Bilateral Adrenal Metastasis of Prostatic Adenocarcinoma on 68 Ga-PSMA PET/CT Imaging.

ABSTRACT: An 84-year-old man with prostate adenocarcinoma underwent 68 Ga-PSMA PET/CT due to PSA recurrence. Foci of 68 Ga-PSMA uptake were observed in bilateral adrenal glands. Adrenal MRI showed metastasis only in the left adrenal gland. Metastatic 68 Ga-PSMA uptake was also observed in the mediastinum and bone. Enzalutamide treatment was started. Follow-up 68 Ga-PSMA PET/CT scan showed regression in both adrenal gland metastases and other metastases.

Detection of Rare Gallbladder Microperforation by 18F-FDG PET/CT in a Patient with Maxillary Sinus Cancer.

Gallbladder perforation is one of the most serious complications of cholecystitis and is rarely seen in 2-11% of cases. Pericholecystic abscesses secondary to gallbladder perforation are rare. Rapid diagnosis is important because of high morbidity and mortalityrates. A subcapsular abscess secondary to gallbladder microperforation is presented on 18F-fluorodeoxyglucose positron emission tomography/computed tomography performed for restaging in a patient with maxillary sinus cancer.

Can an apparent diffusion coefficient of uterine fibroid before uterine artery embolization predict potential fibroid response?

OBJECTIVES: ACOG guidance confirms the use of uterine artery embolisation (UAE) as an alternative to hysterectomy or myomectomy. The main objective of this article is to evaluate the ability of preoperative magnetic resonance imaging (MRI) to study the relationship between uterine fibroid reduction and diffusion coefficient (ADC) value after UAE. This is a relevant topic with the growing interest in using ADC as a noninvasive imaging biomarker for monitoring tissue changes and predicting uterine fibroid response to UAE over the past years. MATERIAL AND METHODS: In this prospective controlled non-randomized trial; uterine fibroid volume, fibroid diameter, uterine volume, fibroid ADC and normal myometrium ADC were recorded before and after UAE. Wilcoxon test was used in the analysis of the dependent quantitative data. Pearson correlation coefficients were calculated between post-UAE uterine volume, fibroid volume, and average fibroid diameter reduction and the patient's age, parity, gravidity, fibroid ADC and myometrial ADC before UAE. RESULTS: The mean fibroid volume reduction was 36.0% (range between 17.3-77.7%). Mean fibroid diameter, fibroid volume, uterine volume, and myometrium ADC values after UAE were significantly lower than before the procedure (p = 0.002, < 0.001, 0.001, 0.006 respectively), but the decrease in fibroid ADC is not significant. As a result decrease in fibroid volume was greater as pre-UAE fibroid ADC values increased, and that finding may contribute to the selection of the patients for the procedure. CONCLUSIONS: The ADC value before UAE was positively correlated with fibroid volume reduction.

Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion.

PURPOSE: Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poor prognosis in gastric cancers. In this work, we aimed to investigate the potential role of computed tomography (CT) texture analysis in predicting LVI and PNI in patients with tubular gastric adenocarcinoma (GAC) using a machine learning (ML) approach. METHODS: Sixty-eight patients who underwent total gastrectomy with curative (R0) resection and D2-lymphadenectomy were included in this retrospective study. Texture features were extracted from the portal venous phase CT images. Dimension reduction was first done with a reproducibility analysis by two radiologists. Then, a feature selection algorithm was used to further reduce the high-dimensionality of the radiomic data. Training and test splits were created with 100 random samplings. ML-based classifications were done using adaptive boosting, k-nearest neighbors, Naive Bayes, neural network, random forest, stochastic gradient descent, support vector machine, and decision tree. Predictive performance of the ML algorithms was mainly evaluated using the mean area under the curve (AUC) metric. RESULTS: Among 271 texture features, 150 features had excellent reproducibility, which were included in the further feature selection process. Dimension reduction steps yielded five texture features for LVI and five for PNI. Considering all eight ML algorithms, mean AUC and accuracy ranges for predicting LVI were 0.777-0.894 and 76%-81.5%, respectively. For predicting PNI, mean AUC and accuracy ranges were 0.482-0.754 and 54%-68.2%, respectively. The best performances for predicting LVI and PNI were achieved with the random forest and Naive Bayes algorithms, respectively. CONCLUSION: ML-based CT texture analysis has a potential for predicting LVI and PNI of the tubular GACs. Overall, the method was more successful in predicting LVI than PNI.