Shoeprints performs a vital role in forensic investigations. It has been an advanced research issue in forensic science. The main purpose of shoeprint image retrieval is to acquire a ranking list of shoeprint images in a database, according to their feature similarities to the query image. In this way, a shoeprint can not only be used as an exhibit for bringing criminal charges but also to provide a clue to a case. The goal of this work is to present an overview of the existing works conducted in shoeprint image retrieval. We detail the different phases of the shoeprint retrieval task and present a summary of the state-of-the-art methods. We analyzed the difficulties and problems in this field and discussed future work directions. This review may help neophytes become involved in research easily and quickly.

This study aimed to develop and validate a positron emission tomography/computed tomography (PET/CT)-based nomogram for individualized prediction of 3-year progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC). A total of 128 patients with NPC who underwent pretreatment PET/CT imaging were retrospectively enrolled. Radiomic features were extracted from PET and CT images, and clinical variables were collected. Feature selection was performed using univariate Cox analysis, multivariable Cox analysis, and the least absolute shrinkage and selection operator regression to identify independent predictors. A nomogram was constructed by integrating CT-Radscore, PET-Radscore, and key clinical variables. Model performance in predicting 3-year PFS was evaluated using the concordance index, time-dependent area under the receiver operating characteristic curve, Kaplan–Meier survival curves, calibration curves, and decision curve analysis in both training and validation cohorts. The nomogram incorporating CT-Radscore, PET-Radscore, and lactate dehydrogenase demonstrated robust predictive ability for PFS in NPC. The area under the receiver operating characteristic curve for predicting 3-year PFS was 0.813 in the training cohort and 0.739 in the validation cohort. The corresponding concordance index values were 0.705 and 0.635, respectively. Calibration plots and decision curve analysis confirmed the nomogram’s reliability and clinical utility. A PET/CT-based radiomics nomogram (CT-Radscore, PET-Radscore, and lactate dehydrogenase) achieved robust prediction of 3-year PFS and enhanced prognostic stratification in NPC. External validation in larger multi-center cohorts is needed due to the single-center retrospective design and moderate sample size.

Objectives: Gastric tumors are one of the most common digestive system diseases, and their accurate diagnosis largely depends on the expertise of medical professionals in interpreting imaging data. As medical imaging technology has advanced, the volume of imaging data for gastric tumors has rapidly increased. This wealth of data provides valuable diagnostic information but also poses challenges for experts in diagnosis and analysis. To address these challenges, a more accurate and efficient diagnostic model is urgently needed. Methods: In this study, we propose a gastric tumor classification model that combines deep learning and radiomic features to differentiate gastrointestinal stromal tumors from other gastric tumor types. This retrospective study collected 4,806 endoscopic ultrasound images from 219 patients. Deep learning and radiomic features were extracted, and feature selection was performed using t-tests and the LASSO method. Additionally, clinical features were recorded. Single-parameter models were constructed based on these three categories of features. Following this, a gastrointestinal tumor classification model was developed by integrating the predictions from the single-parameter models. Results: The model achieved an area under the receiver operating characteristic curve of 0.95, with sensitivities, specificities, positive predictive values, negative predictive values, and accuracies of 100.00%, 82.86%, 60.00%, 100.00%, and 86.36%, respectively. Conclusions: The experimental results indicate that the gastric tumor classification model has demonstrated strong performance in classifying gastric tumors using endoscopic ultrasound images, offering valuable diagnostic support for clinicians.