[82],"11-channel,":[84],"19.2M-parameter":[85],"3D":[86],"U-Net":[87],"trained":[88],"with":[89,99,164,199],"masked":[91],"loss":[92],"over":[93],"feasible":[95],"region":[96],"equipped":[98],"Monte":[100,137],"Carlo":[101,138],"dropout":[102],"voxel-wise":[104],"epistemic":[105],"uncertainty.":[106],"To":[107],"emulate":[108],"update":[110],"loop":[111,134],"on":[112],"static":[114],"benchmark,":[115],"introduce":[117],"decoder-only":[118],"proxy":[119,188],"recalibration":[120],"illustrate":[122],"uncertainty-aware":[123],"virtual-therapy":[124],"evaluation":[125],"DVH-based":[127],"biological":[129],"utilities.":[130],"A":[131],"complete":[132],"three-fraction":[133],"including":[135],"recalibration,":[136],"inference,":[139],"spatial":[141],"optimization":[142],"executes":[143],"10.3~s.":[145],"On":[146],"100-patient":[148],"test":[149,180],"set,":[150],"model":[152],"achieved":[153],"mean":[154,166],"dose":[155,183],"DVH":[157],"scores":[158],"2.65":[160],"1.82~Gy,":[162],"respectively,":[163],"0.58~s":[165],"inference":[167],"time":[168],"per":[169],"patient.":[170],"case":[173],"study":[174],"thus":[175],"serves":[176],"as":[177],"bed":[181],"prediction,":[184],"closed-loop":[189],"adaptation,":[190],"while":[191],"future":[192],"institutional":[193],"studies":[194],"will":[195],"address":[196],"longitudinal":[197],"calibration":[198],"delivered-dose":[200],"logs":[201],"repeat":[203],"imaging.":[204]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-02T00:00:00"}
