Safety profile and technical success of narrow window CT-guided percutaneous biopsy with blunt needle approach in the abdomen and pelvis

OBJECTIVE

To assess success and safety of CT-guided procedures with narrow window access for biopsy.

METHODS

Three hundred ninety-six consecutive patients undergoing abdominal or pelvic CT-guided biopsy or fiducial placement between 01/2015 and 12/2018 were included (183 women, mean age 63 ± 14 years). Procedures were classified into "wide window" (width of the needle path between structures > 15 mm) and "narrow window" (≤ 15 mm) based on intraprocedural images. Clinical information, complications, technical and clinical success, and outcomes were collected. The blunt needle approach is preferred by our interventional radiology team for narrow window access.

RESULTS

There were 323 (81.5%) wide window procedures and 73 (18.5%) narrow window procedures with blunt needle approach. The median depth for the narrow window group was greater (97 mm, interquartile range (IQR) 82-113 mm) compared to the wide window group (84 mm, IQR 60-106 mm); p = 0.0017. Technical success was reached in 100% (73/73) of the narrow window and 99.7% (322/323) of the wide window procedures. There was no difference in clinical success rate between the two groups (narrow: 86.4%, 57/66; wide: 89.5%, 265/296; p = 0.46). There was no difference in immediate complication rate (narrow: 1.3%, 1/73; wide: 1.2%, 4/323; p = 0.73) or delayed complication rate (narrow: 1.3%, 1/73; wide: 0.6%, 1/323; p = 0.50).

CONCLUSION

Narrow window (< 15 mm) access biopsy and fiducial placement with blunt needle approach under CT guidance is safe and successful.

CLINICAL RELEVANCE STATEMENT

CT-guided biopsy and fiducial placement can be performed through narrow window access of less than 15 mm utilizing the blunt-tip technique.

KEY POINTS

• A narrow window for CT-guided abdominal and pelvic biopsies and fiducial placements was considered when width of the needle path between vital structures was ≤ 15 mm. • Seventy-three biopsies and fiducial placements performed through a narrow window with blunt needle approach had a similar rate of technical and clinical success and complications compared to 323 procedures performed through a wide window approach, with traditional approach (> 15 mm). • This study confirmed the safety of the CT-guided percutaneous procedures through < 15 mm window with blunt-tip technique.

Routine radiology-pathology concordance evaluation of CT-guided percutaneous lung biopsies increases the number of cancers identified

BACKGROUND

Routine concordance evaluation between pathology and imaging findings was introduced for CT-guided biopsies.

PURPOSE

To analyze malignancy rate in concordant, discordant, and indeterminate non-malignant results of CT-guided lung biopsies.

METHODS

Concordance between pathology results and imaging findings of consecutive patients undergoing CT-guided lung biopsy between 7/1/2016 and 9/30/2021 was assessed during routine meetings by procedural radiologists. Concordant was defined as pathology consistent with imaging findings; discordant was used when pathology could not explain imaging findings; indeterminate when pathology could explain imaging findings but there was concern for malignancy. Recommendations for discordant and indeterminate were provided. All the malignant results were concordant. Pathology of repeated biopsy, surgical sample, or follow-up was considered reference standard.

RESULTS

Consecutive 828 CT-guided lung biopsies were performed on 795 patients (median age 70 years, IQR 61-77), 423/828 (51%) women. On pathology, 224/828 (27%) were non-malignant. Among the non-malignant, radiology-pathology concordance determined 138/224 (62%) to be concordant with imaging findings, 54/224 (24%) discordant, and 32/224 (14%) indeterminate. When compared to the reference standard, 33/54 (61%) discordant results, 6/30 (20%) indeterminate, and 3/133 (2%) concordant were malignant. The prevalence of malignancy in the three groups was significantly different (p < 0.001). Time to diagnosis was significantly different between patients who reached the diagnosis with imaging follow-up (median 114 days, IQR 69-206) compared to repeat biopsy (33 days, IQR 18-133) (p = 0.01).

CONCLUSION

Routine radiology-pathology concordance evaluation of CT-guided lung biopsy correctly identifies patients at high risk for missed diagnosis of malignancy. Repeat biopsy is the fastest method to reach diagnosis.

CLINICAL RELEVANCE STATEMENT

A routine radiology-pathology concordance assessment identifies patients with non-malignant CT-guided lung biopsy result who are at greater risk of missed diagnosis of malignancy.

KEY POINTS

• A routine radiology-pathology concordance evaluation of CT-guided lung biopsies classified 224 non-malignant results as concordant, discordant, or indeterminate. • The percentage of malignancy on follow-up was significantly different in concordant (2%), discordant (61%), and indeterminate (20%) (p < 0.001). • Time to definitive diagnosis was significantly shorter with repeat biopsy (33 days), compared to imaging follow-up (114 days), p = 0.01.

Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients

OBJECTIVES

Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging.

METHODS

Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35-62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019-12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)-100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling.

RESULTS

Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 (p < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms.

CONCLUSIONS

The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it.

CLINICAL RELEVANCE STATEMENT

Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads.

KEY POINTS

• Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data. • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality. • Image domain algorithms can generalize well and can be implemented at other institutions.

Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma

OBJECTIVE

To develop and evaluate task-based radiomic features extracted from the mesenteric-portal axis for prediction of survival and response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Consecutive patients with PDAC who underwent surgery after neoadjuvant therapy from two academic hospitals between December 2012 and June 2018 were retrospectively included. Two radiologists performed a volumetric segmentation of PDAC and mesenteric-portal axis (MPA) using a segmentation software on CT scans before (CTtp0) and after (CTtp1) neoadjuvant therapy. Segmentation masks were resampled into uniform 0.625-mm voxels to develop task-based morphologic features (n = 57). These features aimed to assess MPA shape, MPA narrowing, changes in shape and diameter between CTtp0 and CTtp1, and length of MPA segment affected by the tumor. A Kaplan-Meier curve was generated to estimate the survival function. To identify reliable radiomic features associated with survival, a Cox proportional hazards model was used. Features with an ICC  ≥ 0.80 were used as candidate variables, with clinical features included a priori.

RESULTS

In total, 107 patients (60 men) were included. The median survival time was 895 days (95% CI: 717, 1061). Three task-based shape radiomic features (Eccentricity mean tp0, Area minimum value tp1, and Ratio 2 minor tp1) were selected. The model showed an integrated AUC of 0.72 for prediction of survival. The hazard ratio for the Area minimum value tp1 feature was 1.78 (p = 0.02) and 0.48 for the Ratio 2 minor tp1 feature (p = 0.002).

CONCLUSION

Preliminary results suggest that task-based shape radiomic features can predict survival in PDAC patients.

KEY POINTS

• In a retrospective study of 107 patients who underwent neoadjuvant therapy followed by surgery for PDAC, task-based shape radiomic features were extracted and analyzed from the mesenteric-portal axis. • A Cox proportional hazards model that included three selected radiomic features plus clinical information showed an integrated AUC of 0.72 for prediction of survival, and a better fit compared to the model with only clinical information.

Is it possible to use low-dose deep learning reconstruction for the detection of liver metastases on CT routinely?

OBJECTIVES

To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR).

METHODS

A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR.

RESULTS

The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: -0.112; 95% confidence interval [CI]: -0.178 to 0.047) and full-dose IR (difference: -0.123; 95% CI: -0.182 to 0.053) (p < 0.001).

CONCLUSION

DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR.

KEY POINTS

• Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).

Automated versus manual analysis of body composition measures on computed tomography in patients with bladder cancer

PURPOSE

Manual measurement of body composition on computed tomography (CT) is time-consuming, limiting its clinical use. We validate a software program, Automatic Body composition Analyzer using Computed tomography image Segmentation (ABACS), for the automated measurement of body composition by comparing its performance to manual segmentation in a cohort of patients with bladder cancer.

METHOD

We performed a retrospective analysis of 285 patients treated for bladder cancer at the Duke University Health System from 1996 to 2017. Abdominal CT images were manually segmented at L3 using Slice-O-Matic. Automated segmentation was performed with ABACS on the same L3-level images. Measures of interest were skeletal muscle (SM) area, subcutaneous adipose tissue (SAT) area, and visceral adipose tissue (VAT) area. SM index, SAT index, and VAT index were calculated by dividing component areas by patient height2 (m2). Patients were dichotomized as sarcopenic, having excessive subcutaneous fat, or having excessive visceral fat using published cut-off values. Agreement between manual and automated segmentation was assessed using the Pearson product-moment correlation coefficient (PPMCC), the interclass correlation coefficient (ICC3), and the kappa statistic (κ).

RESULTS

There was strong agreement between manual and automatic segmentation, with PPMCCs > 0.90 and ICC3s > 0.90 for SM, SAT, and VAT areas. Categorization of patients as sarcopenic (κ = 0.73), having excessive subcutaneous fat (κ = 0.88), or having excessive visceral fat (κ = 0.90) displayed high agreement between methods.

CONCLUSIONS

Automated segmentation of body composition measures on CT using ABACS performs similarly to manual analysis and may expedite data collection in body composition research.

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

BACKGROUND

The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown.

PURPOSE

To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions.

MATERIAL AND METHODS

A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II-IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions.

RESULTS

The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934-0.979) as well as in the test dataset (AUCs = 0.892-0.962) of cohort B (P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images (P = 0.038) in the training dataset of cohort B.

CONCLUSION

No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

Pericardial Adipose Tissue Volume and Left Ventricular Assist Device-Associated Outcomes

BACKGROUND

Pericardial adipose tissue (PAT) is associated with adverse cardiovascular outcomes in those with and without established heart failure (HF). However, it is not known whether PAT is associated with adverse outcomes in patients with end-stage HF undergoing left ventricular assist device (LVAD) implantation. This study aimed to evaluate the associations between PAT and LVAD-associated outcomes.

METHODS AND RESULTS

We retrospectively measured computed tomography-derived PAT volumes in 77 consecutive adults who had available chest CT imaging prior to HeartMate 3 LVAD surgery between October 2015 and March 2019 at Duke University Hospital. Study groups were divided into above-median (≥219 cm³) and below-median (<219 cm³) PAT volume. Those with above-median PAT had a higher proportion of atrial fibrillation, chronic kidney disease and ischemic cardiomyopathy. Groups with above-median vs below-median PAT had similar Kaplan-Meier incidence rates over 2 years for (1) composite all-cause mortality, redo-LVAD surgery and cardiac transplantation (35.9 vs 32.2%; log-rank P = 0.65) and (2) composite incident hospitalizations for HF, gastrointestinal bleeding, LVAD-related infection, and stroke (61.5 vs 60.⁵%; log-rank P = 0.67).

CONCLUSIONS

In patients with end-stage HF undergoing LVAD therapy, PAT is not associated with worse 2-year LVAD-related outcomes. The significance of regional adiposity vs obesity in LVAD patients warrants further investigation.

CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study

Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preoperative assessment of arterial involvement in patients with surgically proven PDAC. Materials and Methods This retrospective study included consecutive patients with PDAC who underwent surgery after preoperative CT between 2012 and 2019. A three-dimensional segmentation of PDAC and perivascular tissue surrounding the superior mesenteric artery (SMA) was performed on preoperative CT images with radiomic features extracted to characterize morphology, intensity, texture, and task-based spatial information. The reference standard was the pathologic SMA margin status of the surgical sample: SMA involved (tumor cells ≤1 mm from margin) versus SMA not involved (tumor cells >1 mm from margin). The preoperative assessment of SMA involvement by a fellowship-trained radiologist in multidisciplinary consensus was the comparison. High reproducibility (intraclass correlation coefficient, 0.7) and the Kolmogorov-Smirnov test were used to select features included in the logistic regression model. Results A total of 194 patients (median age, 66 years; interquartile range, 60-71 years; age range, 36-85 years; 99 men) were evaluated. Aside from surgery, 148 patients underwent neoadjuvant therapy. A total of 141 patients' samples did not involve SMA, whereas 53 involved SMA. A total of 1695 CT radiomic features were extracted. The model with five features (maximum hugging angle, maximum diameter, logarithm robust mean absolute deviation, minimum distance, square gray level co-occurrence matrix correlation) showed a better performance compared with the radiologist assessment (model vs radiologist area under the curve, 0.71 [95% CI: 0.62, 0.79] vs 0.54 [95% CI: 0.50, 0.59]; P < .001). The model showed a sensitivity of 62% (33 of 53 patients) (95% CI: 51, 77) and a specificity of 77% (108 of 141 patients) (95% CI: 60, 84). Conclusion A model based on tumor-related and perivascular CT radiomic features improved the detection of superior mesenteric artery involvement in patients with pancreatic ductal adenocarcinoma. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Do and Kambadakone in this issue.

Concordance Assessment of Pathology Results with Imaging Findings after Image-Guided Biopsy

PURPOSE

To assess the impact of radiology review for discordance between pathology results from computed tomography (CT)-guided biopsies versus imaging findings performed before a biopsy.

MATERIALS AND METHODS

In this retrospective review, which is compliant with the Health Insurance Portability and Accountability Act and approved by the institutional review board, 926 consecutive CT-guided biopsies performed between January 2015 and December 2017 were included. In total, 453 patients were presented in radiology review meetings (prospective group), and the results were classified as concordant or discordant. Results from the remaining 473 patients not presented at the radiology review meetings were retrospectively classified. Times to reintervention and to definitive diagnosis were obtained for discordant cases; of these, 49 (11%) of the 453 patients were in the prospective group and 55 (12%) of the 473 patients in the retrospective group.

RESULTS

Pathology results from CT-guided biopsies were discordant with imaging in 11% (104/926) of the cases, with 57% (59/104) of these cases proving to be malignant. In discordant cases, reintervention with biopsy and surgery yielded a shorter time to definitive diagnosis (28 and 14 days, respectively) than an imaging follow-up (78 days) (P < .001). The median time to diagnosis was 41 days in the prospective group and 56 days in the retrospective group (P = .46). When radiologists evaluated the concordance between pathology and imaging findings and recommended a repeat biopsy for the discordant cases, more biopsies were performed (50% [11/22] vs 13% [4/31]; P = .005).

CONCLUSIONS

Eleven percent of CT-guided biopsies yielded pathology results that were discordant with imaging findings, with 57% of these proving to be malignant on further workup.

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence

OBJECTIVE

To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm.

METHODS

A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics.

RESULTS

For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence.

CONCLUSIONS

The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.

Correlation of preoperative imaging characteristics with donor outcomes and operative difficulty in laparoscopic donor nephrectomy

This study aimed to understand the relationship of preoperative measurements and risk factors on operative time and outcomes of laparoscopic donor nephrectomy. Two hundred forty-two kidney donors between 2010 and 2017 were identified. Patients' demographic, anthropomorphic, and operative characteristics were abstracted from the electronic medical record. Glomerular filtration rates (GFR) were documented before surgery, within 24 hours, 6, 12, and 24 months after surgery. Standard radiological measures and kidney volumes, and subcutaneous and perinephric fat thicknesses were assessed by three radiologists. Data were analyzed using standard statistical measures. There was significant correlation between cranio-caudal and latero-lateral diameters (P < .0001) and kidney volume. The left kidney was transplanted in 92.6% of cases and the larger kidney in 69.2%. Kidney choice (smaller vs. larger) had no statistically significant impact on the rate of change of donor kidney function over time adjusting for age, sex and race (P = .61). Perinephric fat thickness (+4.08 minutes) and surgery after 2011 were significantly correlated with operative time (P ≤ .01). In conclusion, cranio-caudal diameters can be used as a surrogate measure for volume in the majority of donors. Size may not be a decisive factor for long-term donor kidney function. Perinephric fat around the donor kidney should be reported to facilitate operative planning.

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

BACKGROUND

Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.

METHODS

Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method ("training with input selection and testing") was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs).

RESULTS

A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87-100%), a specificity of 37/41 (90%, 95% CI 77-97%), and an accuracy of 64/68 (94%, 95% CI 86-98%).

CONCLUSION

This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.

Complications of sporadic, hereditary, and acquired renal cysts: cross-sectional imaging findings

Commonly encountered in the general adult and elderly population, in most cases simple renal cysts are confidently diagnosed on imaging studies and do not require further workup or treatment. However, large or growing renal cysts sometimes cause symptoms or signs such as hypertension, palpable mass, flank or abdominal pain, obstructive uropathy, and hematuria, which may indicate the need for minimally invasive percutaneous or laparoscopic treatment. Furthermore, severe complications such as cystic hemorrhage, rupture, or superinfection may occur, particularly in patients with polycystic renal disorders, either hereditary (namely adult polycystic kidney diseases) or acquired in chronic renal failure. This pictorial essay reviews and discusses the cross-sectional imaging appearances of symptomatic and complicated sporadic, hereditary, and acquired renal cysts. Early cross-sectional imaging with multidetector computed tomography or magnetic resonance imaging or both, including contrast enhancement unless contraindicated by renal dysfunction, is warranted to investigate clinical and laboratory signs suggesting retroperitoneal hemorrhage or infection in patients with pre-existent renal cysts, particularly if large, multiple, or hereditary.

Images in medicine: Spontaneous aortocaval fistula complicating abdominal aortic aneurysm

Aortocaval fistula represents a rare, life-threatening complication of abdominal aortic aneurysm that needs emergency surgical treatment. The presentation couples that of a rupturing aneurysm with other more characteristic features resulting from the massive arteriovenous shunt. Early recognition and emergency surgical treatment are essential in reducing mortality and morbidity. Prompt investigation with multidetector computed tomography (CT) angiography quickly and accurately establishes a precise preoperative diagnosis, thereby enabling proper planning of operative treatment.