The aim of this study was to investigate the diagnostic value of machine-learning models with radiomic features and clinical features in preoperative differentiation of common lesions located in the anterior skull base.Methods: A total of 235 patients diagnosed with pituitary adenoma, meningioma, craniopharyngioma, or Rathke cleft cyst were enrolled in the current study. The discrimination was divided into three groups: pituitary adenoma vs. craniopharyngioma, meningioma vs. craniopharyngioma, and pituitary adenoma vs. Rathke cleft cyst. In each group, five selection methods were adopted to select suitable features for the classifier, and nine machine-learning classifiers were employed to build discriminative models. The diagnostic performance of each combination was evaluated with area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity calculated for both the training group and the testing group. Results:In each group, several classifiers combined with suitable selection methods represented feasible diagnostic performance with AUC of more than 0.80. Moreover, the combination of least absolute shrinkage and selection operator as the feature-selection method and linear discriminant analysis as the classification algorithm represented the best comprehensive discriminative ability. Conclusion:Radiomics-based machine learning could potentially serve as a novel method to assist in discriminating common lesions in the anterior skull base prior to operation.
ObjectiveFluorodeoxyglucose Positron emission tomography/computerized tomography (FDG PET/CT) has become popular for diagnosing periprosthetic joint infections (PJI). However, the diagnostic accuracy for this technique has varied from report to report. This meta-analysis was performed to assess the accuracy of FDG PET/CT for PJI diagnosis.Material and MethodsWe conducted a systematic search of online academic databases for all studies reporting the diagnostic accuracy of FDG PET/CT for PJI. Meta-analysis was performed using STATA software.Results23 studies, containing data on 1,437 patients, met inclusion criteria. Pooled sensitivity and specificity of FDG PET/CT for diagnosing PJI were 85% (95% CI, 76%, 91%) and 86% (95% CI, 78%, 91%), respectively with an AUC of 0.92. LRP was 6.1 (95% CI, 3.8, 9.7) and LRN was 0.17 (0.11, 0.28), indicating that FDG PET/CT cannot be used for confirmation or exclusion of PJI. There was significant inter-study heterogeneity, but no significant publication bias was noted.ConclusionsOur study found that FDG PET/CT has an important role as a diagnostic tool for PJI with high sensitivity and specificity. Further studies exploring its accuracy in different PJI locations remain necessary.
BackgroundThis study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas.MethodsIn total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.ResultsSeveral classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.ConclusionOur ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.
Background This study aimed to investigate the relationship between active pulmonary tuberculosis (TB) and type 2 diabetes mellitus (T2DM) by analysing the clinical features and computed tomography (CT) findings of patients with active pulmonary TB and comorbid T2DM (TB-DM) in the LiangShan Yi regions. Methods We collected data from 154 hospitalised patients with TB-DM initially confirmed at an infectious disease hospital in the Liangshan Yi Autonomous Prefecture between 1 and 2019, and 31 December 2021. These were matched by sex and age ± 3 years to 145 hospitalised patients with initially confirmed pulmonary TB without comorbid T2DM (TB-NDM) over the same period. The clinical characteristics of the two groups were analysed separately. Three group-blinded radiologists independently analysed the CT findings and classified them into mild-to-moderate and severe groups. Severe chest CT lesion refers to a lesion that is less diffused or moderately dense and either exceeds the total volume of one lung, a high-density fused lesion greater than one-third of the volume of one lung, or a cavitary lesion with a maximum diameter ≥ 4 cm. Results No significant differences were observed in the presentation of clinical features. Regarding the severity of chest CT manifestation, patients with TB-DM had significantly more severe TB than those with TB-NDM (89.61% vs. 68.97%, P < 0.0001). Regarding CT findings, patients with TB-DM had higher proportions of consolidation (79.22% vs. 52.41%, P < 0.0001), cavitary lesions (85.06% vs. 59.31%, P < 0.0001), bronchiectasis (71.43% vs. 31.03%, P < 0.0001), exudative lesions (88.96% vs. 68.28%, P < 0.0001), and fibrous lesions (93.51% vs. 68.97%, P < 0.0001) than patients with TB-NDM. In conclusion, patients with TB-DM have more severe pulmonary TB CT findings than those without. There were no significant differences in the distribution of lesions in the lung lobes between TB-DM and TB-NDM patients. Conclusions Among patients hospitalised with pulmonary TB, those with T2DM had more severe findings on chest CT than those without T2DM. However, the clinical presentation was not significantly different.
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