MRI and Clinical Risk Indicators for Osteomyelitis

Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era

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.

Value of Diagnostic Tools in the Diagnosis of Osteomyelitis: Pilot Study to Establish an Osteomyelitis Score

Osteomyelitis (OM) remains one of the most feared complications in bone surgery and trauma. Its diagnosis remains a major challenge due to lack of guidelines. The aim of this study was to prospectively analyze the value of the most common and available diagnostic tools and to establish an OM score to derive treatment recommendations. All patients with suspected OM were included in a prospective pilot study. All patients underwent blood sampling for C-reactive protein and white blood cell count analysis. Magnetic resonance imaging (MRI), and microbiologic and histopathologic samples, were taken from representative sites of initial debridement. All patients were treated according to their OM test results and followed for at least one year. Subsequently, the value of individual or combined diagnostic tools was analyzed in patients with confirmed OM and in patients in whom OM was ruled out. Based on these findings, an OM score was developed that included MRI, microbiology, and histopathology. The score identified all control patients and all but one OM patient, resulting in a correct diagnosis of 93.3%, which was validated in a second independent larger cohort. This was the first study to analyze the value of the most commonly used tools to diagnose OM. The proposed OM score provides a simple scoring system to safely interpret test results with high accuracy.

Pathogen-specific molecular imaging and molecular testing methods in the prognosis of the complicated course of diabetic foot syndrome, the risk of amputation, and patient survival

The aim of this review was to provide extended information on current trends in the diagnosis of complicated diabetic foot syndrome (DFS), the most frequent and severe complication of diabetes mellitus, including hightech medical imaging methods and instrumental and laboratory predictors of the complicated course and risk of amputation in DFS.

The article provides an analytical review of modern publications over the past 5 years on diagnosis and therapy. Pilot data on the use of high-tech medical imaging methods, assessment of skin microbiota and ulcers in DFS, molecular testing methods in terms of predicting the amputation risk and survival of patients with DFS, as well as the effectiveness of biosensing systems have been systematized, summarized, and subjected to analytical evaluation.

The review provides an expert assessment of the capabilities of pathogen-specific molecular imaging using modern positron emission tomography (PET), single-photon emission computed tomography (SPECT), and highenergy radionuclides in bacterial infection to understand its pathogenesis, minimize diagnostic problems, improve antimicrobial treatment, and address fundamental and applied aspects of DFS. Literature data on the assessment of foot perfusion in diabetic patients with varying degrees of limb ischemia by hybrid technologies (SPECT / CT and PET / CT) and new modalities of magnetic resonance imaging (MRI) are also systematized, which contributes to new understanding of the response to revascularization, surgical shunting, and stimulation of angiogenesis within ischemic tissue, as well as potentially to healing of foot ulcers.

The review is aimed at substantiating a multidisciplinary approach in DFS, selection, development, and implementation of innovative strategies for diagnostic modalities to identify diabetic foot pathologies, and choice of an adequate method for treating and monitoring the results of therapy in the context of personalized medicine.