18F-FDG PET/CT imaging of IgG4-producing MALT lymphoma with multiple site involvement.

18F-FDG PET/CT is regarded as a modality utilized for the purpose of lesion localization, staging and assessment of treatment response in patients with lymphoma. However, it is difficult that we diagnose among multifocal lymphoma, IgG4-related disease (IgG4-RD), or a combination of both conditions when confronted with multiple sites of 18F-FDG uptake with heightened serum IgG4 levels. We present a case of a 72-year-old male who was suspected of Sjögren's syndrome based on symptoms of xerostomia accompanied by swelling of the bilateral upper eyelid and salivary glands. Following a diagnostic biopsy that revealed mucosa-associated lymphoid tissue (MALT) lymphoma as a possible finding, 18F-FDG PET/CT was conducted, which demonstrated multiple sites of 18F-FDG accumulation. While multifocal MALT lymphoma was initially suspected, the coexistence of IgG4-RD could not be definitively ruled out due to the elevated serum IgG4 levels. Subsequent histopathological and immunohistochemical examinations confirmed the diagnosis of IgG4-producing MALT lymphoma. After receiving systemic therapy with rituximab, the swelling of the bilateral upper eyelid and parotid glands resolved upon visual examination, and the serum IgG4 levels returned to within the normal range in a few months. No new lesions were detected during the subsequent follow-up examinations conducted over a period of 3 years.

Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer.

PURPOSE: To develop and identify machine learning (ML) models using pretreatment clinical and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-FDG-PET)-based radiomic characteristics to predict disease recurrences in patients with breast cancers who underwent surgery. PROCEDURES: This retrospective study included 112 patients with 118 breast cancer lesions who underwent [18F]-FDG-PET/ X-ray computed tomography (CT) preoperatively, and these lesions were assigned to training (n=95) and testing (n=23) cohorts. A total of 12 clinical and 40 [18F]-FDG-PET-based radiomic characteristics were used to predict recurrences using 7 different ML algorithms, namely, decision tree, random forest (RF), neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine (SVM) with a 10-fold cross-validation and synthetic minority over-sampling technique. Three different ML models were created using clinical characteristics (clinical ML models), radiomic characteristics (radiomic ML models), and both clinical and radiomic characteristics (combined ML models). Each ML model was constructed using the top ten characteristics ranked by the decrease in Gini impurity. The areas under ROC curves (AUCs) and accuracies were used to compare predictive performances. RESULTS: In training cohorts, all 7 ML algorithms except for logistic regression algorithm in the radiomics ML model (AUC = 0.760) achieved AUC values of >0.80 for predicting recurrences with clinical (range, 0.892-0.999), radiomic (range, 0.809-0.984), and combined (range, 0.897-0.999) ML models. In testing cohorts, the RF algorithm of combined ML model achieved the highest AUC and accuracy (95.7% (22/23)) with similar classification performance between training and testing cohorts (AUC: training cohort, 0.999; testing cohort, 0.992). The important characteristics for modeling process of this RF algorithm were radiomic GLZLM_ZLNU and AJCC stage. CONCLUSIONS: ML analyses using both clinical and [18F]-FDG-PET-based radiomic characteristics may be useful for predicting recurrence in patients with breast cancers who underwent surgery.

Application of 123I-MIBG myocardial maximum standardized uptake value to characterize cardiac function in patients with pheochromocytoma: comparison with echocardiography.

PURPOSE: This study examined the usefulness of the maximum standardized uptake value (SUVmax) of myocardial [123I]-metaiodobenzylguanidine ([123I]-MIBG) to characterize myocardial function by comparing it with echocardiographic parameters in patients with pheochromocytoma. MATERIALS AND METHODS: This study included 18 patients with pheochromocytoma who underwent both planar and [123I]-MIBG single-photon emission computed tomography/computed tomography scans and echocardiography before surgery. Myocardial [123I]-MIBG visibility and SUVmax were compared with echocardiographic parameters related to systolic and diastolic functions. The Mann-Whitney U test, Fisher exact test, or Spearman rank correlation assessed differences or relationships between two quantitative variables. RESULTS: On visual analysis, 6 patients showed normal myocardial [123I]-MIBG uptake, whereas 12 patients showed decreased myocardial [123I]-MIBG uptake. No patients showed systolic dysfunction. A significant difference was observed in the incidence of diastolic dysfunction between the groups with normal and decreased uptake (p = 0.009), and left ventricular (LV) diastolic dysfunction was observed in 9 (75%) of 12 patients with decreased myocardial uptake. The myocardial SUVmax was significantly lower in 9 patients with LV diastolic dysfunction than in 9 patients with normal cardiac function (1.67 ± 0.37 vs. 3.03 ± 1.38, p = 0.047). Myocardial SUVmax was positively correlated with septal e' (early diastolic velocity of septal mitral annulus) (ρ = 0.51, p = 0.031) and negatively correlated with the septal E/e' ratio (early mitral E-velocity to early diastolic velocity of septal mitral annulus; ρ = - 0.64, p = 0.004), respectively. CONCLUSIONS: LV diastolic dysfunction was inversely related to myocardial [123I]-MIBG uptake. Myocardial [123I]-MIBG SUVmax may be useful for characterizing cardiac function in patients with pheochromocytoma. Second abstract. The semiquantitative analysis using the myocardial SUVmax in 123I-MIBG SPECT/CT was found to be potentially useful for characterizing cardiac function in patients with pheochromocytoma.

The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer.

PURPOSE: To examine whether the machine learning (ML) analyses using clinical and pretreatment 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-FDG-PET)-based radiomic features were useful for predicting prognosis in patients with hypopharyngeal cancer. PROCEDURES: This retrospective study included 100 patients with hypopharyngeal cancer who underwent [18F]-FDG-PET/X-ray computed tomography (CT) before treatment, and these patients were allocated to the training (n=80) and validation (n=20) cohorts. Eight clinical (age, sex, histology, T stage, N stage, M stage, UICC stage, and treatment) and 40 [18F]-FDG-PET-based radiomic features were used to predict disease progression. A feature reduction procedure based on the decrease of the Gini impurity was applied. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were compared using the area under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis. RESULTS: The five most important features for predicting disease progression were UICC stage, N stage, gray level co-occurrence matrix entropy (GLCM_Entropy), gray level run length matrix run length non-uniformity (GLRLM_RLNU), and T stage. Patients who experienced disease progression displayed significantly higher UICC stage, N stage, GLCM_Entropy, GLRLM_RLNU, and T stage than those without progression (each, p<0.001). In both cohorts, the logistic regression model constructed by these 5 features was the best performing classifier (training: AUC=0.860, accuracy=0.800; validation: AUC=0.803, accuracy=0.700). In the logistic regression model, 5-year PFS was significantly higher in patients with predicted non-progression than those with predicted progression (75.8% vs. 8.3%, p<0.001), and this model was only the independent factor for PFS in multivariate analysis (hazard ratio = 3.22; 95% confidence interval = 1.03-10.11; p=0.045). CONCLUSIONS: The logistic regression model constructed by UICC, T and N stages and pretreatment [18F]-FDG-PET-based radiomic features, GLCM_Entropy, and GLRLM_RLNU may be the most important predictor of prognosis in patients with hypopharyngeal cancer.

Differentiation of hemangioblastoma from brain metastasis using MR amide proton transfer imaging.

BACKGROUND AND PURPOSE: Differentiation between hemangioblastoma and brain metastasis remains a challenge in neuroradiology using conventional MRI. Amide proton transfer (APT) imaging can provide unique molecular information. This study aimed to evaluate the usefulness of APT imaging in differentiating hemangioblastomas from brain metastases and compare APT imaging with diffusion-weighted imaging and dynamic susceptibility contrast perfusion-weighted imaging. METHODS: This retrospective study included 11 patients with hemangioblastoma and 20 patients with brain metastases. Region-of-interest analyses were employed to obtain the mean, minimum, and maximum values of APT signal intensity, apparent diffusion coefficient (ADC), and relative cerebral blood volume (rCBV), and these indices were compared between hemangioblastomas and brain metastases using the unpaired t-test and Mann-Whitney U test. Their diagnostic performances were evaluated using receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC). AUCs were compared using DeLong's method. RESULTS: All MRI-derived indices were significantly higher in hemangioblastoma than in brain metastasis. ROC analysis revealed the best performance with APT-related indices (AUC = 1.000), although pairwise comparisons showed no significant difference between the mean ADC and mean rCBV. CONCLUSIONS: APT imaging is a useful and robust imaging tool for differentiating hemangioblastoma from metastasis.

Microglial Ca(2+)-activated K(+) channels are possible molecular targets for the analgesic effects of S-ketamine on neuropathic pain.

Ketamine is an important analgesia clinically used for both acute and chronic pain. The acute analgesic effects of ketamine are generally believed to be mediated by the inhibition of NMDA receptors in nociceptive neurons. However, the inhibition of neuronal NMDA receptors cannot fully account for its potent analgesic effects on chronic pain because there is a significant discrepancy between their potencies. The possible effect of ketamine on spinal microglia was first examined because hyperactivation of spinal microglia after nerve injury contributes to neuropathic pain. Optically pure S-ketamine preferentially suppressed the nerve injury-induced development of tactile allodynia and hyperactivation of spinal microglia. S-Ketamine also preferentially inhibited hyperactivation of cultured microglia after treatment with lipopolysaccharide, ATP, or lysophosphatidic acid. We next focused our attention on the Ca(2+)-activated K(+) (K(Ca)) currents in microglia, which are known to induce their hyperactivation and migration. S-Ketamine suppressed both nerve injury-induced large-conductance K(Ca) (BK) currents and 1,3-dihydro-1-[2-hydroxy-5-(trifluoromethyl)phenyl]-5-(trifluoromethyl)-2H-benzimidazol-2-one (NS1619)-induced BK currents in spinal microglia. Furthermore, the intrathecal administration of charybdotoxin, a K(Ca) channel blocker, significantly inhibited the nerve injury-induced tactile allodynia, the expression of P2X(4) receptors, and the synthesis of brain-derived neurotrophic factor in spinal microglia. In contrast, NS1619-induced tactile allodynia was completely inhibited by S-ketamine. These observations strongly suggest that S-ketamine preferentially suppresses the nerve injury-induced hyperactivation and migration of spinal microglia through the blockade of BK channels. Therefore, the preferential inhibition of microglial BK channels in addition to neuronal NMDA receptors may account for the preferential and potent analgesic effects of S-ketamine on neuropathic pain.