Purpose: To assess the value of histogram analysis, using diffusion kurtosis imaging (DKI), in differentiating glioblastoma multiforme (GBM) from single brain metastasis (SBM) and to compare the diagnostic efficiency of different region of interest (ROI) placements.
Method: Sixty-seven patients with histologically confirmed GBM (n = 35) and SBM (n = 32) were recruited. Two ROIs-the contrast-enhanced area and whole-tumor area-were delineated across all slices. Eleven histogram parameters of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) from both ROIs were calculated. All histogram parameter values were compared between GBM and SBM, using the Mann-Whitney U test. The accuracies of different histogram parameters were compared using the McNemar test. Receiver operating characteristic (ROC) analyses were conducted to assess the diagnostic performance.
Results: In the contrast-enhanced area, FA, FA, FA, FA, FA, FA, FA, MD, MD, and MK were significantly higher for GBM than for SBM. FA was significantly lower for GBM than for SBM. FA (0.815) had the highest area under the curve (AUC). In the whole-tumor area, FA, FA, FA, FA, FA, FA, FA, FA, MD, MD, and MK were significantly higher for GBM than for SBM. FA (0.805) had the highest AUC. The accuracy of FA in the contrast-enhanced area was significantly higher than that of the FA in the whole-tumor area.
Conclusions: GBM and SBM can be differentiated using the DKI-based histogram analysis. Placing the ROI on the contrast-enhanced area results in better discrimination.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ejrad.2021.110104 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Texture Feature Differentiation of Glioblastoma and Solitary Brain Metastases Based on Tumor and Tumor-brain Interface.
Acad Radiol
January 2025
Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China (Y.C., H.L., J.S., R.P., Y.Z., B.S.). Electronic address:
Article Synopsis
- This study examines the importance of texture features from both the entire tumor area and the adjacent tumor-to-brain interface in distinguishing between glioblastomas and metastatic tumors.
- Researchers analyzed imaging data from 97 patients, applying machine learning techniques to develop diagnostic models that enhance the identification of tumor types based on these texture features.
- The models demonstrated varying performance, with the best model achieving an accuracy of 0.857, highlighting the potential of this approach for more accurate tumor characterization.
Differentiation of glioblastoma from solitary brain metastasis using deep ensembles: Empirical estimation of uncertainty for clinical reliability.
Comput Methods Programs Biomed
September 2024
Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
Article Synopsis
- Researchers developed a deep learning model to differentiate between glioblastoma and solitary brain metastasis using MRI, focusing on predictive uncertainty and interpretability.
- The model was trained on 469 patients and validated with another 143, achieving a high area under the curve (AUC) of 0.83 in the external test set, with better performance in low-uncertainty cases.
- The model showed strong reliability and robustness, indicating its potential for clinical use, particularly in accurately classifying uncertain cases.
Comparison of Steady-State Bone Marrow and GCSF-Primed Stem Cell Sources Used in Allogeneic Hematopoietic Stem Cell Transplantation.
J Coll Physicians Surg Pak
March 2024
Department of Clinical Haematology, Armed Forces Institute of Bone Marrow Transplant Centre, Rawalpindi, Pakistan.
Objective: To compare donor graft characteristics and clinical outcomes in recipients of allogeneic heamatopoietic stem cell transplantation (HSCT) using GCSF primed bone marrow (GBM) and steady-state bone marrow (SBM) as stem cell sources.
Study Design: Observational study. Place and Duration of the Study: Department of Clinical Haematology, Armed Forces Bone Marrow Transplant Centre, Rawalpindi, Pakistan, from August 2018 to October 2020.
Aided Diagnosis Model Based on Deep Learning for Glioblastoma, Solitary Brain Metastases, and Primary Central Nervous System Lymphoma with Multi-Modal MRI.
Biology (Basel)
February 2024
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
(1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.
View Article and Find Full Text PDFRadiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis.
BMC Cancer
December 2023
Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China.
Background: We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM).
Methods: We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!