Should contrast CT urography replace non-contrast CT as an investigation for ureteric colic in the emergency department in those aged 65 and over?

A Novel Deep Learning-based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography

BACKGROUND AND OBJECTIVE

Our aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume and density, which are essential for treatment decisions. The performance of the system was compared to that of urologists in emergency room (ER) scenarios.

METHODS

Axial CT images for patients who underwent stone surgery between August 2022 and July 2023 comprised the data set, which was divided into 70% for training, 10% for internal validation, and 20% for testing. Two urologists and an AI specialist annotated stones using Labelimg for ground-truth data. The YOLOv4 architecture was used for training, with acceleration via an RTX 4900 graphics processing unit (GPU). External validation was performed using CT images for 100 patients with suspected urolithiasis.

KEY FINDINGS AND LIMITATIONS

The AI system was trained on 39 433 CT images, of which 9.1% were positive. The system achieved accuracy of 95%, peaking with a 1:2 positive-to-negative sample ratio. In a validation set of 5736 images (482 positive), accuracy remained at 95%. Misses (2.6%) were mainly irregular stones. False positives (3.4%) were often due to artifacts or calcifications. External validation using 100 CT images from the ER revealed accuracy of 94%; cases that were missed were mostly ureterovesical junction stones, which were not included in the training set. The AI system surpassed human specialists in speed, analyzing 150 CT images in 13 s, versus 38.6 s for evaluation by urologists and 23 h for formal reading. The AI system calculated stone volume in 0.2 s, versus 77 s for calculation by urologists.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Our AI system, which uses advanced deep learning, assists in diagnosing urolithiasis with 94% accuracy in real clinical settings and has potential for rapid diagnosis using standard consumer-grade GPUs.

PATIENT SUMMARY

We developed a new AI (artificial intelligence) system that can quickly and accurately detect kidney stones in CT (computed tomography) scans. Testing showed that this system is highly effective, with accuracy of 94% for real cases in the emergency department. It is much faster than traditional methods and provides rapid and reliable results to help doctors in making better treatment decisions for their patients.

Impact of COVID-19 on Uro-Oncological Patients: A Comprehensive Review of the Literature

Background: The aim of this paper is to discuss the impact of COVID-19 on patients with urological malignancies (prostate cancer, bladder and upper tract urothelial cancer, kidney cancer, penile and testicular cancer) and to review the available recommendations reported in the literature. Methods: A review was performed, through the PubMed database, regarding available recommendations reported in the literature, to identify studies examining the impact of COVID-19 on treatment and clinical outcomes (including upstaging, recurrence, and mortality) for uro-oncological patients. Results: The COVID-19 pandemic dramatically changed the urological guidelines and patients' access to screening programs and follow-up visits. Great efforts were undertaken to guarantee treatments to high-risk patients although follow up was not always possible due to recurrent surges, and patients with lower risk cancers had to wait for therapies. Conclusions: Physically and mentally, uro-oncological patients paid a heavy price during the COVID-19 pandemic. Long term data on the "costs" of clinical decisions made during the COVID-19 pandemic are still to be revealed and analyzed.

Role of Multi-Detector Computed Tomography Indices in Predicting Extracorporeal Shockwave Lithotripsy Outcome in Patients With Nephrolithiasis

Background

Nephrolithiasis is one of the most common renal pathologies and is routinely encountered in daily practice. Non-contrast computed tomography (NCCT) is the gold standard diagnostic imaging modality for urolithiasis. The role of HU (Hounsfield units) in calculus as a predictor of extracorporeal shock wave lithotripsy (ESWL) has been studied in the past. This study aims to evaluate the role of HU value and various other NCCT indices in predicting the outcome of ESWL.

Material and methods

This was a prospective observational study that included 45 patients suffering from nephrolithiasis who underwent NCCT-KUB (kidney, ureter, and bladder) followed by ESWL. The NCCT indices were evaluated and correlated with the outcome of ESWL. NCCT-KUB was performed using multidetector SIEMENS® SOMATOM EMOTION 16-slice CT scanner (SIEMENS, Munich, Germany).

Results

In our study, the HU value turned out to be a statistically significant predictor of ESWL success (p <0.05), and the renal pelvis also proved to be a good prognostic indicator for ESWL success. The cut-off value of <1179 HU favored a successful outcome of ESWL, while if >1179 HU, ESWL is likely to fail. Hence, the successful outcome of ESWL is inversely proportional to the HU value. Renal pelvic calculi (n=14) showed a 100% success rate, which was better than all other calculus locations (p<0.05). However, the rest of the indices did not show any statistical significance.

Conclusion

Multi-detector NCCT-KUB indices can help in the selection of patients with a good prognosis for ESWL, which will prevent the patient from undergoing undesired invasive procedures.

A prospective, matched comparison of ultra-low and standard-dose computed tomography for assessment of renal colic

OBJECTIVE

To determine the diagnostic accuracy of ultra-low-dose computed tomography (ULDCT) compared with standard-dose CT (SDCT) in the evaluation of patients with clinically suspected renal colic, in addition to secondary features (hydroureteronephrosis, perinephric stranding) and additional pathological entities (renal masses).

PATIENTS AND METHODS

A prospective, comparative cohort study was conducted amongst patients presenting to the emergency department with signs and symptoms suggestive of renal or ureteric colic. Patients underwent both SDCT and ULDCT. Single-blinded review of the image sets was performed independently by three board-certified radiologists.

RESULTS

Among 21 patients, the effective radiation dose was lower for ULDCT [mean (SD) 1.02 (0.16) mSv] than SDCT [mean (SD) 4.97 (2.02) mSv]. Renal and/or ureteric calculi were detected in 57.1% (12/21) of patients. There were no significant differences in calculus detection and size estimation between ULDCT and SDCT. A higher concordance was observed for ureteric calculi (75%) than renal calculi (38%), mostly due to greater detection of calculi of <3 mm by SDCT. Clinically significant calculi (≥3 mm) were detected by ULDCT with high specificity (97.6%) and sensitivity (100%) compared to overall detection (specificity 91.2%, sensitivity 58.8%). ULDCT and SDCT were highly concordant for detection of secondary features, while ULDCT detected less renal cysts of <2 cm. Inter-observer agreement for the ureteric calculi detection was 93.9% for SDCT and 87.8% for ULDCT.

CONCLUSION

ULDCT performed similarly to SDCT for calculus detection and size estimation with reduced radiation exposure. Based on this and other studies, ULDCT should be considered as the first-line modality for evaluation of renal colic in routine practice.