Objective: To investigate the diagnostic value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) whole-lesion histogram parameters in differentiating benign and malignant solitary pulmonary lesions (SPLs).
Materials And Methods: Patients with SPLs detected by chest CT examination and with further routine MRI, DKI and IVIM-DWI functional sequence scanning data were recruited. According to the pathological results, SPLs were divided into a benign group and a malignant group. Independent samples t tests (normal distribution) or Mann‒Whitney U tests (nonnormal distribution) were used to compare the differences in DKI (Dk, K), IVIM (D, D*, f) and ADC whole-lesion histogram parameters between the benign and malignant SPL groups. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the histogram parameters and determine the optimal threshold. The area under the curve (AUC) of each histogram parameter was compared by the DeLong method. Spearman rank correlation was used to analyze the correlation between histogram parameters and malignant SPLs.
Results: Most of the histogram parameters for diffusion-related values (Dk, D, ADC) of malignant SPLs were significantly lower than those of benign SPLs, while most of the histogram parameters for the K value of malignant SPLs were significantly higher than those of benign SPLs. DKI (Dk, K), IVIM (D) and ADC were effective in differentiating benign and malignant SPLs and combined with multiple parameters of the whole-lesion histogram for the D value, had the highest diagnostic efficiency, with an AUC of 0.967, a sensitivity of 90.00% and a specificity of 94.03%. Most of the histogram parameters for the Dk, D and ADC values were negatively correlated with malignant SPLs, while most of the histogram parameters for the K value were positively correlated with malignant SPLs.
Conclusions: DKI (Dk, K) and IVIM (D) whole-lesion histogram parameters can noninvasively distinguish benign and malignant SPLs, and the diagnostic performance is better than that of DWI. Moreover, they can provide additional information on SPL microstructure, which has important significance for guiding clinical individualized precision diagnosis and treatment and has potential clinical application value.
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http://dx.doi.org/10.3389/fonc.2022.1075072 | DOI Listing |
Hypertens Res
January 2025
Division of Internal Medicine, Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy.
Blood pressure (BP) variability (BPV) is an independent predictor of cardiovascular (CV) events. The role of BPV in defining risk of cancer therapy-related cardiovascular toxicity (CTR-CVT) is currently unknown. The aims of this study were: (i) to evaluate BPV in a population of patients with Multiple Myeloma, undergoing proteasome inhibitors therapy; (ii) to assess the predictive value of BPV for CTR-CVT; (iii) to analyze clusters of subjects based on BPV.
View Article and Find Full Text PDFImmun Inflamm Dis
January 2025
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Objective: This study aimed to evaluate the activity of extraocular muscles (EOMs) in patients with thyroid-associated ophthalmopathy (TAO) using turbo spin echo imaging. By analyzing tissue heterogeneity, apparent diffusion coefficient (ADC) histogram analysis offers enhanced insights into edema within the EOMs.
Methods: Eighty-eight patients with TAO were retrospectively evaluated and allocated into active (n = 24, clinical activity score [CAS] ≥ 3) and inactive (n = 64, CAS < 3) groups.
Adv Appl Bioinform Chem
January 2025
Department of Information Technology, Mutah University, Al-Karak, Jordan.
Purpose: The incidence of cancer, which is a serious public health concern, is increasing. A predictive analysis driven by machine learning was integrated with haematology parameters to create a method for the simultaneous diagnosis of several malignancies at different stages.
Patients And Methods: We analysed a newly collected dataset from various hospitals in Jordan comprising 19,537 laboratory reports (6,280 cancer and 13,257 noncancer cases).
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.
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.
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