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Machine learning-based quantitative analysis of bone marrow fibrosis in patients with MF
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A phase II trial (NCT01981850) evaluated zinpentraxin alfa for the treatment of patients with myelofibrosis (MF).1 The study design and results from this trial were reported previously by the MPN Hub. A post hoc analysis of this trial assessed the use of machine learning to improve the detection and quantification of marrow fibrosis in patients with MF. Quantitative assessment of fibrosis using Continuous Indexing of Fibrosis (CIF) was performed by automated analyzes using bone marrow trephine (BMT) samples from a subset of 50 patients. Results from this analysis were published in Hemasphere by Ryou et al.1
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This study demonstrates that quantitative analysis using CIF may provide an objective assessment of fibrosis severity and heterogeneity within BMT samples in patients with MF. Additionally, CIF allows an objective comparison between sequential samples from individual patients and accurate comparisons within trial cohorts. |
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There was a moderate correlation between CIF score and the conventional manual fibrosis grading for all samples (Spearman’s rho = 0.39); however, discrepancies were identified, with 38% of samples graded as MF-2 falling within the interquartile range of CIF distribution observed in MF-3, and 48% of MF-3 samples falling within the interquartile range observed in MF-2. |
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Importantly, several samples graded as MF-2 manually had average CIF scores similar to those graded as MF-0 or MF-1. This suggests the need for more standardized, objective measures in clinical trials to improve the accuracy of therapeutic evaluations. |
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While CIF score improvements did not directly correlate with secondary clinical outcomes, there was a trend toward association with best overall response, indicating potential utility as a surrogate marker for clinical efficacy in MF treatment trials. |
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The study underscores the potential of integrating advanced imaging analysis methods like CIF into trial protocols, together with conventional methods, to better capture fibrosis dynamics, which could influence treatment strategies for patients with MF in clinical practice. |
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