AIMC Topic: Multiple Myeloma
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Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.
Scientific reports
Jul 2, 2025
PANoptosis is closely associated with tumorigenesis and therapeutic response, yet its role in multiple myeloma (MM) remains unclear. This study analyzed bulk transcriptomic and clinical data from the TCGA and GEO databases to identify seven PANoptosi...
MedScale-Former: Self-guided multiscale transformer for medical image segmentation.
Medical image analysis
Apr 4, 2025
Accurate medical image segmentation is crucial for enabling automated clinical decision procedures. However, existing supervised deep learning methods for medical image segmentation face significant challenges due to their reliance on extensive label...
Individualized dynamic risk assessment and treatment selection for multiple myeloma.
British journal of cancer
Apr 1, 2025
BACKGROUND: Individualized treatment decisions for multiple myeloma (MM) patients require accurate risk stratification that accounts for patient-specific consequences of cytogenetic abnormalities on disease progression.
Metabolomics and machine learning approaches for diagnostic biomarkers screening in systemic light chain amyloidosis.
Annals of hematology
Mar 13, 2025
Delayed diagnosis of systemic light chain (AL) amyloidosis is common and associated with worse survival and early mortality. Current diagnosis still relies on invasive tissue biopsies, highlighting the need for sensitive, noninvasive biomarkers for e...
Bone-wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes.
Journal of applied clinical medical physics
Mar 11, 2025
BACKGROUND: Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert...
Inhibition of CDC27 O-GlcNAcylation coordinates the antitumor efficacy in multiple myeloma through the autophagy-lysosome pathway.
Acta pharmacologica Sinica
Feb 21, 2025
Multiple myeloma (MM) is a prevalent hematologic malignancy characterized by abnormal proliferation of cloned plasma cells. Given the aggressive nature and drug resistance of MM cells, identification of novel genes could provide valuable insights for...
Ultrasensitive Detection of Circulating Plasma Cells Using Surface-Enhanced Raman Spectroscopy and Machine Learning for Multiple Myeloma Monitoring.
Analytical chemistry
Jan 24, 2025
Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advancements, there remains a critical need for reliable, noninvasive methods to monitor multiple myeloma...
Radiomics and Artificial Intelligence Landscape for [F]FDG PET/CT in Multiple Myeloma.
Seminars in nuclear medicine
Dec 13, 2024
[F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treatment response in this disease. On the other hand, due to the heterogeneous and sometimes complex pat...
HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data.
Nature communications
Nov 2, 2024
In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in reality only a small fraction of them may actually be perturbed. Here, using simulations, we demonstr...
Measurable residual disease (MRD) dynamics in multiple myeloma and the influence of clonal diversity analyzed by artificial intelligence.
Blood cancer journal
Aug 7, 2024
Minimal residual disease (MRD) assessment is a known surrogate marker for survival in multiple myeloma (MM). Here, we present a single institution's experience assessing MRD by NGS of Ig genes and the long-term impact of depth of response as well as ...
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