☆ 4.6 Review

Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review

DIAGNOSTICS (2023)

Related references

Note: Only part of the references are listed.

Article Radiology, Nuclear Medicine & Medical Imaging

Feasibility of artificial intelligence-supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein - a retrospective observational study

Philipp Fervers et al.

Summary: The study aimed to predict BM infiltration in MM patients using an automated assessment of the spine in DECT VNCa. A non-fatty portion of BM > 0.93% could anticipate osteolytic lesions and the clinical diagnosis of MM. The method also identified MM patients without osteolytic lesions on conventional CT.

EUROPEAN RADIOLOGY (2022)

Add to Collection

Review Medicine, General & Internal

Diagnosis and Management of Multiple Myeloma A Review

Andrew J. Cowan et al.

Summary: Multiple myeloma is a hematologic malignancy characterized by abnormal plasma cells in the bone marrow, often leading to anemia, bone disease, and kidney injury. Diagnosis involves measuring various biomarkers, while treatment options include chemotherapy, autologous hematopoietic stem cell transplantation, and maintenance therapy.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2022)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Semi-automated histogram analysis of normal bone marrow using 18F-FDG PET/CT: correlation with clinical indicators

Yoko Satoh et al.

Summary: A normal FDG uptake pattern was demonstrated by simplified FDG PET/CT bone marrow quantification.

BMC MEDICAL IMAGING (2022)

Add to Collection

Review Oncology

Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

Alessandro Allegra et al.

Summary: Multiple myeloma is a complex malignant neoplasm of plasma cells. The application of artificial intelligence, particularly machine learning and deep learning algorithms, has significant implications in the diagnosis and treatment of multiple myeloma.

CANCERS (2022)

Add to Collection

Review Radiology, Nuclear Medicine & Medical Imaging

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

Salvatore Gitto et al.

Summary: This study systematically reviewed radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The findings showed variations in approaches among studies, with some including feature reproducibility analysis, utilizing machine learning validation techniques for model development, and conducting clinical validation.

INSIGHTS INTO IMAGING (2021)

Add to Collection

Article Hematology

P-018: Automatic analysis of magnetic resonance imaging in multiple myeloma patients: deep-learning based pelvic bone marrow segmentation and radiomics analysis for prediction of plasma cell infiltration

Markus Wennmann et al.

Clinical Lymphoma Myeloma & Leukemia (2021)

Add to Collection

Article Hematology

P-012: Automatic bone marrow segmentation in whole-body magnetic resonance imaging: towards comprehensive, objective MRI-phenotypic bone marrow characterization in multiple myeloma

Markus Wennmann et al.

Clinical Lymphoma Myeloma & Leukemia (2021)

Add to Collection

Article Oncology

Differentiating Between Multiple Myeloma and Metastasis Subtypes of Lumbar Vertebra Lesions Using Machine Learning-Based Radiomics

Xing Xiong et al.

Summary: Machine learning classifiers based on conventional MRI sequences showed satisfactory performance in differentiating MM lesions from tumor metastasis, with the ANN classifier from T2WI images achieving the best performance. However, their value in distinguishing myeloma from different metastasis subtypes was moderate.

FRONTIERS IN ONCOLOGY (2021)

Add to Collection

Article Oncology

MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma

Yang Li et al.

Summary: The study developed and validated a radiomics nomogram for predicting overall survival in multiple myeloma patients. Results showed significant association between the radiomics signature and MM OS, with the predictive ability of the radiomics nomogram outperforming other models.

FRONTIERS IN ONCOLOGY (2021)

Add to Collection

Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review

Cindy Xue et al.

Summary: Radiomics research has been rapidly growing in recent years, with concerns on reliability raised. This systematic review focused on the use of the intraclass correlation coefficient (ICC) as a reliability metric in radiomics studies. The review found that while feature reliability to image segmentation is extensively studied, image acquisition introduces more variability than image segmentation, particularly in MRI studies.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021)

Add to Collection

Article Medicine, General & Internal

Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients Undergoing Autologous Transplantation: A Feasibility Study with CT Data

Daniela Schenone et al.

Summary: This study applied radiomics to analyze multiple myeloma patients' radiographic images, utilizing fuzzy clustering and Hough transform filtering for patient stratification, achieving higher sensitivity and critical success index. Additionally, focal features were selected via correlation, outperforming standard cytogenetic classification in terms of scores.

DIAGNOSTICS (2021)

Add to Collection

Review Medicine, General & Internal

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Matthew J. Page et al.

Summary: The PRISMA statement was designed to help systematic reviewers transparently report the purpose, methods, and findings of their reviews. The updated PRISMA 2020 statement includes new reporting guidance, a 27-item checklist, an abstract checklist, and revised flow diagrams for reviews.

BMJ-BRITISH MEDICAL JOURNAL (2021)

Add to Collection

Article Oncology

Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics

Jan C. Peeken et al.

Summary: Soft-tissue sarcomas are a rare cancer type with a high risk of disease recurrence. By comparing radiomics and semantic imaging features, it was found that radiomics outperformed in predicting patients at high risk for cancer-specific mortality. While semantic features were helpful in identifying high-risk patients, the radiomic approach showed overall improved ability in patient stratification.

CANCERS (2021)

Add to Collection

Review Radiology, Nuclear Medicine & Medical Imaging

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

Evi J. van Kempen et al.

Summary: The study provides an overview and meta-analysis of various machine learning algorithms for automated segmentation of gliomas, showing promising accuracy. However, high heterogeneity between studies and publication bias were observed as potential challenges.

EUROPEAN RADIOLOGY (2021)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma

Jianfang Liu et al.

Summary: The radiomics model based on spinal MRI is effective in predicting high-risk cytogenetic abnormalities in patients with multiple myeloma (MM), showing comparable performance with the combined model incorporating basic clinical characteristics. This MRI-based radiomics model may serve as a useful and independent tool for predicting HRCAs in MM patients.

RADIOLOGIA MEDICA (2021)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

A deep look into radiomics

Camilla Scapicchio et al.

Summary: This review provides a detailed look into radiomics, covering its basis, technical applications, challenges, and potential in precision medicine. It discusses techniques used in the radiomics workflow and highlights promising results achieved in various applications, with a focus on limitations and possible solutions for clinical implementation. An in-depth and comprehensive understanding of current methods and applications is crucial in realizing the potential power of radiomics in improving patient care, especially in cancer detection, diagnosis, prognosis, and treatment evaluation.

RADIOLOGIA MEDICA (2021)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics of Renal Masses: Systematic Review of Reproducibility and Validation Strategies

Burak Kocak et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2020)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection

Tiansong Xie et al.

EUROPEAN RADIOLOGY (2020)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Computed tomography-based skeletal segmentation for quantitative PET metrics of bone involvement in multiple myeloma

Maria E. S. Takahashi et al.

NUCLEAR MEDICINE COMMUNICATIONS (2020)

Add to Collection

Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics in medical imaging-how-to guide and critical reflection

Janita E. van Timmeren et al.

INSIGHTS INTO IMAGING (2020)

Add to Collection

Article Medical Informatics

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

Julia Amann et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2020)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Whole-Body Imaging of Multible Myeloma: Diagnostic Criteria

Alipio G. Ormond Filho et al.

RADIOGRAPHICS (2019)

Add to Collection

Review Hematology

Imaging in multiple myeloma: How? When?

Elena Zamagni et al.

BLOOD (2019)

Add to Collection

Review Oncology

The Critical Role of Imaging in the Management of Multiple Myeloma

Shahzad Raza et al.

CURRENT HEMATOLOGIC MALIGNANCY REPORTS (2017)

Add to Collection

Article Radiology, Nuclear Medicine & Medical Imaging

Automated Bone Subtraction Image Analysis Software Package for Improved and Faster CT Monitoring of Longitudinal Spine Involvement in Patients with Multiple Myeloma

Marius Horger et al.

ACADEMIC RADIOLOGY (2017)

Add to Collection

Article Hematology

Impact of CRAB symptoms in survival of patients with symptomatic myeloma in novel agent era

Aya Nakaya et al.

HEMATOLOGY REPORTS (2017)

Add to Collection

Article Biology

Fully automated classification of bone marrow infiltration in low-dose CT of patients with multiple myeloma based on probabilistic density model and supervised learning

Francisco Martinez-Martinez et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2016)

Add to Collection

Article Hematology

European Myeloma Network Guidelines for the Management of Multiple Myeloma-related Complications

Evangelos Terpos et al.

HAEMATOLOGICA (2015)

Add to Collection

Article Hematology

European Myeloma Network Guidelines for the Management of Multiple Myeloma-related Complications

Evangelos Terpos et al.

HAEMATOLOGICA (2015)

Add to Collection

Review Oncology

International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma

S. Vincent Rajkumar et al.

LANCET ONCOLOGY (2014)

Add to Collection

Article Orthopedics

Imaging of multiple myeloma: Current concepts

Thorsten Derlin et al.

WORLD JOURNAL OF ORTHOPEDICS (2014)

Add to Collection

Article Oncology

Myeloma bone disease: Pathophysiology and management

Rebecca Silbermann et al.

JOURNAL OF BONE ONCOLOGY (2013)

Add to Collection

Meeting Abstract Oncology

FULLY AUTOMATED MODEL POSITIONING FOR SHAPE-BASED BONE SEGMENTATION

A. Fraenzle et al.

RADIOTHERAPY AND ONCOLOGY (2011)

Add to Collection

Article Hematology

Molecular Imaging in Myeloma Precursor Disease

Esther Mena et al.

SEMINARS IN HEMATOLOGY (2011)

Add to Collection

© Peeref 2019-2024. All rights reserved.