Artificial intelligence-enabled early intracranial volumetric response predicts systemic progression-free survival in the phase III CROWN study
Open Access
- 12 January 2026
- journal article
- Published by Oxford University Press (OUP) in Neuro-Oncology Advances
- Vol. 8 (1)
Abstract
Background: 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). Methods: 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’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, 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 (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. Trial Registration: ClinicalTrials.gov identifier: NCT03052608.Keywords
Funding Information
- Pfizer
- Vysioneer Inc.
This publication has 35 references indexed in Scilit:
- Deep Learning-Based Detect-Then-Track Pipeline for Treatment Outcome Assessments in Immunotherapy-Treated Liver CancerJournal of Imaging Informatics in Medicine, 2024
- Efficacy and safety of first-line lorlatinib versus crizotinib in patients with advanced, ALK-positive non-small-cell lung cancer: updated analysis of data from the phase 3, randomised, open-label CROWN studyThe Lancet Respiratory Medicine, 2022
- First-Line Lorlatinib or Crizotinib in Advanced ALK -Positive Lung CancerNew England Journal of Medicine, 2020
- Best Response According to RECIST During First-line EGFR-TKI Treatment Predicts Survival in EGFR Mutation-positive Non–Small-cell Lung Cancer PatientsClinical Lung Cancer, 2018
- Observer variability in RECIST-based tumour burden measurements: a meta-analysisEuropean Journal of Cancer, 2016
- Insights into brain metastasis in patients with ALK+ lung cancer: is the brain truly a sanctuary?Cancer and Metastasis Reviews, 2015
- Brain metastases in patients with EGFR -mutated or ALK -rearranged non-small-cell lung cancersLung Cancer, 2015
- Estimating and comparing time‐dependent areas under receiver operating characteristic curves for censored event times with competing risksStatistics in Medicine, 2013
- Dabrafenib in patients with Val600Glu or Val600Lys BRAF-mutant melanoma metastatic to the brain (BREAK-MB): a multicentre, open-label, phase 2 trialThe Lancet Oncology, 2012
- A practical guide to understanding Kaplan‐Meier curvesOtolaryngology–Head and Neck Surgery, 2010