AIMC Topic: Multiple Myeloma

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The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning.

BMC cancer
BACKGROUND: In multiple myeloma, progression within 24 months (POD24) is a strong adverse prognostic factor. However, its impact on overall survival (OS) remains underexplored through machine learning.

Machine learning reveals distinct T-cell receptor clusters in plasma cell dyscrasias compared to healthy controls.

PloS one
T-cell receptor (TCR) repertoire diversity has been implicated in the progression and prognosis of multiple myeloma (MM). This study aimed to evaluate the association between T-cell clonality, immune response, and clinical outcomes in patients with p...

Construction of a machine learning-based screening model for IgD myeloma.

Clinica chimica acta; international journal of clinical chemistry
OBJECTIVE: Immunoglobulin D (IgD) myeloma is a rare subtype of multiple myeloma (MM), comprising approximately 1 %-2 % of all MM cases. Owing to the diminished levels of IgD in serum, IgD MM manifests as subtle M protein spikes in routine serum elect...

Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.

Scientific reports
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
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
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
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
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
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
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...