ObjectiveThe objective of the study was to explore the value of MRI texture features based on T1WI, T2-FS and diffusion-weighted imaging (DWI) in differentiation of renal changes in patients with stage III type 2 diabetic nephropathy (DN) and normal subjects.Materials and MethodsA retrospective analysis was performed to analyze 44 healthy volunteers (group A) and 40 patients with stage III type 2 diabetic nephropathy (group B) with microalbuminuria. Urinary albumin to creatinine ratio (ACR) <30 mg/g, estimated glomerular filtration rate (eGFR) in the range of 60–120 ml/(min 1.73 m2), and randomly divided into primary cohort and test cohort. Conventional MRI and DWI of kidney were performed using 1.5 T magnetic resonance imaging (MRI). The outline of the renal parenchyma was manually labeled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method.ResultsThere was a significant difference in sex and body mass index (BMI) (P <0.05) in the primary cohort, with no significant difference in age. In the final results, the wavelet and Laplacian–Gaussian filtering are used to extract 1,892 image features from the original T1WI image, and the LASSO algorithm is used for selection. One first-order feature and six texture features are selected through 10 cross-validations. In the mass, 1,638 imaging extracts features from the original T2WI image.1 first-order feature and 5 texture features were selected. A total of 1,241 imaging features were extracted from the original ADC images, and 5 texture features were selected. Using LASSO-Logistic regression analysis, 10 features were selected for modeling, and a combined diagnosis model of diabetic nephropathy based on texture features was established. The average unit cost in the logistic regression model was 0.98, the 95% confidence interval for the predictive efficacy was 0.9486–1.0, specificity 0.97 and precision 0.93, particularly. ROC curves also revealed that the model could distinguish with high sensitivity of at least 92%.ConclusionIn consequence, the texture features based on MR have broad application prospects in the early detection of DN as a relatively simple and noninvasive tool without contrast media administration.
Background Tongue squamous cell carcinoma (TSCC) is one of the most difficult malignancies to control. It displays particular and aggressive behaviour even at an early stage. The purpose of this paper is to explore the value of radiomics based on magnetic resonance fat-suppressed T2-weighted images in predicting the degree of pathological differentiation of TSCC. Methods Retrospective analysis of 127 patients with TSCC who were randomly divided into a primary cohort and a test cohort, including well-differentiated, moderately differentiated and poorly differentiated. The tumour regions were manually labelled in fat-suppressed T2-weighted imaging (FS-T2WI), and PyRadiomics was used to extract radiomics features. The radiomics features were then selected by the least absolute shrinkage and selection operator (LASSO) method. The model was established by the logistic regression classifier using a 5-fold cross-validation method, applied to all data and evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. Results In total, 1132 features were extracted, and seven features were selected for modelling. The AUC in the logistic regression model for well-differentiated TSCC was 0.90 with specificity and precision values of 0.92 and 0.78, respectively, and the sensitivity for poorly differentiated TSCC was 0.74. Conclusions The MRI-based radiomics signature could discriminate between well-differentiated, moderately differentiated and poorly differentiated TSCC and might be used as a biomarker for preoperative grading.
In this review, we aim to provide an overview of the imaging techniques most commonly used to study Alzheimer's disease (AD).Background: Neuroimaging biomarkers can be used to evaluate these abnormalities, improve the ability of early diagnosis and help predict disease progression. These signs mainly include local brain atrophy on structural MRI, hypometabolic foci, and amyloid plaque deposition in the brain detected by specific imaging.These techniques not only have their unique advantages, but can complement each other in multimodal imaging evaluation of patients with cognitive impairment and dementia.Methods: A literature search was performed on PubMed using the search term combinations "Alzheimer's disease", "Amyloid-beta plaques", and "MRI". We discuss various magnetic resonance imaging (MRI) based techniques, including direct imaging, indirect imaging, amyloid-beta (Aβ) plaque and radiomics, Hybrid PET/MRI and MRI imaging technology in the future, placing a special emphasis on multimodal imaging, and focus our review on the MRI features of Aβ plaques (AD biomarkers).Conclusions: After a lot of research and reasonable selection, multimodal imaging composed of MRI and PET can significantly improve the diagnosis and treatment of AD patients, and the complementary information can be obtained by the new PET/MR instrument at the same time. The findings of this review emphasize that multimodal imaging is likely to be the preferred method for future AD research and clinical practice.
Background To research the first-order features of apparent diffusion coefficient (ADC) values on diffusion-weighted magnetic resonance imaging (DWI) in maxillofacial malignant mesenchymal tumours. Methods The clinical data of 12 patients with rare malignant mesenchymal tumours of the maxillofacial region (6 cases of sarcoma and 6 cases of lymphoma) treated in the hospital from May 2018 to June 2020 and were confirmed by postoperative pathology were retrospectively analyzed. The patients were all examined by 1.5T magnetic resonance imaging. PyRadiomics were used to extract radiomics imaging first-order features. Group differences in quantitative variables were examined using independent-samples t-tests. Results The voxels number of ADCmean and ADCmedian of sarcoma tissues were 44.9124 and 44.2064, respectively, significantly higher than those in lymphoma tissues (ADCmean (− 68.8379) and ADCmedian (− 74.0045)), the difference considered statistically significant, so do the ADCkurt and ADCskew. Conclusions The statistical difference of ADCmean and ADCmedian is significant, it is consistent with the outcome of the manual measurement of the ADC mean value of the most significant cross-section of twelve cases of lymphoma. Development of tumour volume based on the ADC parameter map of DWI demonstrates that the first-order ADC radiomics features analysis can provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Therefore, first-order ADC features of ADCkurt combined ADCskew may improve the diagnosis level.
Objective A prediction model of benign and malignant differentiation was established by magnetic resonance signs of parotid gland tumors to provide an important basis for the preoperative diagnosis and treatment of parotid gland tumor patients. Methods The data from 138 patients (modeling group) who were diagnosed based on a pathologic evaluation in the Department of Stomatology of Jilin University from June 2019 to August 2021 were retrospectively analyzed. The independent factors influencing benign and malignant differentiation of parotid tumors were selected by logistic regression analysis, and a mathematical prediction model for benign and malignant tumors was established. The data from 35 patients (validation group) who were diagnosed based on pathologic evaluation from September 2021 to February 2022 were collected for verification. Results Univariate and multivariate logistic regression analysis showed that tumor morphology, tumor boundary, tumor signal, and tumor apparent diffusion coefficient (ADC) were independent risk factors for predicting benign and malignant parotid gland tumors (P < 0.05). Based on multivariate logistic regression analysis of the modeling group, a mathematical prediction model was established as follows: Y = the ex/(1 + ex) and X = 0.385 + (1.416 × tumor morphology) + (1.473 × tumor border) + (1.306 × tumor signal) + (2.312 × tumor ADC value). The results showed that the area under the receiver operating characteristic curve of the model was 0.832 (95% confidence interval, 0.75–0.91), the sensitivity was 82.6%, and the specificity was 70.65%. The validity of the model was verified using validation group data, for which the sensitivity was 85.71%, the specificity was 96.4%, and the correct rate was 94.3%. The results showed that the area under receiver operating characteristic curve was 0.936 (95% confidence interval, 0.83–0.98). Conclusions Combined with tumor morphology, tumor ADC, tumor boundary, and tumor signal, the established prediction model provides an important reference for preoperative diagnosis of benign and malignant parotid gland tumors.
To explore the value of 1.5T magnetic resonance (MR) fat saturation-T2-weighted imaging (FS-T2WI) and apparent diffusion coefficient (ADC) imaging texture features in distinguishing the renal changes of patients with stage III type 2 diabetic kidney disease (DKD) from healthy people. Methods: This study collected 55 patients with stage III DKD (39 males and 16 females) and 33 healthy controls (13 males and 20 females) from December 2021 to June 2022 in the China-Japan Union Hospital of Jilin University. All subjects were randomly divided in a ratio of 6:4 to extract and screen the FS-T2WI and ADC texture features of the right kidney of the subjects. The area under the curve (AUC) was used to assess the diagnostic accuracy of each model. Results: There were significant differences between urea, creatinine and sex (p<0.05) of the two groups in the training and test set, and no significant difference in age and body mass index (BMI). We extracted 1409 imaging features from the original ADC sequence and selected them by wavelet and Laplace-Gaussian filter and LASSO algorithm, and using the same methods of FS-T2WI. Finally, FS-T2WI and ADC models were selected to construct the united model, including 3 first-order features and 8 texture features. The AUC values of the training set of FS-T2WI, ADC, FS-T2WI+ADC combined logistic regression model were 0.96, 0.91, 0.98; the AUC values of the test set were 0.91, 0.89 and 0.93, and the specificity and accuracy values of the united model were 0.90 and 0.89, respectively. Conclusion: FS-T2WI and ADC imaging features based on 1.5 T MR had diagnostic value in the early diagnosis of DKD stage III, and the combined model of FS-T2WI and ADC had high diagnostic efficiency.
Background The purpose of this study was to identify neurogenic tumours and pleomorphic adenomas of the parapharyngeal space based on the texture characteristics of MRI-T2WI. Methods MR findings and pathological reports of 25 patients with benign tumours in the parapharyngeal space were reviewed retrospectively (13 cases with pleomorphic adenomas and 12 cases with neurogenic tumours). Using PyRadiomics, the texture of the region of interest in T2WI sketched by radiologists was analysed. By using independent sample t-tests and Mann‒Whitney U tests, the selected texture features of 36 Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Dependence Matrix (GLDM) were tested. A set of parameters of texture features showed statistically significant differences between the two groups, which were selected, and the diagnostic efficiency was evaluated via the operating characteristic curve of the subjects. Results The differences in the three parameters – small dependence low level emphasis (SDLGLE), low level emphasis (LGLE) and difference variance (DV) of characteristics – between the two groups were statistically significant (P < 0.05). No significant difference was found in the other indices. ROC curves were drawn for the three parameters, with AUCs of 0.833, 0.795, and 0.744, respectively. Conclusions There is a difference in the texture characteristic parameters based on magnetic resonance T2WI images between neurogenic tumours and pleomorphic adenomas in the parapharyngeal space. For the differential diagnosis of these two kinds of tumours, texture analysis of significant importance is an objective and quantitative analytical tool.
Aggressive fibromatosis (AF), also known as ligamentoid fibromatosis and desmoid tumor, is a fibroblast clonoproliferative lesion located in the deep soft tissue. The present study reports the case of a 36-year-old female with AF who underwent cervical spinal cord ependymoma surgery. AF developed in the soft tissue of the neck adjacent to the incision site. The size of the neck AF increased rapidly over 2 years, and due to discomfort, the patient underwent initial surgical resection without any other combined treatment methods. When the patient was routinely reviewed at 6 months post-surgery, a recurrence of AF of the neck was found. The patient was recommended surgical resection and radiotherapy. This case report should improve the understanding of clinicians with regard to AF, and help the diagnostic process and treatment plan.
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