Purpose: This study aimed to differentiate well-differentiated liposarcoma (WDLPS) from lipoma using the magnetic resonance imaging (MRI) findings and improve the preoperative diagnostic accuracy between the two tumors. Although lipoma is a benign tumor that does not necessarily require surgical intervention, WDLPS exhibits local aggressiveness and typically necessitates surgical excision, making its differentiation clinically significant. Materials and Methods: Among 363 patients diagnosed with lipoma or WDLPS at the authors’ institution between January 2013 and April 2022, 45 patients with lipoma and 15 with WDLPS were selected using propensity score matching based on age and sex. The following six MRI features were analyzed: tumor size, shape, septal thickness, tumor depth, fat content ratio, and the presence of nodular non-fatty tissue components. The sensitivity and specificity of each feature were evaluated. Results: Among the MRI features, the fat content ratio (specificity 95%) and the presence of nodular non-fatty tissue components (specificity 98%) were useful for excluding WDLPS, but their sensitivities were relatively low (46% and 27%). The sensitivities of other features, such as tumor size, shape, septal thickness, and depth, ranged from 47% to 67%, suggesting that MRI alone has limitations in reliably distinguishing the two tumors. In addition, fat content ratios below 75% and the presence of nodular non-fatty tissue components were significantly more common in WDLPS than in lipoma. Conclusion The MRI findings can serve as a useful tool for excluding WDLPS when differentiating it from lipoma. Nevertheless, MRI alone has limitations in achieving a definitive diagnosis because of the low sensitivity of individual features. Therefore, additional approaches such as radiomics analysis, artificial intelligence algorithms, and multicenter studies, are needed to improve the diagnostic accuracy and establish personalized treatment strategies.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Objective: To evaluate the clinical efficacy of ultrafast dynamic contrast-enhanced (DCE)-MRI using a compressed sensing (CS) technique for differentiating benign and malignant soft-tissue tumors (STTs) and to evaluate the factors related to the grading of malignant STTs. Materials and Methods: A total of 165 patients (96 male; mean age, 61 years), comprising 111 with malignant STTs and 54 with benign STTs according to the 2020 WHO classification, underwent DCE-MRI with CS between June 2018 and June 2023. The clinical, qualitative, and quantitative parameters associated with conventional MRI were also obtained. During post-processing of the early arterial phase of DCE-MRI, the time-to-enhance (TTE), time-to-peak (TTP), initial area under the curve at 60 s (iAUC60), and maximum slope were calculated. Furthermore, the delayed arterial phase parameters of DCEMRI, including Ktrans , Kep, Ve, and iAUC values and time-concentration curve (TCC) types, were determined. Clinical and MRI parameters were statistically analyzed to differentiate between benign and malignant tumors and their correlation with tumor grading. Results: According to logistic regression analysis, the TTE value (P < 0.001) of the early arterial phase and Ve (P = 0.039) and iAUC (P = 0.006) values of the delayed arterial phase, as well as age, location, peritumoral edema, and contrast heterogeneity on conventional MRI, were significant (P = 0.001–0.015) in differentiating benign and malignant tumors. Among all the quantitative parameters, the TTE value had the highest accuracy, with an area under the receiver operating characteristic curve of 0.902. The grading of malignant tumors was significantly correlated with peritumoral edema; CE heterogeneity; visual diffusion restriction; minimum and mean ADC; TTP, Kep, and Ve values; and the TCC graph (all P < 0.05). Conclusion Among the quantitative parameters obtained using ultrafast DCE-MRI, early arterial phase TTE was the most accurate for distinguishing between benign and malignant tumors.

Objective: To compare the progression of muscle fibrosis of various site and its relation between cardiac deterioration in Duchenne muscular dystrophy (DMD). In this study aimed to examine the associations between echocardiogram-based cardiac function indices and fibrosis of the abdominal and lower extremity muscles in patients with DMD to facilitate early detection of cardiac dysfunction and identify its predictors. Methods: Twenty-one patients with DMD patients were enrolled in the study. The association between cardiac dysfunction and fibrosis of the abdominal and lower extremity muscles was determined by analyzing the echocardiography and elastography. Non-parametric Spearman rank correlation coefficients were used to examine the pairwise relationships between cardiac function and muscle elasticity. Results: All patients were male and non-ambulant. Their mean age was 18.45±4.28 years. The strain ratios of the abdominal muscle and quadriceps muscles were significantly higher than those of the medial gastrocnemius. The strain ratio of the rectus abdominis muscle has a significant negative correlation with left ventricular ejection fraction. Cardiac function and valvular insufficiency were not significantly correlated with muscle strain ratio. According to the result of our study, the only skeletal muscle which showed significant correlation with cardiac dysfunction was degree abdominal muscle fibrosis. Conclusion The degree of fibrosis of respiratory muscles was also significantly associated with cardiac dysfunction; therefore, it can be used as a predictor of cardiac dysfunction in patients with DMD in clinical practice.

Objective: To compare the progression of muscle fibrosis of various site and its relation between cardiac deterioration in Duchenne muscular dystrophy (DMD). In this study aimed to examine the associations between echocardiogram-based cardiac function indices and fibrosis of the abdominal and lower extremity muscles in patients with DMD to facilitate early detection of cardiac dysfunction and identify its predictors. Methods: Twenty-one patients with DMD patients were enrolled in the study. The association between cardiac dysfunction and fibrosis of the abdominal and lower extremity muscles was determined by analyzing the echocardiography and elastography. Non-parametric Spearman rank correlation coefficients were used to examine the pairwise relationships between cardiac function and muscle elasticity. Results: All patients were male and non-ambulant. Their mean age was 18.45±4.28 years. The strain ratios of the abdominal muscle and quadriceps muscles were significantly higher than those of the medial gastrocnemius. The strain ratio of the rectus abdominis muscle has a significant negative correlation with left ventricular ejection fraction. Cardiac function and valvular insufficiency were not significantly correlated with muscle strain ratio. According to the result of our study, the only skeletal muscle which showed significant correlation with cardiac dysfunction was degree abdominal muscle fibrosis. Conclusion The degree of fibrosis of respiratory muscles was also significantly associated with cardiac dysfunction; therefore, it can be used as a predictor of cardiac dysfunction in patients with DMD in clinical practice.

Objective: The Scales for Outcomes in Parkinson’s Disease–Cognition (SCOPA-Cog) was developed to assess cognition in patients with Parkinson’s disease (PD). In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPACog (K-SCOPA-Cog). Methods: We enrolled 129 PD patients with movement disorders from 31 clinics in South Korea. The original version of the SCOPA-Cog was translated into Korean using the translation-retranslation method. The test–retest method with an intraclass correlation coefficient (ICC) and Cronbach’s alpha coefficient were used to assess reliability. Spearman’s rank correlation analysis with the Montreal Cognitive Assessment-Korean version (MOCA-K) and the Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach’s alpha coefficient was 0.797, and the ICC was 0.887. Spearman’s rank correlation analysis revealed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusion Our results demonstrate that the K-SCOPA-Cog has good reliability and validity.