Pseudocirrhosis: A Case Series with Clinical and Radiographic Correlation and Review of the Literature.

BACKGROUND AND AIMS: Pseudocirrhosis is a poorly understood acquired morphologic change of the liver that occurs in the setting of metastatic malignancy and radiographically resembles cirrhosis. Pseudocirrhosis has been primarily described in metastatic breast carcinoma, with few case reports arising from other primary malignancies. We present 29 cases of pseudocirrhosis, including several cases from primary malignancies not previously described. METHODS: Radiologic, clinical, demographic, and biomedical data were collected retrospectively and analyzed. We compared clinical and radiologic characteristics and outcomes between patients with pseudocirrhosis arising in metastatic breast cancer and non-breast primary malignancies. RESULTS: Among the 29 patients, 14 had breast cancer and 15 had non-breast primaries including previously never reported primaries associated with pseudocirrhosis, melanoma, renal cell carcinoma, appendiceal carcinoid, and cholangiocarcinoma. Median time from cancer diagnosis to development of pseudocirrhosis was 80.8 months for patients with primary breast cancer and 29.8 months for non-breast primary (p = 0.02). Among all patients, 15 (52%) had radiographic features of portal hypertension. Radiographic evidence of portal hypertension was identified in 28.6% of breast cancer patients, compared to 73.3% of those with non-breast malignancies (p = 0.03). CONCLUSION: Pseudocirrhosis has most commonly been described in the setting of metastatic breast cancer but occurs in any metastatic disease to the liver. Our study suggests that portal hypertensive complications are more common in the setting of non-breast primary cancers than in metastatic breast cancer. Prior exposure to multiple chemotherapeutic agents, and agents known to cause sinusoidal injury, is a common feature but not essential for the development of pseudocirrhosis.

Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study.

INTRODUCTION: This study investigates how quantitative texture analysis can be used to non-invasively identify novel radiogenomic correlations with clear cell renal cell carcinoma (ccRCC) biomarkers. METHODS: The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma open-source database was used to identify 190 sets of patient genomic data that had corresponding multiphase contrast-enhanced CT images in The Cancer Imaging Archive. 2,824 radiomic features spanning fifteen texture families were extracted from CT images using a custom-built MATLAB software package. Robust radiomic features with strong inter-scanner reproducibility were selected. Random forest, AdaBoost, and elastic net machine learning (ML) algorithms evaluated the ability of the selected radiomic features to predict the presence of 12 clinically relevant molecular biomarkers identified from the literature. ML analysis was repeated with cases stratified by stage (I/II vs. III/IV) and grade (1/2 vs. 3/4). 10-fold cross validation was used to evaluate model performance. RESULTS: Before stratification by tumor grade and stage, radiomics predicted the presence of several biomarkers with weak discrimination (AUC 0.60-0.68). Once stratified, radiomics predicted KDM5C, SETD2, PBRM1, and mTOR mutation status with acceptable to excellent predictive discrimination (AUC ranges from 0.70 to 0.86). CONCLUSIONS: Radiomic texture analysis can potentially identify a variety of clinically relevant biomarkers in patients with ccRCC and may have a prognostic implication.

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

OBJECTIVES: Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. MATERIALS AND METHODS: Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC. RESULTS: Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively. CONCLUSION: Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. KEY POINTS: • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.

OBJECTIVE: The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity. MATERIALS AND METHODS: A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps. RESULTS: A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family. CONCLUSION: The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.

Texture Analysis of Imaging: What Radiologists Need to Know.

OBJECTIVE: Radiologic texture is the variation in image intensities within an image and is an important part of radiomics. The objective of this article is to discuss some parameters that affect the performance of texture metrics and propose recommendations that can guide both the design and evaluation of future radiomics studies. CONCLUSION: A variety of texture-extraction techniques are used to assess clinical imaging data. Currently, no consensus exists regarding workflow, including acquisition, extraction, or reporting of variable settings leading to poor reproducibility.

Reliability of CT-based texture features: Phantom study.

OBJECTIVE: To determine the intra-, inter- and test-retest variability of CT-based texture analysis (CTTA) metrics. MATERIALS AND METHODS: In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust. RESULTS: As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used. CONCLUSION: Following large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

Differentiation of Predominantly Solid Enhancing Lipid-Poor Renal Cell Masses by Use of Contrast-Enhanced CT: Evaluating the Role of Texture in Tumor Subtyping.

OBJECTIVE: The purpose of this study was to assess the accuracy of a panel of texture features extracted from clinical CT in differentiating benign from malignant solid enhancing lipid-poor renal masses. MATERIALS AND METHODS: In a retrospective case-control study of 174 patients with predominantly solid nonmacroscopic fat-containing enhancing renal masses, 129 cases of malignant renal cell carcinoma were found, including clear cell, papillary, and chromophobe subtypes. Benign renal masses-oncocytoma and lipid-poor angiomyolipoma-were found in 45 patients. Whole-lesion ROIs were manually segmented and coregistered from the standard-of-care multiphase contrast-enhanced CT (CECT) scans of these patients. Pathologic diagnosis of all tumors was obtained after surgical resection. CECT images of the renal masses were used as inputs to a CECT texture analysis panel comprising 31 texture metrics derived with six texture methods. Stepwise logistic regression analysis was used to select the best predictor among all candidate predictors from each of the texture methods, and their performance was quantified by AUC. RESULTS: Among the texture predictors aiding renal mass subtyping were entropy, entropy of fast-Fourier transform magnitude, mean, uniformity, information measure of correlation 2, and sum of averages. These metrics had AUC values ranging from good (0.80) to excellent (0.98) across the various subtype comparisons. The overall CECT-based tumor texture model had an AUC of 0.87 (p < 0.05) for differentiating benign from malignant renal masses. CONCLUSION: The CT texture statistical model studied was accurate for differentiating benign from malignant solid enhancing lipid-poor renal masses.

Does Computed Tomography Still Have Limitations to Distinguish Benign from Malignant Renal Tumors for Radiologists?

OBJECTIVES: To evaluate the current accuracy of CT for diagnosing benign renal tumors. MATERIALS AND METHODS: We retrospectively reviewed 905 patients who underwent preoperative CT followed by surgical resection. The final pathology was benign in 156 patients (17%). After exclusions, 140 patients with 163 benign tumors were included and 3 sets of the CT interpretations by radiologists with varying levels of experience were analyzed. RESULTS: The histological breakdown was as follows: oncocytomas (54.6%), angiomyolipomas (AMLs; 30.7%), renal cysts (8.0%), other miscellaneous benign tumors (6.7%). The sensitivities of diagnosing oncocytomas were 3.4, 9.0, and 13.5% in primary radiological reports, second blinded reviews, and third non-blinded reviews, respectively (p = 0.055). The sensitivities of diagnosing AMLs were 46.0, 58.0, and 62.0% in the 3-sets of CT interpretations, respectively (p = 0.246). As for renal cysts, the sensitivities were 69.2, 92.3, and 100% in the 3-sets of CT interpretations, respectively (p = 0.051). In primary reports, the positive predictive values were 95.8% in lipid poor (lp)-AMLs, 60.0% in oncocytomas, 69.2% in renal cysts, respectively (p < 0.05). CONCLUSIONS: Current conventional CT imaging still has limitations in differentiating oncocytomas and lp-AMLs from renal cell carcinomas, even when images were re-examined by experienced radiologists.

Contrast-enhanced ultrasound (CEUS) of cystic and solid renal lesions: a review.

Incidentally detected renal lesions have traditionally undergone imaging characterization by contrast-enhanced computer tomography (CECT) or magnetic resonance imaging. Contrast-enhanced ultrasound (CEUS) of renal lesions is a relatively novel, but increasingly utilized, diagnostic modality. CEUS has advantages over CECT and MRI including unmatched temporal resolution due to continuous real-time imaging, lack of nephrotoxicity, and potential cost savings. CEUS has been most thoroughly evaluated in workup of complex cystic renal lesions, where it has been proposed as a replacement for CECT. Using CEUS to differentiate benign from malignant solid renal lesions has also been studied, but has proven difficult due to overlapping imaging features. Monitoring minimally invasive treatments of renal masses is an emerging application of CEUS. An additional promising area is quantitative analysis of renal masses using CEUS. This review discusses the scientific literature on renal CEUS, with an emphasis on imaging features differentiating various cystic and solid renal lesions.

Quantitative assessment of solid renal masses by contrast-enhanced ultrasound with time-intensity curves: how we do it.

PURPOSE: To discuss the evaluation of the enhancement curve over time of the major renal cell carcinoma (RCC) subtypes, oncocytoma, and lipid-poor angiomyolipoma, to aid in the preoperative differentiation of these entities. Differentiation of these lesions is important, given the different prognoses of the subtypes, as well as the desire to avoid resecting benign lesions. METHODS: We discuss findings from CT, MR, and US, but with a special emphasis on contrast-enhanced ultrasound (CEUS). CEUS technique is described, as well as time-intensity curve analysis. RESULTS: Examples of each of the major RCC subtypes (clear cell, papillary, and chromophobe) are shown, as well as examples of oncocytoma and lipid-poor angiomyolipoma. For each lesion, the time-intensity curve of enhancement on CEUS is reviewed, and correlated with the enhancement curve over time reported for multiphase CT and MR. CONCLUSIONS: Preoperative differentiation of the most common solid renal masses is important, and the time-intensity curves of these lesions show some distinguishing features that can aid in this differentiation. The use of CEUS is increasing, and as a modality it is especially well suited to the evaluation of the time-intensity curve.

Contrast-Enhanced Sonography for Monitoring Neoadjuvant Chemotherapy in Soft Tissue Sarcomas.

Neoadjuvant chemotherapy is a mainstay in treating soft tissue sarcomas. Soft tissue sarcomas can show an increase in size and central necrosis, with a decrease in the viable tumor, as an initial response to neoadjuvant chemotherapy. Thus, the maximum tumor diameter may not reliably assess the response to this therapy. Contrast-enhanced sonography may address this limitation. We evaluated 4 patients with soft tissue sarcomas by contrast-enhanced sonography, performed concomitantly with conventional imaging (computed tomography, magnetic resonance imaging, or positron emission tomography). Quantitative analysis was also performed on 1 sarcoma. A viable, enhancing tumor versus tumor necrosis was nearly identical on contrast-enhanced sonography and conventional imaging. Preliminary results demonstrate potential for contrast-enhanced sonographic monitoring of soft tissue sarcomas during neoadjuvant chemotherapy.

Pulmonary pseudoemboli: a new artifact arising from a commercial metal artifact reduction algorithm for computed tomographic image reconstruction.

Metal artifact reduction algorithms for computed tomographic (CT) image reconstruction have recently become commercially available on modern CT scanners for reducing artifacts from orthopedic hardware. However, we have observed that a commercial orthopedic metal artifact reduction algorithm can produce the appearance of artifactual pulmonary emboli when applied to spinal hardware in contrast-enhanced CT scans of the chest. We provide 4 case examples demonstrating this previously undescribed artifact.

Automated pediatric abdominal effective diameter measurements versus age-predicted body size for normalization of CT dose.

There has been increasing interest in adjusting CT radiation dose data for patient body size. A method for automated computation of the abdominal effective diameter of a patient from a CT image has previously only been tested in adult patients. In this work, we tested the method on a set of 128 pediatric patients aged 0.8 to 12.9 years (average 8.0 years, SD = 3.7 years) who had CT abdomen/pelvis exams performed on a Toshiba Aquilion 64 scanner. For this set of patients, age-predicted abdominal effective diameter extrapolated based on data from the International Commission on Radiation Units and Measurements was a relatively poor predictor of measured effective diameter. The mean absolute percentage error between the CTDI normalization coefficient calculated from a manually measured effective diameter and the coefficient determined by age-predicted effective diameter was 12.3 % with respect to a 32 cm phantom (range 0.0-52.8 %, SD 8.7 %) and 12.9 % with respect to a 16 cm phantom (range 0.0-56.4 %, SD 9.2 %). In contrast, there is a close correspondence between the automated and manually measured patient effective diameters, with a mean absolute error of 0.6 cm (error range 0.2-1.3 cm). This correspondence translates into a high degree of correspondence between normalization coefficients determined by automated and manual measurements; the mean absolute percentage error was 2.1 % with respect to a 32 cm phantom (range 0.0-8.1 %, SD = 1.4 %) and 2.3 % with respect to a 16 cm phantom (range 0.0-9.3 %, SD = 1.6 %).

Cost-effectiveness of Contrast-Enhanced Ultrasound for Diagnosis and Active Surveillance of Complex Cystic Renal Lesions.

Purpose: To determine the cost-effectiveness of Contrast-Enhanced Ultrasound (ceUS) for the active surveillance of complex renal masses compared to the more established imaging modalities of CT and MRI. Methods: A decision-analytic Markov state microsimulation model was constructed in TreeAge Pro. We simulated independent cohorts of 100,000 60-year-old individuals with either a Bosniak IIF or Bosniak III complex renal mass who were followed for 10 years or until death. The model compared three imaging strategies: (1) ceUS, (2) contrast-enhanced magnetic-resonance imaging (ceMRI), and (3) contrast-enhanced computed tomography (ceCT) for active surveillance of a complex renal mass. Results: For 60-year-old patients with either Bosniak IIF or III renal masses, ceUS was the most cost-effective strategy even after varying rates of active surveillance from 10-100%. Conclusion: ceUS is a viable and cost-effective option in the active surveillance of Bosniak class IIF and III renal cysts. Even after varying the rates of active surveillance usage, ceUS was robust and remained the most dominant strategy. For patients who have impaired kidney functions, ceUS is can be a safer alternative than non-contrast enhanced CT or MRI in the management of patients with Bosniak III renal cysts.

Localized Multifocal Retroperitoneal Ganglioneuroma with an Infiltrative Appearance on Imaging: A Case Report.

Multifocal ganglioneuromas are characterized by the presence of multiple benign neuroepithelial tumor nodules and are less common than solitary tumors. A small percentage of ganglioneuromas present with a fatty appearance. Only a few cases of multifocal ganglioneuromas have been reported, due to both their rarity and minimal symptomatic presentation; therefore, generalizations about risk factors and predictive markers are very difficult. Here, we report a case of multifocal retroperitoneal ganglioneuroma with an infiltrative appearance on computed tomography (CT). The tumor demonstrated slow growth on multiple imaging studies and was associated with abdominal and flank pain. The aggressive appearance eventually led to surgical resection 18 months after the initial incidental finding on CT. Postsurgical analysis of the tumor on imaging was crucial in revealing its nodularity and infiltration, as well as for clarifying its retroperitoneal location inseparable from the adrenal gland. Histology demonstrated Schwann cells and ganglion cells without atypia or increased cellularity, and with no mitosis or necrosis seen. Our case highlights the consideration of ganglioneuroma with fatty infiltration in the differential diagnosis of a fatty tumor in the mediastinum or retroperitoneum. Additionally, our report differentiates multifocal ganglioneuroma with fatty infiltration from lipomatous ganglioneuroma on radiology and histopathology.

Comprehensive Systematic Review of Biomarkers in Metastatic Renal Cell Carcinoma: Predictors, Prognostics, and Therapeutic Monitoring.

BACKGROUND: Challenges remain in determining the most effective treatment strategies and identifying patients who would benefit from adjuvant or neoadjuvant therapy in renal cell carcinoma. The objective of this review is to provide a comprehensive overview of biomarkers in metastatic renal cell carcinoma (mRCC) and their utility in prediction of treatment response, prognosis, and therapeutic monitoring in patients receiving systemic therapy for metastatic disease. METHODS: A systematic literature search was conducted using the PubMed database for relevant studies published between January 2017 and December 2022. The search focused on biomarkers associated with mRCC and their relationship to immune checkpoint inhibitors, targeted therapy, and VEGF inhibitors in the adjuvant, neoadjuvant, and metastatic settings. RESULTS: The review identified various biomarkers with predictive, prognostic, and therapeutic monitoring potential in mRCC. The review also discussed the challenges associated with anti-angiogenic and immune-checkpoint monotherapy trials and highlighted the need for personalized therapy based on molecular signatures. CONCLUSION: This comprehensive review provides valuable insights into the landscape of biomarkers in mRCC and their potential applications in prediction of treatment response, prognosis, and therapeutic monitoring. The findings underscore the importance of incorporating biomarker assessment into clinical practice to guide treatment decisions and improve patient outcomes in mRCC.

Spatial assessments in texture analysis: what the radiologist needs to know.

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.

Introduction: Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT). Method: The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review. Results: The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9. Discussion: Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

What the radiologist needs to know about urolithiasis: part 1--pathogenesis, types, assessment, and variant anatomy.

OBJECTIVE: This article reviews types of urinary calculi and their imaging appearances, presents direct and secondary imaging findings of urolithiasis, and provides an overview of treatment methods. Pertinent imaging findings that impact clinical management are highlighted. The implications of complex or variant genitourinary anatomy are reviewed. We outline a standard format for the reporting of urolithiasis to facilitate informed clinical management decisions. CONCLUSION: Unenhanced CT is the preferred examination for evaluation of urolithiasis because of its availability, ease of performance, and high sensitivity. An awareness of the important imaging findings to report allows appropriate and efficient therapy.

What the radiologist needs to know about urolithiasis: part 2--CT findings, reporting, and treatment.

OBJECTIVE: This article reviews types of urinary calculi and their imaging appearances, presents direct and secondary imaging findings of urolithiasis, and provides an overview of treatment methods. Pertinent imaging findings that affect clinical management are highlighted. The implications of complex or variant genitourinary anatomy are reviewed. We outline a standard format for the reporting of urolithiasis to facilitate informed clinical management decisions. CONCLUSION: Unenhanced CT is the preferred examination for evaluation of urolithiasis because of its availability, ease of performance, and high sensitivity. An awareness of the important imaging findings to report allows appropriate and efficient therapy.