Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.
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January 2025
Quantitative immunoprecipitation mass spectrometry (QIP-MS) allows the identification of the M-protein in patients with multiple myeloma (MM) otherwise in complete response, and could be considered suitable for measurable residual disease (MRD) evaluation in peripheral blood. In the context of the GEM2012MENOS65 and GEM2014MAIN trials, we compared the performance of QIP-MS in serum with next-generation flow (NGF) cytometry in bone marrow to assess MRD in paired samples obtained postinduction, transplant, consolidation and after 24 cycles of maintenance. At each time point, both NGF and QIP-MS were able to segregate 2 groups of patients with significantly different progression-free survival; when the evolution of the results obtained with either method was considered, maintaining or converting to MRD negativity was associated with longer survival, significantly better when compared with sustaining or converting to MRD positivity.
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