AI-Enabled Early Intracranial Volumetric Response Predicts Systemic Progression Free Survival in the Phase III CROWN Study

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ID: 327899
2026
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Abstract
Abstract Background Manual endpoint assessment is labor-intensive and variable. This study evaluated AI-powered intracranial response assessment and explored imaging prognostic factors in the phase III CROWN trial of advanced treatment-naive ALK-positive NSCLC. Methods CROWN randomized patients to lorlatinib or crizotinib. Seventy-two with baseline brain metastases (BMs) had brain MRI every eight 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. PFS was assessed by blinded central review, independent of AI. VBrain assessments were compared with two neuroradiologists (R1, R2). ETR at the first on-treatment scan was derived from modified RECIST (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’s C and time-dependent AUC(t) with bootstrap uncertainty. Results Per mRECIST v1.1, VBrain, R1, and R2 measured 68, 32, and 51 targets, with high concordance (r =0.94). Across 666 time-points, pair-wise 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 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 versus a clinical base model (Δc-index +0.07; AUC(t) 0.71 vs 0.55). Conclusions 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. ClinicalTrials.gov identifier NCT03052608
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Authors Shao-Lun Lu, Yu-Cheng Chang, Chih-Hung Liang, Po-Lin Chiang, Kevin Maresca, Erqi Pollom, Keith Wilner, Jen-Tang Lu, Francesca Toffalorio, Giuseppe Giaccone, Chong Duan
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Journal Neuro-Oncology Advances
Year 2026
DOI
10.1093/noajnl/vdag236
URL
https://doi.org/10.1093/noajnl/vdag236
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