Diagnostics最新文献

Background/Objectives: Hereditary transthyretin-mediated amyloidosis (ATTRv) is a rare disease characterized by the deposition of amyloid in the heart and peripheral nerves, particularly affecting small fibers. This study aims to evaluate autonomic cardiac involvement in ATTRv. Methods: Twelve patients with ATTRv and twelve sex- and age-matched healthy subjects underwent ¹²³I-mIBG scintigraphy to evaluate the early and late heart-to-mediastinum ratio (eH/M and lH/M), ^99mTc-HDP bone scan scintigraphy, and neurophysiological assessments. Data were analyzed in relation to functional cardiac and neurologic scales (NYHA and FAP scales). Results: Patients with ATTRv exhibited significant cardiac denervation, as demonstrated by the reduction in early and late H/M ratios compared to the control group (eH/M: 1.48 ± 0.08 vs. 1.89 ± 0.05, p < 0.001; lH/M: 1.39 ± 0.08 vs. 2.01 ± 0.05, p < 0.001). Values of eH/M and lH/M < 1.6 effectively differentiated patients with ATTRv from the healthy controls. Cardiac denervation correlated with interventricular septal thickness and the Perugini score but was not related to neurophysiological assessments or NYHA and FAP scales. Conclusions: Ultimately, ¹²³I-mIBG scintigraphy is an effective tool for assessing cardiac denervation in patients with ATTRv.

Brain tumors, both primary and metastatic, represent a significant global health burden due to their high incidence, mortality, and the severe neurological deficits they frequently cause. Gliomas, especially high-grade gliomas (HGGs), rank among the most aggressive and lethal neoplasms, with only modest gains in long-term survival despite extensive molecular research and established standard therapies. In neurosurgical practice, maximizing the extent of safe resection is a principal strategy for improving clinical outcomes. Yet, the infiltrative nature of gliomas often complicates the accurate delineation of tumor margins. Confocal laser endomicroscopy (CLE), originally introduced in gastroenterology, has recently gained prominence in neuro-oncology by enabling real-time, high-resolution cellular imaging during surgery. This technique allows for intraoperative tumor characterization and reduces dependence on time-consuming frozen-section analyses. Recent technological advances, including device miniaturization and second-generation CLE systems, have substantially improved image quality and diagnostic utility. Furthermore, integration with deep learning algorithms and telepathology platforms fosters automated image interpretation and remote expert consultations, thereby accelerating surgical decision making and enhancing diagnostic consistency. Future work should address remaining challenges, such as mitigating motion artifacts, refining training protocols, and broadening the range of applicable fluorescent probes, to solidify CLE's role as a critical intraoperative adjunct in neurosurgical oncology.

Background/Objectives: Several factors, such as age, parity, body mass index, a favorable cervix, and fetal birth weight, are known to be related to the success of labor induction. With advancements in ultrasound technology, these factors have been studied to predict the success of vaginal delivery. However, there has been limited research on ultrasound measures that can effectively predict entry into the active phase of labor. Thus, we aimed to assess the use of cervical quantitative strain sonoelastography to predict entry into the active phase of labor induction. Methods: This prospective study included nulliparous term singleton pregnant women scheduled for labor induction between July 2018 and July 2022. Sonographic parameters were obtained using a transvaginal ultrasound approach with semiautomatic quantitative strain elastography software (E-Cervix; Samsung WS80A ultrasound device with a VR5-9 transducer, Samsung Medison Co., Ltd., Seoul, Republic of Korea), which provides objective measurements through the pixel-based analysis of elastographic maps. Univariate and multivariate logistic regression and area-under-the-curve analyses were used to evaluate the diagnostic performance of the variables under consideration in predicting the onset of the active phase of labor. Results: A total of 71 women were included in the study, and 29 progressed to the active phase. The cervical length, angle of progression, and mean strain from the external cervical os were significantly associated with successful entry into the active phase. The receiver operating characteristic (ROC) curve model also indicated a higher predictive value when the elastographic parameters were combined. Conclusions: Cervical elastography can be used as a sonographic index to predict progression to the active phase of labor.

Background and Objectives: Analysing the characteristics of the mandibular bone through panoramic radiographs could be useful as a prescreening tool for detecting individuals with osteoporosis. The aims of this study were to evaluate the possible associations between the mandibular cortical index (MCI) and bone mineral density (BMD) in various bone regions, to investigate whether BMD better identifies moderate-severe mandibular erosion or severe mandibular erosion, and to establish BMD cut-off points to identify individuals with moderate or severe mandibular cortical erosion. Methods: This study analysed 179 Spanish Caucasian women between September 2021 and June 2024. Bone measurements, including amplitude-dependent speed of sound (Ad-SOS), the ultrasound bone profiler index (UBPI), and the bone transmission time (BTT), were obtained via dual energy X-ray absorptiometry (DXA) for the femoral neck, lumbar spine, and trochanter and quantitative bone ultrasound (QUS) for the phalanx. The MCI was calculated via the Klemetti index from panoramic radiographs. Results: According to the Klemetti index classification, lower QUS measurements in the phalanx and DXA measurements in the femoral neck, trochanter, and lumbar spine were found in women with poorer mandibular cortical bone quality. Our results revealed that, compared with moderate cortical erosion, all the BMD measures had better AUCs when identifying severe cortical erosion. Moreover, femoral neck BMD had the largest area under the curve (AUC = 0.719) for detecting severe mandibular cortical erosion, suggesting a cut-off of <0.703 gr/cm². Finally, predictor analysis of osteoporosis revealed that moderate and severe mandibular cortical erosion, compared with an uninjured mandibular cortical area, was independently associated with a diagnosis of osteoporosis. Conclusions: In conclusion, MCI was associated with BMD measurements assessed by QUS and DXA in various bone regions. Our results suggest that the Klemetti index could be used as a predictor of osteoporosis and fracture risk.

Background/Objectives: Vascular malformations form masses in subcutaneous and muscular tissues throughout the body and are occasionally misdiagnosed as subcutaneous nonvascular tumors. Understanding and differentiating their clinical and imaging features are crucial due to their different treatments and prognoses. This study aimed to report cases of nonvascular tumors that were initially misdiagnosed and treated as vascular malformations. Methods: In this retrospective observational study, we enrolled 14 (1.8%) patients with pathologically diagnosed nonvascular tumors from among 536 patients with 759 lesions of clinically diagnosed vascular malformations. Results: The average age at the initial visit was 41.9 years, with a male-to-female ratio of 3:11. Tumor locations included the lower limb in seven patients, the upper limb in five patients, and the trunk and head in one patient each. Ultrasound evaluation revealed 12 lesions of low-flow vascular malformations and two lesions of high-flow vascular malformations. These findings led to an initial diagnosis of venous or lymphatic malformations in 12 patients and arteriovenous malformations in two patients. Based on the clinical diagnosis, treatments administered before tumor resection included sclerotherapy in four patients and transcatheter arterial embolization in one patient. All patients underwent tumor resection. The final histopathological diagnoses included schwannoma in six patients, epidermal cyst and angiomyoma in two patients each, and other types of tumors in four patients. The average time from initial diagnosis to final histopathological diagnosis was 370 days. Conclusions: Multimodal diagnostic strategies, especially the use of ultrasound, enhance the differentiation between vascular malformations and nonvascular tumors.

Background: Surgical site infections (SSIs) lead to higher hospital readmission rates and healthcare costs, representing a significant global healthcare burden. Machine learning (ML) has demonstrated potential in predicting SSIs; however, the challenge of addressing imbalanced class ratios remains. Objectives: The aim of this study is to evaluate and enhance the predictive capabilities of machine learning models for SSIs by assessing the effects of feature selection, resampling techniques, and hyperparameter optimization. Methods: Using routine SSI surveillance data from multiple hospitals in Saudi Arabia, we analyzed a dataset of 64,793 surgical patients, of whom 1632 developed SSI. Seven machine learning algorithms were created and tested: Decision Tree (DT), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Stochastic Gradient Boosting (SGB), and K-Nearest Neighbors (KNN). We also improved several resampling strategies, such as undersampling and oversampling. Grid search five-fold cross-validation was employed for comprehensive hyperparameter optimization, in conjunction with balanced sampling techniques. Features were selected using a filter method based on their relationships with the target variable. Results: Our findings revealed that RF achieves the highest performance, with an MCC of 0.72. The synthetic minority oversampling technique (SMOTE) is the best-performing resampling technique, consistently enhancing the performance of most machine learning models, except for LR and GNB. LR struggles with class imbalance due to its linear assumptions and bias toward the majority class, while GNB's reliance on feature independence and Gaussian distribution make it unreliable for under-represented minority classes. For computational efficiency, the Instance Hardness Threshold (IHT) offers a viable alternative undersampling technique, though it may compromise performance to some extent. Conclusions: This study underscores the potential of ML models as effective tools for assessing SSI risk, warranting further clinical exploration to improve patient outcomes. By employing advanced ML techniques and robust validation methods, these models demonstrate promising accuracy and reliability in predicting SSI events, even in the face of significant class imbalances. In addition, using MCC in this study ensures a more reliable and robust evaluation of the model's predictive performance, particularly in the presence of an imbalanced dataset, where other metrics may fail to provide an accurate evaluation.

Background/Objectives: While gradient-echo (GRE)-based chemical shift-encoded magnetic resonance imaging (CSE-MRI) offers precise method for measuring adiposity in bone marrow, its limitation lies in the need for additional imaging. On the other hand, spin-echo (SE)-based CSE-MRI can seamlessly integrate into conventional protocols. Recently, a novel technique called the vertebral bone quality (VBQ) score has been introduced. The objective of this study was to investigate the association between fat fraction (FF) measured by GRE-based CSE-MRI (FF_GRE) and FF measured by SE-based CSE-MRI (FF_SE) or the VBQ score. Methods: A retrospective study with 344 patients assessed the correlation between FF and the VBQ score and each measurement's correlation with age using Pearson's correlation (r). Concordance between FF_GRE and FF_SE was assessed using Lin's concordance correlation coefficient (ρ_c). Vertebral lesions (n = 41) were categorized as benign and malignant, and the Mann-Whitney U test was used for comparison. Results: FF_GRE demonstrated strong positive correlations with FF_SE and the VBQ score (r = 0.861 and 0.708, respectively). However, the concordance between FF_GRE and FF_SE was poor (ρ_c = 0.295). All measurements moderately correlated with age (FF_GRE, r = 0.583; FF_SE, r = 0.477; VBQ score, r = 0.468). FF was significantly higher in benign lesions (FF_GRE, p = 0.004; FF_SE, p = 0.007), while the VBQ score did not show statistically significant differences between the two groups (p = 0.089). Conclusions: FF_GRE exhibited a high correlation with the VBQ score. FF_SE showed a strong correlation with FF_GRE, but replacing FF_GRE with FF_SE may be challenging. Both FF and the VBQ score moderately correlated with age. FF demonstrated statistically significant differences between benign and malignant lesions, while the VBQ score did not provide a distinguishable separation.

Interstitial lung abnormalities (ILAs) are incidental radiologic abnormalities on chest computed tomography (CT) examination performed on people in whom interstitial lung disease (ILD) is not suspected. Despite the fact that most of these individuals are asymptomatic, ILAs are not synonymous with subclinical ILD, as a subset of them have symptoms and lung function impairment. On the other hand, not all ILAs progress to clinically significant ILD. Specific imaging features and patterns have been proven more likely to progress, while some individuals may comprise a higher risk group for progression. Numerous studies have demonstrated that ILAs are not only associated with an increased risk of progression toward pulmonary fibrosis and fibrosis-related mortality but are also linked to a greater incidence of lung cancer and a higher rate of all-cause mortality. Considering that the systematic evaluation of large cohorts has shown a prevalence of ILAs up to 7% and that the natural history of ILAs is unclear, successful screening and appropriate monitoring of ILAs is of particular significance for earlier diagnosis, risk factor modification, and treatment. The present review aims to summarize the current knowledge on ILAs and highlight the need to define those at greatest risk of progression to ILD and worse clinical outcomes.

Background/Objectives: Oral leukoplakia (OLK) is an oral mucosal lesion classified in the oral potentially malignant disorder group and is associated with an increased risk of malignant transformation (MT). The aim of this study was to compare the clinical and histopathological features of two OLK groups, a group of smokers and a group of non-smokers. Methods: In this retrospective study, a cohort of 154 patients with OLK was divided into two groups based on the presence of smoking as a major risk factor. OLK diagnoses were established via clinical and histopathological examination. Results: Females were more abundant in the non-smoking group than in the smoking group, where males were more abundant (p < 0.001). The average age of the smokers was lower than that of the non-smokers (p = 0.003). In the smokers, the buccal mucosa was most frequently affected, while in the non-smokers, the gums and the tongue were primarily involved (p = 0.016). In female smokers, involvement of the buccal area and multiple-site involvement were statistically significantly more frequently observed compared to that in female non-smokers (p = 0.006). Non-dysplastic lesions were predominant in both groups, with severe dysplasia observed more frequently in the non-smokers than in the smokers. MT was higher in the non-smoker group compared to that in the smoker group. Conclusions: OLK in smokers is different from OLK in non-smokers concerning female gender involvement, site location, the number of lesions, and the MT rate.

Background/Objectives: Our study aimed to determine the accuracy of the artificial intelligence-based Diagnocat system (DC) in detecting periapical lesions (PL) on panoramic radiographs (PRs). Methods: 616 teeth were selected from 357 panoramic radiographs, including 308 teeth with clearly visible periapical radiolucency and 308 without any periapical lesion. Three groups were generated: teeth with radiographic signs of caries (Group 1), teeth with coronal restoration (Group 2), and teeth with root canal filling (Group 3). The PRs were uploaded to the Diagnocat system for evaluation. The performance of the convolutional neural network in detecting PLs was assessed by its sensitivity, specificity, and positive and negative predictive values, as well as the diagnostic accuracy value. We investigated the possible effect of the palatoglossal air space (PGAS) on the evaluation of the AI tool. Results: DC identified periapical lesions in 240 (77.9%) cases out of the 308 teeth with PL and detected no PL in 68 (22.1%) teeth with PL. The AI-based system detected no PL in any of the groups without PL. The overall sensitivity, specificity, and diagnostic accuracy of DC were 0.78, 1.00, and 0.89, respectively. Considering these parameters for each group, Group 2 showed the highest values at 0.84, 1.00, and 0.95, respectively. Fisher's Exact test showed that PGAS does not significantly affect (p = 1) the detection of PL in the upper teeth. The AI-based system showed lower probability values for detecting PL in the case of central incisors, wisdom teeth, and canines. The sensitivity and diagnostic accuracy of DC for detecting PL on canines showed lower values at 0.27 and 0.64, respectively. Conclusions: The CNN-based Diagnocat system can support the diagnosis of PL on PRs and serves as a decision-support tool during radiographic assessments.