{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T16:09:00Z","timestamp":1790179740998,"version":"4.1.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":249,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004319","name":"Pfizer Inc.","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004319","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Vysioneer Inc."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Manual endpoint assessment is labor-intensive and variable. This study evaluated artificial intelligence (AI)-powered intracranial response assessment and explored imaging prognostic factors in the phase III CROWN trial of advanced treatment-naive anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>CROWN randomized patients to lorlatinib or crizotinib. Seventy-two with baseline brain metastases (BMs) had brain MRI every 8 weeks. We evaluated an FDA-cleared AI platform, VBrain, for automated lesion-level tracking of BMs and early tumor response (ETR) in relation to systemic progression-free survival (PFS), the primary endpoint of the trial. Progression-free survival was assessed by blinded central review, independent of AI. VBrain assessments were compared with 2 neuroradiologists (R1, R2). Early tumor response at the first on-treatment scan was derived from modified response evaluation criteria in solid tumors (mRECIST) target-lesion diameters and from AI-based all-lesion volumes (AI-ETR). Treatment-neutral Cox models related ETR to systemic PFS; discrimination was assessed using Uno\u2019s C and time-dependent AUC(t) with bootstrap uncertainty.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Per mRECIST v1.1, VBrain, R1, and R2 measured 68, 32, and 51 targets, with high concordance (r\u00a0=\u00a00.94). Across 666 time-points, pairwise assessment discordance and time to intracranial progression did not differ between AI and readers. Beyond mRECIST, VBrain tracked 322 tumors. AI-ETR significantly stratified PFS (multivariable-adjusted hazard ratio [HR] 0.44, 95% CI, 0.23-0.86), whereas target-diameter ETR was not significant (adjusted HR 0.64, 95% CI, 0.29-1.41). Adding AI-ETR improved prediction vs a clinical base model (\u0394c-index +0.07; AUC(t) 0.71 vs 0.55).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The AI and human readers reached a high agreement in endpoint evaluations. Volumetric all-BMs ETR outperforming target-only mRECIST offers early prognostic information in advanced ALK-positive NSCLC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>ClinicalTrials.gov identifier: NCT03052608.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/noajnl\/vdag236","type":"journal-article","created":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T13:52:59Z","timestamp":1788616379000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence-enabled early intracranial volumetric response predicts systemic progression-free survival in the phase III CROWN study"],"prefix":"10.1093","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7056-1899","authenticated-orcid":false,"given":"Shao-Lun","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, National Taiwan University Cancer Center , Taipei,","place":["Taiwan"]},{"name":"Graduate Institute of Oncology, National Taiwan University College of Medicine , Taipei,","place":["Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Cheng","family":"Chang","sequence":"additional","affiliation":[{"name":"Vysioneer Inc. , Cambridge, Massachusetts,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Hung","family":"Liang","sequence":"additional","affiliation":[{"name":"Vysioneer Inc. , Cambridge, Massachusetts,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po-Lin","family":"Chiang","sequence":"additional","affiliation":[{"name":"Vysioneer Inc. , Cambridge, Massachusetts,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Maresca","sequence":"additional","affiliation":[{"name":"Pfizer , Cambridge, Massachusetts,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erqi","family":"Pollom","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Stanford University , Palo Alto, California,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keith","family":"Wilner","sequence":"additional","affiliation":[{"name":"Pfizer , San Diego, California,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jen-Tang","family":"Lu","sequence":"additional","affiliation":[{"name":"Vysioneer Inc. , Cambridge, Massachusetts,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Toffalorio","sequence":"additional","affiliation":[{"name":"Pfizer , 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