ArticleNeuro-oncology advances
Artificial intelligence-enabled early intracranial volumetric response predicts systemic progression-free survival in the phase III CROWN study.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03052608 (A PHASE 3, RANDOMIZED, OPEN-LABEL STUDY OF LORLATINIB), which is not on this map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A phase 3, randomized, open-label study of lorlatinib (pf-06463922) monotherapy versus crizotinib monotherapy in the first-line treatment of patients with advanced alk-positive non-small cell lung cancer
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11 authors.
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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( Results: Per mRECIST v1.1, VBrain, R1, and R2 measured 68, 32, and 51 targets, with high concordance ( 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.
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