This study explored a machine learning approach using 18F-FDG PET/CT as a non-invasive alternative to biopsy, incorporating tumor-to-liver ratio (TLR) PET radiomics, and performed survival analysis to improve lymphoma management. In this cohort study, baseline 18F-FDG PET/CT scans of newly diagnosed, histologically confirmed lymphoma patients were analyzed. Lesions were segmented using 3D Slicer, and radiomic features were extracted and normalized by tumor-to-liver ratios. Patient-level features were used to train three machine learning models (XGBoost, AdaBoost, Logistic Regression) using nested cross-validation with SMOTE for class balancing. A model based on SUVmax metrics served as baseline. Radiomic features were also evaluated for correlation with 3- and 5-year survival using the Mann-Whitney U test. A total of 156 lymphoma patients were analyzed, with 2,076 lesions segmented and 200 radiomic features extracted. For subtype classification, AdaBoost achieved the highest AUC for Diffuse Large B-cell (DLBCL) (0.863, accuracy 0.742), while XGBoost performed best for High-Grade Non-Hodgkin lymphoma (NHL) (AUC 0.825, accuracy 0.735) and Nodular Sclerosis Hodgkin Lymphoma (NS-HL) (AUC 0.827, accuracy 0.832). Logistic regression showed the best results for Classical Hodgkin Lymphoma (C-HL) (AUC 0.849, accuracy 0.775). The SUVmax-based model (LR-SUV_MAX) consistently underperformed (AUCs: C-HL 0.630, High-Grade NHL 0.700, NS-HL 0.638, DLBCL 0.664), with all differences being statistically significant (p < 0.001). Radiomic and clinical features including SUV-GLSZM small area emphasis (p = 0.0019), age (p = 0.0002), and spleen involvement (p = 0.0014) were significantly associated with 3- and 5-year overall survival in 110 and 74 patients, respectively. Radiomic features combined with machine learning significantly improve lymphoma subtype classification over SUVmax alone and show potential for predicting patient survival.
The rapid proliferation of the Internet of Things (IoT) across diverse domains has intensified cybersecurity concerns, particularly within lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT), whose publish–subscribe architecture makes it a frequent target for cyberattacks. Motivated by the growing limitations of conventional intrusion detection systems (IDSs) in handling dynamic IoT environments, this study develops an efficient model that enhances detection accuracy while minimizing inference time. A hybrid intrusion detection framework that integrates the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Sailfish Optimization Algorithm (SOA) is proposed to achieve this objective. The proposed system is validated using uni-flow and bi-flow versions of the MQTT-IoT-IDS2020 dataset after dimensionality reduction via Principal Component Analysis (PCA). Experimental results demonstrate that the ANFIS–SOA model outperforms traditional classifiers, including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), and Naïve Bayes Classifier (NBC), achieving an accuracy of 99.7
Authenticated key exchange (AKE) protocols underpin secure bidirectional communications in smart grid, where real-time flows between neighborhood service providers (NSPs) and resource-constrained smart metering devices (SMDs) demand stringent security and efficiency. Recently, Cheng et al. have proposed an AKE protocol for smart grid intended to be leakage-resilient. Without undermining the noteworthy contributions of their work, this paper demonstrates that their proposed protocol is vulnerable to offline password-guessing and key compromise impersonation (KCI) attacks, and does not offer perfect forward secrecy (PFS). To address these limitations, we propose a decentralized ultra-lightweight leakage-resilient AKE protocol between NSPs and SMDs that not only mitigates the security shortcomings of prior top-related works but also imposes minimal overhead on resource-constrained SMDs. The protocol leverages the public Solana blockchain to enhance transparency and enable simple key revocation. Furthermore, it is ultra-lightweight, as the SMD only performs hashing, symmetric encryption/decryption, and physical unclonable function operations. Our security analyses indicate the proposed AKE protocol supports conditional anonymity, PFS, and resists advanced attacks such as the KCI attack. Performance and feature analyses also demonstrate improvements of 24% and 98% in communication and computational overheads, while offering distinct features.
This systematic review and meta-analysis evaluated the performance and methodological quality of deep learning models for automated segmentation of Diffuse Large B-Cell Lymphoma (DLBCL) on PET/CT imaging. A comprehensive literature search identified 15 eligible studies that were published up to July 2025. Of these, 11 studies were included in the quantitative synthesis, while 4 were assessed qualitatively. Using a random-effects model, the pooled mean DSC was 0.809 (95% CI: 0.791–0.827), indicating strong overall segmentation performance. The reported DSC values across the individual studies ranged from 0.65 to 0.886. Single-center studies generally showed slightly higher median DSC values (≈0.82) than multi-center studies (≈0.78), although pooled subgroup analyses revealed comparable averages (0.77 vs. 0.73). Methodological quality, assessed using the QUADAS-2 tool, showed that most studies (approximately 67–73%) were at low risk of bias, with the remainder classified as moderate or unclear. Despite the variability in algorithms, study designs, and datasets, DL-based methods have consistently achieved reliable segmentation accuracy. Overall, DL models demonstrated promising potential for automated DLBCL segmentation in PET/CT imaging. Nevertheless, future studies should focus on larger and more diverse cohorts, improved reporting standards, and transparent handling of methodological limitations to enhance generalizability and clinical applicability.
Introduction: It is thus essential to describe dynamic changes in brain signals, especially in the Electroencephalogram (EEG) data, to analyze nonlinear and chaotic patterns, particularly in epilepsy. This work presents an approach that seeks to incorporate recurrence plots with textural features in epilepsy seizure detection. This method provides a detailed picture of brain activity and can be helpful for clinical neuroscience. Materials and methods: In this study, Gray-Level Co-occurrence Matrix (GLCM) texture features were computed from recurrence plots of EEG signals to characterize neural dynamics. GLCM measures spatial relationships in the data, providing detailed insights into temporal and spatial patterns of brain activity. The method was initially validated on chaotic systems, demonstrating its ability to capture nonlinear behaviors. It was then applied to EEG data to detect seizures, highlighting its potential in clinical settings. Results: The proposed framework outperformed traditional Recurrence Quantification Analysis (RQA) and other methods in detecting epileptic seizures. The GLCM-enhanced recurrence plots provided a more accurate and sensitive representation of brain dynamics, allowing for earlier and more reliable seizure detection. This method shows strong potential for clinical applications, enhancing the ability to detect seizures early. On the Bonn EEG corpus, the proposed GRP-GLCM + SVM pipeline achieved 98.6 % (Case 1: AB/CD/E), 99.6 % (Case 2: ABCD/E), and 100 % (Case 3: D/E) Accuracy under nested cross-validation. Precision, Recall, and F1 were >= 0.98 in Cases 1-2 and 1.00 in Case 3 (zero FP/FN). Compared with an RQA baseline (76.8 %, 95.6 %, 91.0 %), these results reflect + 21.8, +4.0, and + 9.0 percentage-point gains while remaining competitive with recent CNN-based approaches and preserving interpretability. Conclusion: This study demonstrates that textural analysis of recurrence plots, particularly using GLCM features, provides a robust and efficient tool for epileptic seizure detection. By capturing subtle changes in brain activity, the framework offers a promising approach for improving early detection and intervention in clinical neuroscience.
