Objectives: To investigate whether PET–CT or PET–MRI is more appropriate for imaging prostate cancer, in terms of primary tumor detection, local staging and recurrence, as well as lymph nodes and distant metastases. Methods: A systematic literature search was conducted on Embase, PubMed/MEDLINE, and the Cochrane Library database. Studies evaluating the diagnostic performance of PET–CT vs PET–MRI in prostate cancer patients were emphasized. Results: We reviewed 57 original research articles during the period 2016–2021: 14 articles regarding the radiotracer PSMA; 18 articles regarding the primary tumor detection, local tumor staging, managing local recurrence; 17 articles for managing lymph node metastases; and eight articles for managing bone and other distant metastases. PSMA PET could be complementary to mpMRI for primary prostate cancer localization and is particularly valuable for PI-RADS three lesions. PET–MRI is better than PET–CT in local tumor staging due to its specific benefit in predicting extracapsular extension in MRI-occult prostate cancer patients. PET–MRI is likely superior as compared with PET–CT in detecting local recurrence, and has slightly higher detection rates than PET–CT in lymph node recurrence. PET–CT and PET–MRI seem to have equivalent performance in detecting distant bony or visceral metastases. Conclusion: In conclusion, PET–MRI is suitable for local and regional disease, either primary staging or restaging, whereas PET–CT is valuable for managing distant bony or visceral metastasis. Advances in knowledge: We reviewed the emerging applications of PET–MRI and PET–CT in clinical aspects. Readers will gain an objective overview on the strength and shortfalls of PET–MRI or PET–CT in the management of prostate cancer.

Total Kidney Volume (TKV) is essential for analyzing the progressive loss of renal function in Autosomal Dominant Polycystic Kidney Disease (ADPKD). Conventionally, to measure TKV from medical images, a radiologist needs to localize and segment the kidneys by defining and delineating the kidney’s boundary slice by slice. However, kidney localization is a time-consuming and challenging task considering the unstructured medical images from big data such as Contrast-enhanced Computed Tomography (CCT). This study aimed to design an automatic localization model of ADPKD using Artificial Intelligence. A robust detection model using CCT images, image preprocessing, and Single Shot Detector (SSD) Inception V2 Deep Learning (DL) model is designed here. The model is trained and evaluated with 110 CCT images that comprise 10,078 slices. The experimental results showed that our derived detection model outperformed other DL detectors in terms of Average Precision (AP) and mean Average Precision (mAP). We achieved mAP = 94% for image-wise testing and mAP = 82% for subject-wise testing, when threshold on Intersection over Union (IoU) = 0.5. This study proves that our derived automatic detection model can assist radiologist in locating and classifying the ADPKD kidneys precisely and rapidly in order to improve the segmentation task and TKV calculation.

Background: Total Kidney Volume (TKV) is widely used globally to predict the progressive loss of renal function in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Typically, TKV is calculated using Computed Tomography (CT) images by manually locating, delineating, and segmenting the ADPKD kidneys. However, manual localization and segmentation are tedious, time-consuming tasks and are prone to human error. Specifically, there is a lack of studies that focus on CT modality variation. Methods: In contrast, our work develops a step-by-step framework, which robustly handles both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) images, ensuring balanced sample utilization and consistent performance across modalities. To achieve this, Artificial Intelligence (AI)-enabled localization and segmentation models are proposed for estimating TKV, which is designed to work robustly on both NCCT and Contrast-Computed Tomography (CCT) images. These AI-based models incorporate various image preprocessing techniques, including dilation and global thresholding, combined with Deep Learning (DL) approaches such as the adapted Single Shot Detector (SSD), Inception V2, and DeepLab V3+. Results: The experimental results demonstrate that the proposed AI-based models outperform other DL architectures, achieving a mean Average Precision (mAP) of $95\%$ for automatic localization, a mean Intersection over Union (mIoU) of $92\%$ for segmentation, and a mean $R^2$ score of $97\%$ for TKV estimation. Conclusions: These results clearly indicate that the proposed AI-based models can robustly localize and segment ADPKD kidneys and estimate TKV using both NCCT and CCT images.

Background: This study aimed to elucidate the change of body composition in different clinical stages of renal cell carcinoma (RCC) by analyzing computed tomography (CT) images. Methods: We enrolled patients diagnosed with RCC in a tertiary medical center who did not mention body weight loss or symptoms of cachexia. We grouped patients into those with localized RCC and those with metastatic RCC. Analyses of the volume of skeletal muscles tissue (SMT), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT) calculated based on CT images were performed and included subgroup analyses by sex and age. The correlation between tumor size and body composition in localized RCC was also examined. Results: A total of 188 patients were enrolled in this study. There was significantly lower VAT (p = 0.015) in the metastatic group than in the localized group. SAT, body weight, and body mass index (BMI) were not significantly different between these two groups. In the subgroup analysis, a significant difference in SMT and VAT was noted in the male and younger subgroups but not in the female and older subgroups. Regarding primary tumor size in localized RCC, VAT was significantly higher in patients with larger tumors (p = 0.003). Conclusions: In localized RCC, VAT volume was significantly larger in those with large primary tumor size. However, the VAT was significantly lower in those with metastatic status comparing to those with localized disease. The clinical course of cancers closely correlates with body composition.

Onyx is an emerging treatment modality for visceral vascular malformations, especially in cases in which delicate nidal penetration of the arteriovenous malformation (AVM) is desired. A computed tomography (CT) image presentation of hyperdense striations along the renal medulla secondary to the tantalum powder has not been previously reported. A 65-year-old woman presented to our institution with intermittent gross hematuria and left flank pain for 10 days. Both CT and conventional angiographies confirmed cirsoid-type renal AVM, which was successfully treated with Onyx. Follow-up CT after treatment revealed presence of hyperdense striations along the renal medulla, which resolved during later image follow-up. Despite its frequent usage in neural intervention, the application of Onyx in visceral AVM is gradually gaining interest, especially in cases in which delicate nidal penetration of the AVM is desired. Renal hyperdense striation sign should be recognized to avoid confusion with embolizer migration, and further studies in patients with renal function impairment may be helpful in understanding its influence of renal function.