BACKGROUND AND PURPOSE Quantification of PCF volume and the degree of PCF crowdedness were found beneficial for differential diagnosis of tonsillar herniation and prediction of surgical outcome in CMI. However, lack of automated methods limits the clinical use of PCF volumetry. An atlas-based method for automated PCF segmentation tailored for CMI is presented. The method performance is assessed in terms of accuracy and spatial overlap with manual segmentation. The degree of association between PCF volumes and the lengths of previously proposed linear landmarks is reported. MATERIALS AND METHODS T1-weighted volumetric MR imaging data with 1-mm isotropic resolution obtained with the use of a 3T scanner from 14 patients with CMI and 3 healthy subjects were used for the study. Manually delineated PCF from 9 patients was used to establish a CMI-specific reference for an atlas-based automated PCF parcellation approach. Agreement between manual and automated segmentation of 5 different CMI datasets was verified by means of the t test. Measurement reproducibility was established through the use of 2 repeated scans from 3 healthy subjects. Degree of linear association between PCF volume and 6 linear landmarks was determined by means of Pearson correlation. RESULTS PCF volumes measured by use of the automated method and with manual delineation were similar, 196.2 ± 8.7 mL versus 196.9 ± 11.0 mL, respectively. The mean relative difference of −0.3 ± 1.9% was not statistically significant. Low measurement variability, with a mean absolute percentage value of 0.6 ± 0.2%, was achieved. None of the PCF linear landmarks were significantly associated with PCF volume. CONCLUSIONS PCF and tissue content volumes can be reliably measured in patients with CMI by use of an atlas-based automated segmentation method.

Purpose To build a framework for investigation of the associations between imaging, clinical target volumes (CTVs), and metabolic tumor volumes (MTVs) features for better understanding of the underlying information in the CTVs and dependencies between these volumes. High-throughput extraction of imaging and metabolomic quantitative features from magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging of glioblastoma multiforme (GBM) results in tens of variables per patient. In radiation therapy of GBM the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl aspartate (NAA) and choline (Cho). The corresponding clinical target volumes (CTVs) for radiation therapy are based on contrast-enhanced T1-weighted (CE-T1w) and T2-weighted (T2w)/fluid-attenuated inversion recovery MRI. Methods and Materials Necrotic portions, enhancing lesion, and edema were manually contoured on CE-T1w/T2w images for 17 GBM patients. Clinical target volumes and MTVs for NAA (MTVNAA) and Cho (MTVCho) were constructed. Imaging and metabolic features related to size, shape, and signal intensities of the volumes were extracted. Tumors were also scored categorically for 10 semantic imaging traits by a neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and CTVs/MTVs features were visualized as heatmaps. Associations between MTVNAA and MTVCho and imaging features were studied using Spearman correlation. Results Forty-eight imaging features were extracted per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. Twenty features were extracted from CTVs and MTVs. A series of semantic imaging traits were replaced with automatically extracted continuous variables. There were multiple (22) significant correlations of imaging measures with CTVs/MTVNAA, whereas there were only 6 with CTVs/MTVCho. Conclusions A framework for investigation of codependencies between MRI and magnetic resonance spectroscopic imaging radiomic features and CTVs/MTVs has been established. The MTV for NAA was found to be closely associated with MRI volumes, whereas very few imaging features were related to MTVCho, indicating that Cho provides additional information to imaging.

Background and Purpose: Paragangliomas (PGLs) are rare neuroendocrine tumors with imaging features that can overlap with other entities. This study hypothesizes that given overexpression of somatostatin receptor (SSTR) 2, PGLs can be differentiated on Ga-68 DOTATATE positron emission tomography/computed tomography (PET/CT) from other benign or malignant lesions. Materials and Methods: Ninety-six patients with known tumors of the head and neck who underwent Ga-68 DOTATATE PET/CT from May 2017 to December 2021 were retrospectively reviewed from a single institution. Of these, 43 patients had histopathological confirmation and 66 positive lesions were discovered on PET/CT. For each lesion, the SUV max, the SUV lesion to liver ratio, and the SUV lesion to spleen ratio were analyzed. Results: PGLs ( n = 37) showed the most intense uptake, and the mean of SUVmax was 69.3 (range 3.7–225.9). Metastatic PGL and metastasis from other neuroendocrine tumors ( n = 13) demonstrated intermediate uptake, the mean of SUVmax was 15.16 (range 2.3–40.3). Meningiomas ( n = 3) had intermediate uptake, and the mean of SUVmax was 12.37 (range 2.5–19.4). One patient with esthesioneuroblastoma had 5 lesions in the head and neck, and the mean of SUVmax was 18.9 (range 6.9–49.4). Schwannomas ( n = 4) had very low uptake, and the mean of SUVmax was 1.75 (range 1.1–2.2). Other rare cases with low uptake included 1 each of osteosarcoma, acinic cell carcinoma, ectopic thyroid tissue, and plasmacytoma, and the mean of SUVmax was 4.75 (range 2.3–6.1). Conclusions: Ga-68 DOTATATE PET/CT can be a useful adjunct in differentiating tumors in the head and neck. PGLs demonstrate the highest uptake. Meningioma, esthesioneuroblastoma, and neuroendocrine tumor metastasis have intermediate uptake. Schwannomas and other rare tumors exhibit low uptake.