Size and volume of kidney stones in computed tomography: Influence of acquisition techniques and image reconstruction parameters

Radiomics-driven spectral profiling of six kidney stone types with monoenergetic CT reconstructions in photon-counting CT

OBJECTIVES

Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-counting computed tomography (PCCT) in combination with radiomics and machine learning to develop a method for automated and detailed characterization of kidney stones. This approach aims to enhance the accuracy and detail of stone classification beyond what is achievable with conventional computed tomography (CT) and dual-energy CT (DECT).

MATERIALS AND METHODS

In this ex vivo study, 135 kidney stones were first classified using infrared spectroscopy. All stones were then scanned in a PCCT embedded in a phantom. Various monoenergetic reconstructions were generated, and radiomics features were extracted. Statistical analysis was performed using Random Forest (RF) classifiers for both individual reconstructions and a combined model.

RESULTS

The combined model, using radiomics features from all monoenergetic reconstructions, significantly outperformed individual reconstructions and SPP parameters, with an AUC of 0.95 and test accuracy of 0.81 for differentiating all six stone types. Feature importance analysis identified key parameters, including NGTDM_Strength and wavelet-LLH_firstorder_Variance.

CONCLUSION

This ex vivo study demonstrates that radiomics-driven PCCT analysis can improve differentiation between kidney stone subtypes. The combined model outperformed individual monoenergetic levels, highlighting the potential of spectral profiling in PCCT to optimize treatment through image-based strategies.

KEY POINTS

Question How can photon-counting computed tomography (PCCT) combined with radiomics improve the differentiation of kidney stone types beyond conventional CT and dual-energy CT, enhancing personalized therapy? Findings Our ex vivo study demonstrates that a combined spectral-driven radiomics model achieved 95% AUC and 81% test accuracy in differentiating six kidney stone types. Clinical relevance Implementing PCCT-based spectral-driven radiomics allows for precise non-invasive differentiation of kidney stone types, leading to improved diagnostic accuracy and more personalized, effective treatment strategies, potentially reducing the need for invasive procedures and recurrence.

Comparison of actual and automated CT measurements of urinary stone size: a phantom study

Urinary stone size is key in determining treatment. Although computed tomography (CT) scans are widely used for diagnosing urinary stones, measurements of stone size obtained from CT images may be inaccurate compared to actual size. Twenty-four urinary stone phantoms were 3D printed at three densities (100, 1000, and 3000 Hounsfield units [HU]) and eight sizes. CT images of the phantoms were taken. Nineteen radiologists and 33 emergency physicians from two institutions measured stone sizes on CT images using mediastinum and bone settings. An automated algorithm segmented regions of interest and estimated stone size using pixel HUs. Mean absolute error (MAE) was assessed for the accuracy of each measurement method against known phantom sizes. For the mediastinum setting, MAEs for 100, 1000, and 3000 HU stone phantoms were 1.05 mm ± 0.06, 1.01 mm ± 0.06, and 2.38 mm ± 0.17, respectively. For the bone setting, MAEs were 0.98 mm ± 0.07, 0.55 mm ± 0.10, and 1.91 mm ± 0.06, respectively. For automated measurements, MAEs were 1.16 mm, 0.21 mm, and 2.10 mm, respectively. Participant-to-participant variability was observed across all measurement settings, regardless of the stone density or window used. For stone size measurements on CT images, the bone setting provided more accurate results than the mediastinum setting. Automated measurement methods, which estimate stone size by outlining its edges, were more accurate than manual measurements for 1000 HU stones, the most common stone density. However, for stones with densities above or below 1000 HU, the accuracy of the automated method may decrease.

How to estimate stone volume and its use in stone surgery: a comprehensive review

OBJECTIVE

Current interventional guidelines refer to the cumulative stone diameter to choose the appropriate surgical modality (ureteroscopy [URS], extracorporeal shockwave lithotripsy [ESWL] and percutaneous nephrolithotomy [PCNL]). The stone volume (SV) has been introduced recently, to better estimate the stone burden. This review aimed to summarize the available methods to evaluate the SV and its use in urolithiasis treatment.

MATERIAL AND METHODS

A comprehensive review of the literature was performed in December 2022 by searching Embase, Cochrane and Pubmed databases. Articles were considered eligible if they described SV measurement or the stone free rate after different treatment modalities (SWL, URS, PCNL) or spontaneous passage, based on SV measurement. Two reviewers independently assessed the eligibility and the quality of the articles and performed the data extraction.

RESULTS

In total, 28 studies were included. All studies used different measurement techniques for stone volume. The automated volume measurement appeared to be more precise than the calculated volume. In vitro studies showed that the automated volume measurement was closer to actual stone volume, with a lower inter-observer variability. Regarding URS, stone volume was found to be more predictive of stone free rates as compared to maximum stone diameter or cumulative diameter for stones >20 mm. This was not the case for PCNL and SWL.

CONCLUSIONS

Stone volume estimation is feasible, manually or automatically and is likely a better representation of the actual stone burden. While for larger stones treated by retrograde intrarenal surgery, stone volume appears to be a better predictor of SFR, the superiority of stone volume throughout all stone burdens and for all stone treatments, remains to be proven. Automated volume acquisition is more precise and reproducible than calculated volume.

Design and internal validation of S.I.C.K.: a novel nomogram predicting infectious and hemorrhagic events after percutaneous nephrolithotomy

BACKGROUND

Hemorrhagic and infectious events represent severe complications after percutaneous nephrolithotomy (PCNLs). Existing nephrolithometric nomograms have been introduced but their reliability in predicting complications is debated. We present a newly designed nomogram with intention to predict hemorrhagic/infectious events after PCNLs.

METHODS

We conducted a multicentric prospective study on adult patients undergoing standard (24 Fr) or mini (18 Fr) PCNL. Dataset was derived from previous RCT, where patients have been assigned to mini-PCNL or standard-PCNL to treat renal stones up to 40 mm. Aim of the study was to identify preoperative risk factors for early postoperative infectious/hemorrhagic complications including fever, septic shock, transfusion or angioembolization.

RESULTS

A total of 1980 patients were finally included. 992 patients (50.1%) received mini-PCNL and 848 standard PCNL (49.9%). The overall SFR was 86.1% with a mean maximum stone diameter of 29 mm (SD 25.0-35.0). 178 patients (8.9%) had fever,14 (0.7%) urosepsis, 24 patients (1.2%) required transfusion and 18 (0.9%) angioembolization. The overall complication was (11.7%). After multivariable analysis, the included elements in the nomogram were age (P=0.041), BMI (P=0.018), maximum stone diameter (P<0.001), preoperative hemoglobin (P=0.005), type 1/2 diabetes (P=0.05), eGFR<30 (P=0.0032), hypertension (>135/85 mmHg, P=0.001), previous PCNL or pyelo/nephrolithotomy (P=0.0018), severe hydronephrosis (P=0.002). After internal validation, the AUC of the model was 0.73.

CONCLUSIONS

This is the first nomogram predicting infections and bleedings after PCNLs, it shows a good accuracy and can support clinicians in their patients' peri-operative workout and management.

Deep-Learning-Based Image Denoising in Imaging of Urolithiasis: Assessment of Image Quality and Comparison to State-of-the-Art Iterative Reconstructions

This study aimed to compare the image quality and diagnostic accuracy of deep-learning-based image denoising reconstructions (DLIDs) to established iterative reconstructed algorithms in low-dose computed tomography (LDCT) of patients with suspected urolithiasis. LDCTs (CTDIvol, 2 mGy) of 76 patients (age: 40.3 ± 5.2 years, M/W: 51/25) with suspected urolithiasis were retrospectively included. Filtered-back projection (FBP), hybrid iterative and model-based iterative reconstruction (HIR/MBIR, respectively) were reconstructed. FBP images were processed using a Food and Drug Administration (FDA)-approved DLID. ROIs were placed in renal parenchyma, fat, muscle and urinary bladder. Signal- and contrast-to-noise ratios (SNR/CNR, respectively) were calculated. Two radiologists evaluated image quality on five-point Likert scales and urinary stones. The results showed a progressive decrease in image noise from FBP, HIR and DLID to MBIR with significant differences between each method (p < 0.05). SNR and CNR were comparable between MBIR and DLID, while it was significantly lower in HIR followed by FBP (e.g., SNR: 1.5 ± 0.3; 1.4 ± 0.4; 1.0 ± 0.3; 0.7 ± 0.2, p < 0.05). Subjective analysis confirmed best image quality in MBIR, followed by DLID and HIR, both being superior to FBP (p < 0.05). Diagnostic accuracy for urinary stone detection was best using MBIR (0.94), lowest using FBP (0.84) and comparable between DLID (0.90) and HIR (0.90). Stone size measurements were consistent between all reconstructions and showed excellent correlation (r2 = 0.958-0.975). In conclusion, MBIR yielded the highest image quality and diagnostic accuracy, with DLID producing better results than HIR and FBP in image quality and matching HIR in diagnostic precision.

Detection and size measurements of kidney stones on virtual non-contrast reconstructions derived from dual-layer computed tomography in an ex vivo phantom setup

OBJECTIVES

To systematically investigate the usability of virtual non-contrast reconstructions (VNC) derived from dual-layer CT (DLCT) for detection and size measurements of kidney stones with regards to different degrees of surrounding iodine-induced attenuation and radiation dose.

METHODS

Ninety-two kidney stones of varying size (3-14 mm) and composition were placed in a phantom filled with different contrast media/water mixtures exhibiting specific iodine-induced attenuation (0-1500 HU). DLCT-scans were acquired using CTDI_vol of 2 mGy and 10 mGy. Conventional images (CI) and VNC_0H-1500HU were reconstructed. Reference stone size was determined using a digital caliper (Man-M). Visibility and stone size were assessed. Statistical analysis was performed using the McNemar test, Wilcoxon test, and the coefficient of determination.

RESULTS

All stones were visible on CI_0HU and VNC_200HU. Starting at VNC_{400 HU}, the detection rate decreased with increasing HU and was significantly lower as compared to CI_0HU on VNC_{≥ 600HU} (100.0 vs. 94.0%, p < 0.05). The overall detection rate was higher using 10 mGy as compared to 2 mGy protocol (87.9 vs. 81.8%; p < 0.001). Stone size was significantly overestimated on all VNC compared to Man-M (7.0 ± 3.5 vs. 6.6 ± 2.8 mm, p < 0.001). Again, the 10 mGy protocol tended to show a better correlation with Man-M as compared to 2 mGy protocol (R² = 0.39-0.68 vs. R² = 0.31-0.57).

CONCLUSIONS

Detection and size measurements of kidney stones surrounded by contrast media on VNC are feasible. The detection rate of kidney stones decreases with increasing iodine-induced attenuation and with decreasing radiation dose as well as stone size, while remaining comparable to CI_0HU on VNC _{≤ 400 HU}.

KEY POINTS

• The detection rate of kidney stones on VNC depends on the surrounding iodine-induced attenuation, the used radiation dose, and the stone size. • The detection rate of kidney stones on VNC decreases with greater iodine-induced attenuation and with lower radiation dose, particularly in small stones. • The visibility of kidney stones on VNC _{≤ 400 HU} remains comparable to true-non-contrast scans even when using a low-dose technique.

Clinical Low-Dose Photon-Counting CT for the Detection of Urolithiasis: Radiation Dose Reduction Is Possible without Compromising Image Quality

Background: This study evaluated the feasibility of reducing the radiation dose in abdominal imaging of urolithiasis with a clinical photon-counting CT (PCCT) by gradually lowering the image quality level (IQL) without compromising the image quality and diagnostic value. Methods: Ninety-eight PCCT examinations using either IQL70 (n = 31), IQL60 (n = 31) or IQL50 (n = 36) were retrospectively included. Parameters for the radiation dose and the quantitative image quality were analyzed. Qualitative image quality, presence of urolithiasis and diagnostic confidence were rated. Results: Lowering the IQL from 70 to 50 led to a significant decrease (22.8%) in the size-specific dose estimate (SSDE, IQL70 4.57 ± 0.84 mGy, IQL50 3.53 ± 0.70 mGy, p < 0.001). Simultaneously, lowering the IQL led to a minimal deterioration of the quantitative quality, e.g., image noise increased from 9.13 ± 1.99 (IQL70) to 9.91 ± 1.77 (IQL50, p = 0.248). Radiologists did not notice major changes in the image quality throughout the IQLs. Detection rates of urolithiasis (91.3-100%) did not differ markedly. Diagnostic confidence was high and not influenced by the IQL. Conclusions: Adjusting the PCCT scan protocol by lowering the IQL can significantly reduce the radiation dose without significant impairment of the image quality. The detection rate and diagnostic confidence are not impaired by using an ultra-low-dose PCCT scan protocol.

Imaging in stone diagnosis and surgical planning

PURPOSE OF REVIEW

Radiological imaging techniques and applications are constantly advancing. This review will examine modern imaging techniques in the diagnosis of urolithiasis and applications for surgical planning.

RECENT FINDINGS

The diagnosis of urolithiasis may be done via plain film X-ray, ultrasound (US), or contrast tomography (CT) scan. US should be applied in the workup of flank pain in emergency rooms and may reduce unnecessary radiation exposure. Low dose and ultra-low-dose CT remain the diagnostic standard for most populations but remain underutilized. Single and dual-energy CT provide three-dimensional imaging that can predict stone-specific parameters that help clinicians predict stone passage likelihood, identify ideal management techniques, and possibly reduce complications. Machine learning has been increasingly applied to 3-D imaging to support clinicians in these prognostications and treatment selection.

SUMMARY

The diagnosis and management of urolithiasis are increasingly personalized. Patient and stone characteristics will support clinicians in treatment decision, surgical planning, and counseling.

Clinical Low Dose Photon Counting CT for the Detection of Urolithiasis: Evaluation of Image Quality and Radiation Dose

The purpose of this study was the evaluation of image quality and radiation dose parameters of the novel photon counting CT (PCCT, Naeotom Alpha, Siemens Healthineers) using low-dose scan protocols for the detection of urolithiasis. Standard CT scans were used as a reference (S40, Somatom Sensation 40, Siemens Healthineers). Sixty-three patients, who underwent CT scans between August and December 2021, were retrospectively enrolled. Thirty-one patients were examined with the PCCT and 32 patients were examined with the S40. Radiation dose parameters, as well as quantitative and qualitative image parameters, were analyzed. The presence of urolithiasis, image quality, and diagnostic certainty were rated on a 5-point-scale by 3 blinded readers. Both patient groups (PCCT and S40) did not differ significantly in terms of body mass index. Radiation dose was significantly lower for examinations with the PCCT compared to the S40 (2.4 ± 1.0 mSv vs. 3.4 ± 1.0 mSv; p < 0.001). The SNR was significantly better on images acquired with the PCCT (13.3 ± 3.3 vs. 8.2 ± 1.9; p < 0.001). The image quality of the PCCT was rated significantly better (4.3 ± 0.7 vs. 2.8 ± 0.6; p < 0.001). The detection rate of kidney or ureter calculi was excellent with both CT scanners (PCCT 97.8% and S40 99%, p = 0.611). In high contrast imaging, such as the depiction of stones of the kidney and the ureter, PCCT allows a significant reduction of radiation dose, while maintaining excellent diagnostic confidence and image quality. Given this image quality with our current protocol, further adjustments towards ultra-low-dose CT scans appear feasible.

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans

PURPOSE

Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning-based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement. Prior to this work, no such system had been developed for use on noisy low-dose CT or tested on a large-scale external dataset.

METHODS

We used a dataset of 91 CT colonography (CTC) scans with manually marked kidney stones combined with 89 CTC scans without kidney stones. To compare with a prior work half the data was used for training and half for testing. A set of CTC scans from 6185 patients from a separate institution with patient-level labels were used as an external validation set. A 3D U-Net model was employed to segment the kidneys, followed by gradient-based anisotropic denoising, thresholding, and region growing. A 13 layer convolutional neural network classifier was then applied to distinguish kidney stones from false positive regions.

RESULTS

The system achieved a sensitivity of 0.86 at 0.5 false positives per scan on a challenging test set of low-dose CT with many small stones, an improvement over an earlier work that obtained a sensitivity of 0.52. The stone volume measurements correlated well with manual measurements (r 2 = 0.95 $r^2 = 0.95$ ). For patient-level classification, the system achieved an area under the receiver-operating characteristic of 0.95 on an external validation set (sensitivity = 0.88, specificity = 0.91 at the Youden point). A common cause of false positives were small atherosclerotic plaques in the renal sinus that simulated kidney stones.

CONCLUSIONS

Our deep-learning-based system showed improvements over a previously developed system that did not use deep learning, with even higher performance on an external validation set.

Manual kidney stone size measurements in computed tomography are most accurate using multiplanar image reformatations and bone window settings

Computed tomography in suspected urolithiasis provides information about the presence, location and size of stones. Particularly stone size is a key parameter in treatment decision; however, data on impact of reformatation and measurement strategies is sparse. This study aimed to investigate the influence of different image reformatations, slice thicknesses and window settings on stone size measurements. Reference stone sizes of 47 kidney stones representative for clinically encountered compositions were measured manually using a digital caliper (Man-M). Afterwards stones were placed in a 3D-printed, semi-anthropomorphic phantom, and scanned using a low dose protocol (CTDIvol 2 mGy). Images were reconstructed using hybrid-iterative and model-based iterative reconstruction algorithms (HIR, MBIR) with different slice thicknesses. Two independent readers measured largest stone diameter on axial (2 mm and 5 mm) and multiplanar reformatations (based upon 0.67 mm reconstructions) using different window settings (soft-tissue and bone). Statistics were conducted using ANOVA ± correction for multiple comparisons. Overall stone size in CT was underestimated compared to Man-M (8.8 ± 2.9 vs. 7.7 ± 2.7 mm, p < 0.05), yet closely correlated (r = 0.70). Reconstruction algorithm and slice thickness did not significantly impact measurements (p > 0.05), while image reformatations and window settings did (p < 0.05). CT measurements using multiplanar reformatation with a bone window setting showed closest agreement with Man-M (8.7 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05, r = 0.83). Manual CT-based stone size measurements are most accurate using multiplanar image reformatation with a bone window setting, while measurements on axial planes with different slice thicknesses underestimate true stone size. Therefore, this procedure is recommended when impacting treatment decision.