The relationship between nutritional status and white matter integrity in older adults: A diffusion tensor imaging study

OBJECTIVES

The purpose of this study to determinate whether there is a relationship between the nutritional status and white matter integrity in older patients by using Diffusion Tensor Imaging (DTI).

METHODS

The patients were evaluated by Mini-Nutritional Assessment Scale. The patients are categorized in the groups of well-nourished, risk of malnutrition, or malnourished, depending on the overall score> 23.5, 17-23.5, or 17; respectively. All patients had brain MRI and DTI. The mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD) and radial diffusivity (RD) values were calculated by ROI-based method in white matter tracts.

RESULTS

Total of the 224 patients; 86 patients had normal nutrition status (group 1), 107 patients were diagnosed with malnutrition risk (group 2) and 31 patients were diagnosed with malnutrition (group 3). Significantly decreased FA values of genu of corpus callosum, forceps minor and significantly increased MD values of middle cerebellar peduncle, and superior frontooccipital fasciculus were detected in group 2 in comparison to group 1 (p < 0.05). After adjusting for the folate and age, MD and RD values of cingulum remained significantly higher and the AD values of superior cerebellar peduncle remained significantly lower in group 3 (p < 0.05).

CONCLUSIONS

Malnutrition was associated with deteriorated DTI values, especially in cingulum and superior cerebellar peduncle. Assessing the nutritional status of older individuals is crucial to avoid its negative impact on brain.

ADVANCES IN KNOWLEDGE

Early diagnosis of malnutrition-related impaired WM integrity is important for prevention and intervention, and DTI is a useful non-invasive technique to be used for this purpose.

An unusual case of retroperitoneal synovial sarcoma with CT, MRI, and F-18 FDG PET/CT findings

Synovial sarcoma, predominantly found in the extremities, rarely occurs in the retroperitoneum. Tumors can often grow to a considerable size before diagnosis, which warrant the critical importance of early detection to minimize morbidity and mortality. While the final diagnosis relies on pathologic examination, imaging plays a crucial role in early detection.

Role of magnetic resonance imaging to differentiate between borderline and malignant serous epithelial ovarian tumors

PURPOSE

We aimed to differentiate serous borderline ovarian tumors (SBOT) from serous epithelial ovarian carcinomas (SEOC) using morphological and functional MRI findings, to improve the patient management.

METHOD

We retrospectively investigated 24 ovarian lesions diagnosed with SBOT and 64 ovarian lesions diagnosed with SEOC. Additional to the demographic and morphological findings T2W signal intensity ratio, mean apparent diffusion coefficient (ADCmean) and total apparent diffusion coefficient (ADCtotal) values were analyzed and compared between two groups.

RESULTS

Bilaterality, pelvic free fluid presence, serum CA-125 level (U/mL), presence of pelvic peritoneal implant were in favor of SEOC. Lower maximum size of solid component and solid size to maximum size ratio, dominantly cystic and solid-cystic appearance, exophytic growth pattern, presence of papiller projection and papillary architecture and internal branching pattern, higher T2W signal intensity ratio, ADCmean and ADCtotal values were in favor of SBOT.

CONCLUSION

Our study revealed that morphological and functional imaging findings were valuable in differentiating BSOT from SEOC.

Differentiation of lung and breast cancer brain metastases: Comparison of texture analysis and deep convolutional neural networks

PURPOSE

Metastases are the most common neoplasm in the adult brain. In order to initiate the treatment, an extensive diagnostic workup is usually required. Radiomics is a discipline aimed at transforming visual data in radiological images into reliable diagnostic information. We aimed to examine the capability of deep learning methods to classify the origin of metastatic lesions in brain MRIs and compare the deep Convolutional Neural Network (CNN) methods with image texture based features.

METHODS

One hundred forty three patients with 157 metastatic brain tumors were included in the study. The statistical and texture based image features were extracted from metastatic tumors after manual segmentation process. Three powerful pre-trained CNN architectures and the texture-based features on both 2D and 3D tumor images were used to differentiate lung and breast metastases. Ten-fold cross-validation was used for evaluation. Accuracy, precision, recall, and area under curve (AUC) metrics were calculated to analyze the diagnostic performance.

RESULTS

The texture-based image features on 3D volumes achieved better discrimination results than 2D image features. The overall performance of CNN architectures with 3D inputs was higher than the texture-based features. Xception architecture, with 3D volumes as input, yielded the highest accuracy (0.85) while the AUC value was 0.84. The AUC values of VGG19 and the InceptionV3 architectures were 0.82 and 0.81, respectively.

CONCLUSION

CNNs achieved superior diagnostic performance in differentiating brain metastases from lung and breast malignancies than texture-based image features. Differentiation using 3D volumes as input exhibited a higher success rate than 2D sagittal images.

Uncommon Testicular Germ Cell Tumor Thrombus in Azygos Vein Detected by FDG PET/CT

ABSTRACT

A 40-year old man admitted to the nuclear medicine department to undergo PET/CT for restaging of known testicular germ cell tumor. The patient had a history of orchiectomy for left testis and left nephrectomy with retroperitoneal lymph node dissection. Histopathology revealed testicular germ cell tumor. During the follow-up period, there is a rapidly expanding mass in posterior mediastinum. PET reveals increased FDG activity in the azygos vein. Contrast-enhanced CT of the thorax showed distended azygos vein with heterogeneously enhancing thrombus and peripheral fat stranding. Findings are consistent with azygos vein tumor thrombosis related to testicular germ cell tumor.

Diffusion Tensor Imaging of Brain Metastases in Patients with Breast Cancer According to Molecular Subtypes

BACKGROUND AND PURPOSE

Recent studies have shown that diffusion tensor imaging (DTI) parameters are used to follow the patients with breast cancer and correlate well as a prognostic parameter of breast cancer. However, as far as we know, there is no data to compare the DTI features of breast cancer brain metastases according to molecular subtypes in the literature. Our aim is to evaluate whether there are any differences in DTI parameters of brain metastases in patients with breast cancer according to molecular subtypes.

METHODS

Twenty-seven patients with breast cancer and 82 metastatic brain lesions were included. We classified subjects into three subgroups according to their hormone expression; Group 0, triple- negative (n; 6, 19 lesions), group 1, HER2-positive (n;16, 54 lesions) and group 2, hormone-- positive group (n; 5, 9 lesions). The apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) values in DTI were measured and compared between three groups.

RESULTS

ADC, AD and RD values of group 2 were significantly lower compared to group 0. No significant differences were found in FA, ADC, AD and RD values between the group 0 and 1 and the group 1 and 2.

CONCLUSION

Metastasis of aggressive triple-negative breast cancer showed higher ADC values compared to the less aggressive hormone-positive group. Higher ADC values in brain metastases of breast cancer may indicate a poor prognosis, so DTI findings could play a role in planning appropriate treatment.