AIMC Topic: Multiple Myeloma

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Radiomics and Artificial Intelligence Landscape for [F]FDG PET/CT in Multiple Myeloma.

Seminars in nuclear medicine
[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
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
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 ...

Construction of the prediction model for multiple myeloma based on machine learning.

International journal of laboratory hematology
INTRODUCTION: The global burden of multiple myeloma (MM) is increasing every year. Here, we have developed machine learning models to provide a reference for the early detection of MM.

Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

European journal of nuclear medicine and molecular imaging
PURPOSE: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group...

3D CNN-based Deep Learning Model-based Explanatory Prognostication in Patients  with Multiple Myeloma using Whole-body MRI.

Journal of medical systems
Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted ...

Artificial Intelligence Individualized Risk Classifier in Multiple Myeloma.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology

Detection of circulating plasma cells in peripheral blood using deep learning-based morphological analysis.

Cancer
BACKGROUND: The presence of circulating plasma cells (CPCs) is an important laboratory indicator for the diagnosis, staging, risk stratification, and progression monitoring of multiple myeloma (MM). Early detection of CPCs in the peripheral blood (PB...

Dose reduction and toxicity of lenalidomide-dexamethasone in multiple myeloma: A machine-learning prediction model.

Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
PURPOSE: Lenalidomide remains an effective drug for multiple myeloma, but it is often associated with adverse events and requires dose adjustments. The objective of this study was to propose a model for predicting whether a patient would require dose...