Whole slide imaging of tumour microenvironment in classical Hodgkin's lymphoma: development of a clinical prediction model based on programmed death-ligand 1 and tumorous Reed-Sternberg cells

JOURNAL OF CLINICAL PATHOLOGY(2023)

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摘要
AimsThe prognostic impact of programmed death-ligand 1 (PD-L1) cells in classic Hodgkin lymphoma (cHL) tumour microenvironment remains undefined.MethodsModel development via Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines were followed. PD-L1+ and CD30+ tumoral Reed-Sternberg cells were quantified through whole slide imaging and digital image analysis in 155 digital histopathological slides of cHL. Univariate and multivariate survival analyses were performed. The analyses were reproduced for patients with advanced stages (IIB, III and IV) using the Advanced-stage cHL International Prognostic Index.ResultsThe PD-L1/CD30 ratio was statistically significantly associated with survival outcomes. Patients with a PD-L1/CD30 ratio above 47.1 presented a shorter overall survival (mean OS: 53.7 months; 95% CI: 28.7 to 78.7) in comparison with patients below this threshold (mean OS: 105.4 months; 95% CI: 89.6 to 121.3) (p=0.04). When adjusted for covariates, the PD-L1/CD30 ratio retained prognostic impact, both for the OS (HR: 1.005; 95% CI: 1.002 to 1.008; p=0.000) and the progression-free survival (HR: 3.442; 95% CI: 1.045 to 11.340; p=0.04) in a clinical and histopathological multivariate model including the male sex (HR: 3.551; 95% CI: 0.986 to 12.786; p=0.05), a percentage of tumoral cells >= 10.1% (HR: 1.044; 95% CI: 1.003 to 1.087; p=0.03) and high risk International Prognostic Score (>= 3 points) (HR: 6.453; 95% CI: 1.970 to 21.134; p=0.002).ConclusionsThe PD-L1/CD30 ratio identifies a group of cHL patients with an increased risk of treatment failure. Its clinical application can be performed as it constitutes an easy to implement pathological information in the diagnostic work-up of patients with cHL.
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关键词
lymphoma, pathology, molecular, image processing, computer-assisted, hematologic diseases
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