A novel real-world data methodology for lymphoma outcome classification: the real-world Lugano study

Aim: In oncology trials, blinded independent central review (BICR) is the standard for treatment response classification. Real-world data methodologies that align with BICR may reduce misclassification in real-world evidence (RWE) studies and enhance reproducibility, increasing value of RWE. We aimed to develop and validate a novel real-world data-based methodology – real-world Lugano (rwLugano) – for assessing lymphoma response to align with clinical trials. Materials a medical oncologist adjudicated discordances. Results: We compared initial treatment responses using three methods: physician-charted from electronic health records, rwLugano-derived per Lugano 2014 and BICR-adjudicated per Lugano 2014. Agreement was assessed via percentage concordance, kappa (κ), and multivariable generalized linear mixed modeling for assigning complete response (CR). Among 178 patients, CR rates were 63.5% (physician-charted), 81.5% (rwLugano) and 83.1% (BICR). Compared with BICR, rwLugano showed higher agreement (87.9%, κ = 0.52) than physician-charted (77.0%, κ = 0.40). The generalized linear mixed modeling analyses identified clinical factors associated with concordance: for physician charted assessments, greater numbers of extranodal sites increased agreement with BICR (OR 1.92), while MYC mutation (OR 0.38) and anemia (OR 0.37) reduced agreement. For rwLugano, nonprivate insurance was associated with higher agreement (odds ratio OR: 4.40), whereas MYC mutation reduced agreement (OR: 0.26). Conclusion: rwLugano improves real-world lymphoma response classification, aligning with BICR and supporting more accurate, reproducible RWE for clinical and regulatory decision-making. Using methods BICR and rwLugano may provide opportunities to minimize outcome misclassification and improve comparability of clinical trial and clinical practice approaches.