The Diagnostic Yield of CT Urography in the Workup of Hematuria With Negative Cystoscopy

Common, Jessica; Ramonas, Milita; Alabousi, Abdullah

doi:10.1177/0846537120933952

Cited by 3 publications

(12 citation statements)

References 20 publications

(32 reference statements)

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“…Given the emerging evidence surrounding the low risk of upper-tract malignancies in patients with NVH and the risks to patients associated with CTU, there have been growing trends in the literature to limit the routine use of CTU for investigating patients with NVH, without careful consideration of the risks and benefits [16, 18, 19]. After previously recommending investigation with CTU for patients aged 35 and over, the AUA's guideline on the investigation of microhaematuria introduced in 2020 presents an algorithm for risk-stratifying NVH patients based on age, sex, haematuria presentation, and other risk factors for urothelial malignancy, with ultrasonography replacing CTU for upper-tract imaging in all but high-risk patients [20].…”

Section: Discussionmentioning

confidence: 99%

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Optimizing Upper-Tract Imaging for Non-Visible Haematuria

et al. 2022

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Introduction: Non-visible haematuria (NVH) is associated with a small risk of upper-tract urothelial carcinoma (UTUC), though there is little consensus on its investigation, particularly with regard to upper-tract imaging. This study aimed to determine whether the presentation of UTUC can guide investigation of NVH in patients under 60 years old. Methods: All patients investigated at our one-stop haematuria clinics under a cancer pathway were reviewed during a 5-year period, with all patients undergoing cystoscopy and upper-tract imaging. Retrospective analysis of all UTUC cases from our urological cancer multidisciplinary team meeting database over a 10-year period was also undertaken. Results: 2,129 patients with a median age of 67 years underwent urgent investigation for haematuria between March 2015 and February 2020. 449 cases presented with NVH, of whom 124 (27.6%) were under 60. Out of 21 cases of UTUC, only 2 presented with NVH; both were over the age of 60 years. Factors that independently predicted diagnosis with urinary-tract malignancy were age ≥60 (OR 3.70, p < 0.001), visible haematuria (OR 2.50, p = 0.006), and suspicious cystoscopic findings (OR 58.06, p < 0.001). Review of all 119 UTUC cases over 10 years found 6 cases (5.0%) presenting with NVH, with one (0.8%) also presenting under 60 years. Conclusion: Diagnosis with UTUC is rare in patients presenting with NVH under the age of 60 years. Routine use of CTU in this low-risk group is best avoided, with ultrasonography constituting a safer first-line upper-tract imaging modality. Guidelines that risk-stratify NVH patients may be effective in reducing unnecessary investigations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Optimizing Upper-Tract Imaging for Non-Visible Haematuria

et al. 2022

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“…Of the 42 studies, prostate cancer was histopathologically verified in 13 [25][26][27][28][29]36,38,41,45,46,49,51,53], and in one study, some cases were histopathological verified and some clinically verified [33]. The rest of studies provided no information on how the prostate cancer was diagnosed.…”

Section: Prostate Cancer Characteristicsmentioning

confidence: 99%

“…Moreover, Gleason sum score was ≤6 in 17 cases, and ≥7 in eight [29,33,46], with Gleason sum score not described in 480. Histopathology was provided for 50 of the 505 identified prostate cancers, and all were adenocarcinomas [26,27,39,51]. The PSA level was described for 61 of the 505 identified prostate cancers, and all had elevated PSA [23,40,46,57].…”

Section: Prostate Cancer Characteristicsmentioning

confidence: 99%

Prevalence of prostate cancer in men with haematuria: a systematic review and meta‐analysis

et al. 2023

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To investigate the prevalence of prostate cancer in men attending evaluation for haematuria, as this could help healthcare providers to determine whether men with haematuria should have prostate examinations performed. MethodsThe study was performed according to a pre-specified protocol uploaded to the International Prospective Register of Systematic Reviews (PROSPERO; CRD42022299383). A systematic search of MEDLINE, Ovid and Google Scholar was performed in December 2021. Two independent researchers evaluated all titles, available abstracts, and full texts. We included studies on adult men (aged ≥18 years) describing haematuria and prostate cancer. ResultsWe screened 4252 titles and abstracts when available and assessed 350 studies in full text. In total, 65 studies were included and 42 was summarised in a meta-analysis. In total, 18 752 men with haematuria were included, and the pooled prevalence (95% confidence interval [CI]) of prostate cancer was 3.0% (2.0-4.1%). In men with macroscopic haematuria, the pooled prevalence (95% CI) of prostate cancer was 5.9% (2.9-9.9%; n = 265/5373). In men with microscopic haematuria, the pooled prevalence (95% CI) of prostate cancer was 1.4% (0.8-2.2%; n = 71/6642). ConclusionOur findings indicate that the prevalence of prostate cancer is considerable in men attending evaluation for haematuria. Therefore, digital rectal examination and prostate-specific antigen measurement should become a standard procedure for all men with haematuria, especially for men with macroscopic haematuria.

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“…The diagnostic yield provides insight into disease prevalence, which not only informs referring clinicians but also can impact patient management. 8 Insight into appropriateness (input) and diagnostic yield (output) is not routinely assessed, even though this type of information is valuable-it contributes to effective resource utilization [9][10][11][12] and allows for the identification of factors related to overtesting. 13 To leverage the great potential of aggregated data, the retrieval of information from radiology requests and reports must be automated.…”

Section: Introductionmentioning

confidence: 99%

The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study

Olthof

Ooijen

Cornelissen

2022

Health Informatics J

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Background Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification. Methods In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases’ categories of the datasets of requests and reports. Results The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757–0.859)] to 0.976 [95% CI (0.956–0.996)] for the requests and 0.746 [95% CI (0.689–0.802)] to 1.0 [95% CI (1.0–1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922–0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data. Conclusion Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests.

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The Diagnostic Yield of CT Urography in the Workup of Hematuria With Negative Cystoscopy

Cited by 3 publications

References 20 publications

Optimizing Upper-Tract Imaging for Non-Visible Haematuria

Optimizing Upper-Tract Imaging for Non-Visible Haematuria

Prevalence of prostate cancer in men with haematuria: a systematic review and meta‐analysis

The natural language processing of radiology requests and reports of chest imaging: Comparing five transformer models’ multilabel classification and a proof-of-concept study

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