AIMC Topic: Multiple Myeloma
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Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.
Radiology
Sep 6, 2022
Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatia...
Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.
Medical physics
Aug 28, 2022
BACKGROUND: Dual-energy CT with virtual noncalcium (VNCa) images allows the evaluation of focal intramedullary bone marrow involvement in patients with multiple myeloma. However, current commercial VNCa techniques suffer from excessive image noise an...
A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT.
Skeletal radiology
Aug 18, 2022
BACKGROUND: Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional dee...
Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.
BioMed research international
Jul 27, 2022
Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatment...
Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry.
British journal of haematology
Nov 3, 2021
Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to cla...
Deep Learning-Based CT Imaging in Diagnosing Myeloma and Its Prognosis Evaluation.
Journal of healthcare engineering
Sep 13, 2021
Imaging examination plays an important role in the early diagnosis of myeloma. The study focused on the segmentation effects of deep learning-based models on CT images for myeloma, and the influence of different chemotherapy treatments on the prognos...
Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.
PloS one
Jul 28, 2021
Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in c...
Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data.
Leukemia
May 18, 2021
Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrati...
A CNN-based unified framework utilizing projection loss in unison with label noise handling for multiple Myeloma cancer diagnosis.
Medical image analysis
May 13, 2021
Multiple Myeloma (MM) is a malignancy of plasma cells. Similar to other forms of cancer, it demands prompt diagnosis for reducing the risk of mortality. The conventional diagnostic tools are resource-intense and hence, these solutions are not easily ...
The Prevalence of Euthyroid Hypertriiodothyroninemia in Newly Diagnosed Multiple Myeloma and its Clinical Characteristics.
Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists
Dec 13, 2020
OBJECTIVE: To evaluate the prevalence of euthyroid hypertriiodothyroninemia and/or hyperthyroxinemia and its clinical characteristics in multiple myeloma (MM) patients.
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