The evolution in imaging has had an increasing role in the diagnosis, staging and follow up of bladder cancer. Conventional cystoscopy is crucial in the diagnosis of bladder cancer. However, a cystoscopic procedure cannot always depict carcinoma in situ (CIS) or differentiate benign from malignant tumors prior to biopsy. This review will discuss the standard application, novel imaging modalities and their additive role in patients with bladder cancer. Staging can be performed with CT, but distinguishing between T1 and T2 BCa (bladder cancer) cannot be assessed. MRI can distinguish muscle-invasive from non-muscle-invasive tumors with accurate local staging. Vesical Imaging-Reporting and Data System (VI-RADS) score is a new diagnostic modality used for the prediction of tumor aggressiveness and therapeutic response. Bone scintigraphy is recommended in patients with muscle-invasive BCa with suspected bony metastases. CT shows low sensitivity for nodal staging; however, PET (Positron Emission Tomography)/CT is superior and highly recommended for restaging and determining therapeutic effect. PET/MRI is a new imaging technique in bladder cancer imaging and its role is promising. Texture analysis has shown significant steps in discriminating low-grade from high-grade bladder cancer. Radiomics could be a reliable method for quantitative assessment of the muscle invasion of bladder cancer.

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of $95.3\%\pm 2.0\%$, a specificity of $99.9\%\pm 0.4\%$, and Dice similarity coefficient of $0.98\pm 0.01$ in differentiating malignant from benign tumors, as well as an overall accuracy of $89.6\%\pm 5.0\%$ in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

Background: Preventing acute complication of renal angiomyolipoma (AML), preserving renal parenchyma, and improving long-term renal function are the treatment targets of renal angiomyolipoma. Treatment should be considered for symptomatic lesions or those who are at risk of complications, especially bleeding symptoms, which are linked to tumor size, angiogenic component grade, and presence of tuberous sclerosis complex (TSC). Selective arterial embolization (SAE) has become the new norm for preventive or emergency treatment of renal AMLs with minimally invasive selective targeting of small arterial feeders, we aimed to assess the efficacy and safety of selective renal arterial embolization (SAE) in the management of complicated renal angiomyolipoma and to detect the predictors of prophylactic SAE in cases of non-complicated AML.Results: Bleeding symptoms were significantly more frequent in patients with TSC-associated renal AMLs (C = 0.333 andp = 0.036) and patients with intra-lesional aneurysm > 3 mm (C = 0.387 andp = 0.013). Overall success rate: thirty-three (91.7%) renal AMLs were successfully embolized with no recurrence. While three (8.3%) renal AMLs were not; one (2.8%) renal AML was not embolized due to technical failure and two (5.5%) renal AMLs showed recurrence. Primary (technical) success rate: thirty-three (86.9%) successful embolization, five (13.1%) arteriographies were done with failed embolization. The maximum diameter and volume of the lesions after SAE showed statistically significant reduction (z = 4.25 andp < 0.001).Conclusions: SAE is an effective and safe technique to manage renal AMLs preoperatively or in an emergency. TSC-associated lesions, and intra-lesional aneurysms (aneurysms > 3 mm in diameter) were significantly more associated with bleeding symptoms, considering them significant predictors for prophylactic SAE in non-complicated AML.

Objectives: To assess the role of multiparametric magnetic resonance imaging (mp-MRI) in the diagnosis and staging of urinary bladder cancer (BC). Materials and methods: Fifty patients diagnosed with bladder masses underwent mp-MRI study. The results of 3 image sets were analyzed and compared with the histopathological results as a reference standard: T2-weighted image (T2WI) plus dynamic contrast-enhanced (DCE), T2WI plus diffusion-weighted images (DWI), and mp-MRI, including T2WI plus DWI and DCE. The diagnostic accuracy of mp-MRI was evaluated using receiver operating characteristic curve analysis. Results: The accuracy of T2WI plus DCE for detecting muscle invasion of BC was 79.5% with a fair agreement with histopathological examination (κ = 0.59); this percentage increased up to 88.6% using T2WI plus DWI, with good agreement with histopathological examination (κ = 0.74), whereas mp-MRI had the highest overall accuracy (95.4%) and excellent agreement with histopathological data (κ = 0.83). Multiparametric MRI can differentiate between low- and high-grade bladder tumors with a high sensitivity and specificity of 93.3% and 98.3%, respectively. Conclusions: Multiparametric MRI is an acceptable method for the preoperative detection and accurate staging of BC, with reasonable accuracy in differentiating between low- and high-grade BC.

Nashat A, et al. Investig Clin Urol. 2025 Jan;66(1):47-55. https://doi.org/10.4111/icu.20240135

Background: Preoperative assessment of prostate cancer (PCa) aggressiveness is a prerequisite to provide specific management options. The Gleason score (GS) obtained from prostatic biopsy or surgery is crucial for the evaluation of PCa aggressiveness and personalized treatment planning. Diffusion tensor imaging (DTI) provides valuable information about microstructural properties of prostatic tissue. The most common prostate DTI measures are the fractional anisotropy (FA) and median diffusivity (MD) can give more information regarding the biophysical characteristics of prostate tissue. We aimed to explore the correlation of these DTI parameters with GS levels in PCa patients that can affect the management protocol of PCa. Results: The computed area under curve (AUC) of the FA values used to differentiate cancer patients from control group was (0.90) with cutoff point to differentiate both groups were ≥ 0.245. The computed sensitivity, specificity, positive and negative predictive values were (84%, 80%, 95.5%, and 50%), respectively, with accuracy 83.3%. FA showed high positive correlation with Gleason score (p value < 0.001). Median diffusivity (MD) showed negative correlation with GS with statistically significant results (p value = 0.013). PCa fiber bundles were dense, orderly arranged, without interruption in the low grade, and slightly disorganized in the intermediate group. However, in the high-grade group, the fiber bundles were interrupted, irregularly arranged, and absent at the site of cancerous foci. Conclusions: Combined quantitative parameter values (FA and MD values) and parametric diagrams (FA and DTI maps) can be utilized to evaluate prostate cancer aggressiveness and prognosis, helping in the improvement of the management protocol of PCa patients.