The reproducibility of positron emission tomography (PET) radiomics features is affected by several factors, such as scanning equipment, drug metabolism time and reconstruction algorithm. We aimed to explore the role of 3D local binary pattern (LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer.
We retrospectively analysed data from 177 patients with cervical squamous cell carcinoma. They underwent 18F-fluorodeoxyglucose (18F-FDG)whole-body PET/computed tomography (PET/CT), followed by pelvic 18F-FDG PET/magnetic resonance imaging (PET/MR). We selected reproducible and informative PET radiomics features using Lin's concordance correlation coefficient, least absolute shrinkage and selection operator algorithm, and established 4 models, PET/CT, PET/CT-fusion, PET/MR and PET/MR-fusion, using the logistic regression algorithm. We performed receiver operating characteristic (ROC) curve analysis to evaluate the models in the training data set (65 patients who underwent radical hysterectomy and pelvic lymph node dissection) and test data set (112 patients who received concurrent chemoradiotherapy or no treatment). The DeLong test was used for pairwise comparison of the ROC curves among the models.
The distribution of age, squamous cell carcinoma (SCC), International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different (P < 0.05). The LBP-transformed radiomics features (50/379) had higher reproducibility than the original radiomics features (9/107). Accuracy of each model in predicting PLNM was as follows: training data set: PET/CT = PET/CT-fusion = PET/MR-fusion (0.848) and test data set: PET/CT = PET/CT-fusion (0.985) > PET/MR = PET/MR-fusion (0.954). There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both data sets (P > 0.05).
The LBP-transformed radiomics features based on PET images could increase the accuracy and reproducibility of PET radiomics in predicting pelvic lymph node metastasis in cervical cancer to allow the model to be generalised for clinical use across multiple centres.
Segmentation of medical images is a crucial process in various image analysis applications. Automated segmentation methods excel in accuracy when compared to manual segmentation in the context of medical image analysis. One of the essential phases in the quantitative analysis of the brain is automated brain tissue segmentation using clinically obtained magnetic resonance imaging (MRI) data. It allows for precise quantitative examination of the brain, which aids in diagnosis, identification, and classification of disorders. Consequently, the efficacy of the segmentation approach is crucial to disease diagnosis and treatment planning.
This study presented a hybrid optimization method for segmenting brain tissue in clinical MRI scans using a fractional Henry horse herd gas optimization-based Shepard convolutional neural network (FrHHGO-based ShCNN). To segment the clinical brain MRI images into white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) tissues, the proposed framework was evaluated on the Lifespan Human Connectome Projects (HCP) database. The hybrid optimization algorithm, FrHHGO, integrates the fractional Henry gas optimization (FHGO) and horse herd optimization (HHO) algorithms. Training required 30 min, whereas testing and segmentation of brain tissues from an unseen image required an average of 12 s.
Compared to the results obtained with no refinements, the Skull stripping refinement showed significant improvement. As the method included a preprocessing stage, it was flexible enough to enhance image quality, allowing for better results even with low-resolution input. Maximum precision of 93.2%, recall of 91.5%, Dice score of 91.1%, and F1-score of 90.5% were achieved using the proposed FrHHGO-based ShCNN, which was superior to all other approaches.
The proposed method may outperform existing state-of-the-art methodologies in qualitative and quantitative measurements across a wide range of medical modalities. It might demonstrate its potential for real-life clinical application.
Bacterial vaginosis is a polymicrobial syndrome in which the homeostasis exerted by the Latobacillus species that protect the vaginal mucosa has been lost. This study explored the data balancing process with the intention of improving the quality of association rules. The article aimed to balance the unbalanced multiclass dataset to improve association rule creation.
A dataset with 201 observations and 58 variables was analyzed. A preconstructed dataset was used. The authors collected the data between August 2016 and October 2018 in Tabasco, Mexico. The study population comprised sexually active women ages 18 to 50 who underwent gynecological inspection at the infectious and metabolic diseases research laboratory at the Universidad Juarez Autonoma de Tabasco. To determine the best -value, the random-forest algorithm was used and the balancing was performed with the synthetic minority over-sampling technique (SMOTE), random over-sampling examples (ROSE), and adaptive syntetic sampling approach for imbalanced learning (ADASYN) algorithms. The Apriori algorithm created the rules and to select rules with statistical significance, the is.redundant(), is.significant(), and is.maximal() functions and quality metric Fisher’s exact tes were used. The biological validation was carried out by the expert (bacteriologist).
The ADASYN algorithm at the out of the bag (OOB) error was zero, this was the best -values. In the balancing process the ADASYN algorithm show best the performance. From the dataset balanced with ADASYN, the apriori algorithm created the association rules and the selection with the quality metric Fisher’s exact test, and the biological validation reported 13 rules. Gram - bacteria Atopobium vaginae, Gardnerella vaginalis, Megasphaera filotipo 1, Mycoplasma hominis and Ureaplasma parvum were detected by the apriori algorithm from the balanced dataset.
Balancing may improve the creation of association rules to efficiently model the bacteria that cause bacterial vaginosis.
Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new therapies. Machine learning (ML) models have shown satisfactory performance in short-term mortality prediction in patients with heart disease, whereas their utility in long-term predictions is limited. This study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term mortality.
This study used publicly available data from the Collaboration Center of Health Information Application at the Ministry of Health and Welfare, Taiwan, China. The collected data were from patients admitted to the cardiac care unit for acute myocardial infarction (AMI) between November 2003 and September 2004. We collected and analyzed mortality data up to December 2018. Medical records were used to gather demographic and clinical data, including age, gender, body mass index, percutaneous coronary intervention status, and comorbidities such as hypertension, dyslipidemia, ST-segment elevation myocardial infarction, and non-ST-segment elevation myocardial infarction. Using the data, collected from 139 patients with AMI, from medical and demographic records as well as two recently introduced biomarkers, brachial pre-ejection period (bPEP) and brachial ejection time (bET), we investigated the performance of advanced ensemble tree-based ML algorithms (random forest, AdaBoost, and XGBoost) to predict all-cause mortality within 14 years. A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression (LR) as the baseline method.
The developed ML models achieved significantly better performance compared to the baseline LR (C-Statistic, 0.80 for random forest, 0.79 for AdaBoost, and 0.78 for XGBoost, vs. 0.77 for LR) (PRF < 0.001, PAdaBoost < 0.001, and PXGBoost < 0.05). Adding bPEP and bET to our feature set significantly improved the performance of the algorithm, leading to an absolute increase in C-statistic of up to 0.03 (C-statistic, 0.83 for random forest, 0.82 for AdaBoost, and 0.80 for XGBoost, vs. 0.74 for LR) (PRF < 0.001, PAdaBoost < 0.001, PXGBoost < 0.05).
The study indicates that incorporating new biomarkers into advanced ML models may significantly improve long-term mortality prediction in patients with cardiovascular diseases. This advancement may enable better treatment prioritization for high-risk individuals.

