Multiphase renal CT in the evaluation of renal masses: is the nephrographic phase necessary?

Customised weight-based volume contrast media protocol for multiphase abdominal computed tomography

INTRODUCTION

Multiphase computed tomography (CT) using fixed volume contrast media may lead to high radiation exposure and toxicity in patients with low body weight. We evaluated a customised weight-based protocol for multiphase CT in terms of radiation exposure, image quality and cost savings.

METHODS

A total of 224 patients were recruited. An optimised CT protocol was applied using 100 kV and 1 mL/kg of contrast media dosing. The image quality and radiation dose exposure of this CT protocol were compared to those of a standard 120 kV, 80 mL fixed volume protocol. The radiation dose information and CT Hounsfield units were recorded. The signal-to-noise ratio, contrast-to-noise ratio (CNR) and figure of merit (FOM) were used as comparison metrics. The images were assessed for contrast opacification and visual quality by two radiologists. The renal function, contrast media volume and cost were also evaluated.

RESULTS

The median effective dose was lowered by 16% in the optimised protocol, while the arterial phase images achieved significantly higher CNR and FOM. The radiologists' evaluation showed more than 97% absolute agreement with no significant differences in image quality. No significant differences were found in the pre- and post-CT estimated glomerular filtration rate. However, contrast media usage was significantly reduced by 1,680 mL, with an overall cost savings of USD 421 in the optimised protocol.

CONCLUSION

The optimised weight-based protocol is cost-efficient and lowers radiation dose while maintaining overall contrast enhancement and image quality.

Computed tomography for visible haematuria - a single nephrographic phase is sufficient for detecting renal cell carcinoma

OBJECTIVES

No previous studies have compared two computed tomography (CT) protocols in patients presenting with visible haematuria, and most patients undergo a multiphase CT in order to detect upper tract malignancies. We aimed to prospectively compare the diagnostic performance of single- and four-phase CT for detecting renal cell carcinoma (RCC) in patients with visible haematuria.

MATERIALS & METHODS

'A Prospective Trial for Examining Hematuria using Computed Tomography' (PROTEHCT) was a single-centre prospective paired diagnostic study in patients referred for CT due to painless visible haematuria between September 2019 and June 2021. All patients underwent four-phase CT (control) from which a single nephrographic phase dual energy CT (experimental) was extracted. Both were independently assessed for RCC by randomised radiologists. Histologically verified RCC defined a positive reference standard. Follow-up ascertainment of RCC diagnosis was completed in May 2022. Descriptive statistics were used to calculate the accuracies. Inter-reader agreement was assessed by kappa statistics.

RESULTS

A total of 308 patients (median age, 68 years [interquartile range 53-77, range 18-96], 250 males) were included for analysis. RCC was diagnosed in seven (2.3%) patients during a median follow-up time of 19 months (interquartile range: 15-25). For the control and experimental CT, sensitivity was 100% versus 100%, specificity was 97% versus 98% and accuracy 97% versus 97%. The positive predictive value was 44% versus 50%, and the negative predictive value was 100% versus 100%. The agreement between the control and experimental CT was 98% (k = 0.79).

CONCLUSION

A single nephrographic phase dual energy CT is sufficient for detecting RCC in patients with visible haematuria.

Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network

OBJECTIVES

To develop a 3D U-Net-based deep learning model for automated segmentation of kidney and renal mass, and detection of renal mass in corticomedullary phase of computed tomography urography (CTU).

METHODS

Data on 882 kidneys obtained from CTU data of 441 patients with renal mass were used to learn and evaluate the deep learning model. The CTU data of 35 patients with small renal tumors (diameter ≤ 1.5 cm) were used for additional testing. The ground truth data for the kidney, renal tumor, and cyst were manually annotated on corticomedullary phase images of CTU. The proposed segmentation model for kidney and renal mass was constructed based on a 3D U-Net. The segmentation accuracy was evaluated through the Dice similarity coefficient (DSC). The volume of the maximum 3D volume of interest of renal tumor and cyst in the predicted segmentation by the model was used as an identification indicator, while the detection performance of the model was evaluated by the area under the receiver operation characteristic curve.

RESULTS

The proposed model showed a high accuracy in segmentation of kidney and renal tumor, with average DSC of 0.973 and 0.844, respectively. It performed moderately in the renal cyst segmentation, with an average DSC of 0.536 in the test set. Also, this model showed good performance in detecting renal tumor and cyst.

CONCLUSIONS

The proposed automated segmentation and detection model based on 3D U-Net shows promising results for the segmentation of kidney and renal tumor, and the detection of renal tumor and cyst.

KEY POINTS

• The segmentation model based on 3D U-Net showed high accuracy in segmentation of kidney and renal neoplasm, and good detection performance of renal neoplasm and cyst in corticomedullary phase of CTU. • The segmentation model based on 3D U-Net is a fully automated aided diagnostic tool that could be used to reduce the workload of radiologists and improve the accuracy of diagnosis. • The segmentation model based on 3D U-Net would be helpful to provide quantitative information for diagnosis, treatment, surgical planning, etc.

Comparison of post contrast CT urography phases in bladder cancer detection

OBJECTIVES

The aim of this study was to investigate which post-contrast phase(s) in a four-phase CT urography protocol is (are) most suitable for bladder cancer detection.

METHODS

The medical records of 106 patients with visible haematuria who underwent a CT urography examination, including unenhanced, enhancement-triggered corticomedullary (CMP), nephrographic (NP) and excretory (EP) phases, were reviewed. The post-contrast phases (n = 318 different phases) were randomized into an evaluation order and blindly reviewed by two uroradiologists.

RESULTS

Twenty-one patients were diagnosed with bladder cancer. Sensitivity for bladder cancer detection was 0.95 in CMP, 0.83 in NP and 0.81 in EP. Negative predictive value (NPV) was 0.99 in CMP, 0.96 in NP and 0.95 in EP. The sensitivity was higher in CMP than in both NP (p-value 0.016) and EP (p-value 0.0003). NPV was higher in CMP than in NP (p-value 0.024) and EP (p-value 0.002).

CONCLUSIONS

In the CT urography protocol with enhancement-triggered scan, sensitivity and NPV were highest in the corticomedullary phase, and this phase should be used for bladder assessment.

KEY POINTS

• More bladder tumours are detected during the corticomedullary phase than during other phases. • Sensitivity and NPV for bladder cancer detection were highest in corticomedullary phase. • The corticomedullary phase in CT urography should be used for bladder assessment.