Giant Cell Tumors of Bone in Patients Aged 18 Years Old or Younger: Imaging Features and Tumor Characteristics.

OBJECTIVE: The majority of giant cell tumors of bone (GCTB) occur in adult patients, especially between the ages of 20 and 40. This study aims to investigate the imaging features of GCTBs in pediatric patients and compare their characteristics with adult cases. METHODS: Fifty-seven cases of patients aged 18 years old or younger were retrospectively analyzed, accounting for 12.8% of GCTBs in the First Affiliated Hospital of Zhengzhou University from 2001 to 2019. One hundred twenty-six adult patients (19 years of age and older) with GCTB occurring in long tubular bones were also included in this study. The following clinical information was identified from the medical records: age, sex, and follow-up data. Imaging features were reviewed by two musculoskeletal radiologists. Patient characteristics and imaging features between the two groups were compared. RESULTS: A total of 57 patients (32 females, 25 males) were included in the study. The patients' ages ranged from 9 to 18 (median = 17 y). The majority of tumors occurred in tubular bones (n = 38, 66.7%) and the pelvis (n = 8, 14.0%). Imaging features were identified in GCTB cases occurring in the long tubular bones. Compared with adult GCTB patients, pediatric GCTB patients had a larger superior-inferior (SI) diameter (P = 0.005) and smaller left-to-right diameter/SI diameter ratio (P = 0.001). Epiphyseal involvement was relatively less common in pediatric patients with GCTBs than in adult patients (P = 0.009). The median age of patients without epiphyseal involvement was lower than the median age of patients with epiphyseal involvement (11 vs 17 y). CONCLUSION: GCTB in the pediatric age group is rare. This study has found that, in pediatric patients with GCTBs, the epiphysis is relatively less involved, and the tumor is more likely to grow longitudinally. These findings are helpful in the diagnosis of GCTBs in the pediatric population.

An MRI Image Analysis of Primary Cardiac Neoplasms.

OBJECTIVE: This study aimed to examine the magnetic resonance imaging (MRI) characteristics of primary cardiac neoplastic lesions. METHODS: A retrospective investigation was conducted on 24 cases of primary cardiac neoplastic lesions as confirmed by surgery and pathology results. All the cases in this study received MRI multi-sequence and multi-dimension scanning, including the cardiac long-axis and short-axis cine sequences, parameter sequences of the cardiac long axis and short axis (T1WI, T2WI), first-pass perfusion sequence, and delayed enhancement sequence of the cardiac long axis and short axis. The age and gender of the patients and the location, size, signal characteristics, and relationship with the neighboring tissues of all the lesions were examined. RESULTS: Twenty-four cases of primary neoplastic lesions were examined in this study, the onset age was 11-72 years old, the median age was 53 years old, and the mean age was 46 years old. Among these cases, there were 8 cases including males and 16 cases including females, 19 cases were benign lesions; including 11 cases of myxoma, 4 cases of hemangioma, 1 case of paraganglioma, 1 case of PEcoma, 1 case of hamartoma, and 1 case of lipoma. The malignant lesions included 3 sarcomas and 2 lymphomas in 5 patients. CONCLUSION: MRI imaging provides a great value in the preoperative classification of primary cardiac neoplastic lesions.

The role of histogram analysis in diffusion-weighted imaging in the differential diagnosis of benign and malignant breast lesions.

BACKGROUND: The present study aims to investigate the role of histogram analysis of intravoxel incoherent motion (IVIM) in the differential diagnosis of benign and malignant breast lesions. METHODS: The magnetic resonance imaging and clinical data of 55 patients (63 lesions) were retrospectively analyzed. The multi-b-valued diffusion-weighted imaging image was processed using the MADC software to obtain the gray-scaled maps of apparent diffusion coefficient (ADC)-slow, ADC-fast and f. The MaZda software was used to extract the histogram metrics of these maps. Combined with the conventional sequence images, the region of interest (ROI) was manually drawn along the edge of the lesion at the maximum level of the gray-scale image, and the difference of the data was analyzed between the benign and malignant breast lesions. RESULTS: There were 29 patients with 37 benign lesions, which included 23 fibroadenomas, 6 adenosis, 1 breast cysts, 4 intraductal papillomas, and 3 inflammations of breast. Furthermore, 26 malignant lesions in 26 patients, which included 20 non-specific invasive ductal carcinomas, 5 intraductal carcinomas and 1 patient with squamous cell carcinoma. The ADC-slow (mean and the 50th percentile) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile) of these malignant breast lesions were significantly lower than those of benign lesions (P < 0.05), while ADC-fast (kurtosis) and f (variance, skewness) of these malignant breast lesions were significantly higher than those of benign lesions (P < 0.05). CONCLUSION: The histogram analysis of ADC-slow (mean and the 50th percentile), ADC-fast (kurtosis) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile. Variance, skewness) can provide a more objective and accurate basis for the differential diagnosis of benign and malignant breast lesions.

Texture analysis of magnetic resonance T1 mapping with dilated cardiomyopathy: A machine learning approach.

The diagnosis of dilated cardiomyopathy (DCM) remains a challenge in clinical radiology. This study aimed to investigate whether texture analysis (TA) parameters on magnetic resonance T1 mapping can be helpful for the diagnosis of DCM.A total of 50 DCM cases were retrospectively screened and 24 healthy controls were prospectively recruited between March 2015 and July 2017. T1 maps were acquired using the Modified Look-Locker Inversion Recovery (MOLLI) sequence at a 3.0 T MR scanner. The endocardium and epicardium were drawn on the short-axis slices of the T1 maps by an experienced radiologist. Twelve histogram parameters and 5 gray-level co-occurrence matrix (GLCM) features were extracted during the TA. Differences in texture features between DCM patients and healthy controls were evaluated by t test. Support vector machine (SVM) was used to calculate the diagnostic accuracy of those texture parameters.Most histogram features were higher in the DCM group when compared to healthy controls, and 9 of these had significant differences between the DCM group and healthy controls. In terms of GLCM features, energy, correlation, and homogeneity were higher in the DCM group, when compared with healthy controls. In addition, entropy and contrast were lower in the DCM group. Moreover, entropy, contrast, and homogeneity had significant differences between these 2 groups. The diagnostic accuracy when using the SVM classifier with all these histogram and GLCM features was 0.85 ±â€Š0.07.A computer-based TA and machine learning approach of T1 mapping can provide an objective tool for the diagnosis of DCM.