Efficacy of Laser Treatment in Pilonidal Disease: A Single-Arm Meta-Analysis.

Objective: This study aimed to collate all published studies on laser therapy for pilonidal disease and demonstrate the safety and effectiveness of minimally invasive techniques. Methods: A comprehensive literature search, with no language limitations, was performed using PubMed, Embase, and Web of Science from inception to April 23, 2023. Two reviewers independently screened the literature according to the inclusion and exclusion criteria and evaluated the bias risk of included studies. Meta-analysis was performed using RevMan software (version 5.4). (PROSPERO Registration ID Number CRD42023420803). Results: The analysis included 1214 patients from 13 studies, who fulfilled the pre-defined inclusion criteria. With a median follow-up of 12 (range, 7.8-25) months, 1000 (84.4%) patients achieved healing after primary laser treatment. The mean complication and recurrence rates were 12.7% and 7.6%, respectively. Conclusions: Laser ablation for pilonidal sinus disease is a new minimally invasive technique with good treatment efficacy, low postoperative recovery, and shorter recovery periods following employment.

The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures.

PURPOSE: To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). MATERIALS AND METHODS: 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model. RESULTS: For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846. CONCLUSION: Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.