Rationale And Objectives: Preoperative prediction of meningioma consistency is of great clinical value for risk stratification and surgical approach selection. However, to date, objective quantitative criteria for predicting meningioma consistency have not been developed. This study aimed to investigate the predictive value of magnetic resonance imaging (MRI) T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) histogram parameters for meningioma consistency.
Materials And Methods: We retrospectively analyzed the clinical, preoperative MRI, and pathological data of 103 patients with histopathologically confirmed meningiomas. Histogram parameters (mean, variance, skewness, kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%) were calculated automatically on the whole tumor using MaZda software. Chi-square test, Mann-Whitney's U test, or independent samples t-test was used to compare clinical, conventional MRI features, and histogram parameters between soft and hard meningiomas. Receiver operating characteristic curve and binary logistic regression analysis were employed to assess the predictive performance of T2WI and ADC histogram parameters.
Results: Tumor enhancement was the only conventional MRI feature that was statistically different between soft and hard meningiomas. ADC, ADC, ADC, and ADC among ADC histogram parameters, and T2, T2, T2, T2, T2, and T2 among T2WI histogram parameters showed statistically significant differences between soft and hard meningiomas (all P < 0.05). We found that all combined variables (combined) had the best accuracy in predicting meningioma consistency, with area under the curve, sensitivity, specificity, accuracy, positive predictive, and negative predictive values of 0.873 (0.804-0.941), 88.89%, 67.50%, 80.58%, 81.20%, and 79.40%, respectively. Among them, combined is the most beneficial for predicting meningioma consistency.
Conclusion: Combined demonstrated better predictive performance for meningioma consistency than combined. T2WI and ADC histogram parameters may be imaging markers for predicting meningioma consistency.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.acra.2023.12.014 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhanced Pneumonia Detection in Chest X-Rays Using Hybrid Convolutional and Vision Transformer Networks.
Curr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
A combinatory approach of non-chain ring and henon map for image encryption application.
Sci Rep
January 2025
Department of Mathematics, College of Science, King Khalid, University, Abha, 61413, Saudi Arabia.
Algebraic structures play a vital role in securing important data. These structures are utilized to construct the non-linear components of block ciphers. Since constructing non-linear components through algebraic structures is crucial for the confusion aspects of encryption schemes, relying solely on these structures can result in limited key spaces.
View Article and Find Full Text PDFProton radiotherapy significantly reduces pneumonia in oesophageal cancer patients.
Int J Radiat Oncol Biol Phys
January 2025
Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address:
Background: Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection is the current standard of care for oesophageal cancer (EC) patients. This treatment is associated with a variety of complications, with pneumonia being the most common. We hypothesize that proton radiotherapy (PRT) can significantly reduce the incidence of pneumonia compared to photon radiotherapy (PhRT).
View Article and Find Full Text PDFArticle Synopsis
- Developed a statistical model to predict severe acute oral mucositis in head and neck cancer patients receiving carbon-ion radiation therapy.
- Used a combination of clinical data, dose-volume statistics, and advanced imaging features for better prediction accuracy.
- The model showed high accuracy rates (87.1% training, 90.7% test) and can potentially aid in treatment planning and identifying patients at high risk for this complication.
A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.
Sci Rep
January 2025
Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, 00-908, Poland.
Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).
View Article and Find Full Text PDFWant 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!