RESUMO
Introduction: Chronic osteomyelitis is a complex clinical condition that is associated with a high recurrence rate. Traditional surgical interventions often face challenges in achieving a balance between thorough debridement and managing resultant bone defects. Radiomics is an emerging technique that extracts quantitative features from medical images to reveal pathological information imperceptible to the naked eye. This study aims to investigate the potential of radiomics in optimizing osteomyelitis diagnosis and surgical treatment. Methods: Magnetic resonance imaging (MRI) scans of 93 suspected osteomyelitis patients were analyzed. Radiomics features were extracted from the original lesion region of interest (ROI) and an expanded ROI delineated by enlarging the original by 5 mm. Feature selection was performed and support vector machine (SVM) models were developed using the two ROI datasets. To assess the diagnostic efficacy of the established models, we conducted receiver operating characteristic (ROC) curve analysis, employing histopathological results as the reference standard. The model's performance was evaluated by calculating the area under the curve (AUC), sensitivity, specificity, and accuracy. Discrepancies in the ROC between the two models were evaluated using the DeLong method. All statistical analyses were carried out using Python, and a significance threshold of p < 0.05 was employed to determine statistical significance. Results and Discussion: A total of 1,037 radiomics features were extracted from each ROI. The expanded ROI model achieved significantly higher accuracy (0.894 vs. 0.821), sensitivity (0.947 vs. 0.857), specificity (0.842 vs. 0.785) and AUC (0.920 vs. 0.859) than the original ROI model. Key discriminative features included shape metrics and wavelet-filtered texture features. Radiomics analysis of MRI exhibits promising clinical translational potential in enhancing the diagnosis of chronic osteomyelitis by accurately delineating lesions and identifying surgical margins. The inclusion of an expanded ROI that encompasses perilesional tissue significantly improves diagnostic performance compared to solely focusing on the lesions. This study provides clinicians with a more precise and effective tool for diagnosis and surgical decision-making, ultimately leading to improved outcomes in this patient population.
RESUMO
Purpose: The performance and clinical accuracy of combined SDC2/NDRG4 methylation were evaluated in diagnosing colorectal cancer (CRC) and advanced adenoma. Methods: A total of 2333 participants were enrolled to assess the sensitivity and specificity of biomarkers in diagnosing CRC in a multicenter clinical trial through feces DNA methylation tests. Results: SDC2/NDRG4 methylation showed excellent performance for CRC detection in biomarker research and the real world. Its sensitivity for detecting CRC, early CRC and advanced adenoma were 92.06%, 91.45% and 62.61%, respectively. Its specificity was 94.29%, with a total coincidence rate of 88.28%. When interference samples were included, the specificity was still good (82.61%). Therefore, the SDC2/NDRG4 methylation test showed excellent performance in detecting CRC and advanced adenoma under clinical application.
Colorectal cancer (CRC) is one of the most malignant tumors of the digestive system and second only to breast cancer and lung cancer in terms of global incidence. Early CRCs are challenging to determine given their atypical nature. In contrast, late CRC symptoms are affected by the type, location and range of the lesion and complications. Therefore, CRC patients are generally diagnosed late, present with a high degree of malignancy, and have poor prognosis and 5-year survival rates. The current study therefore evaluated whether SDC2 and NDRG4 methylation could be used for diagnosis CRCs at an early stage and whether it has the potential to detect asymptomatic patients with adenomas. The findings presented herein will certainly help support the early diagnosis of CRC and precancerous lesions in clinical practice.