MP06-06 DOES URINARY CYTOLOGY HAVE A ROLE IN HEMATURIA INVESTIGATIONS? RESULTS OF A PROSPECTIVE OBSERVATIONAL STUDY (DETECT I)
Bibliographic record
Abstract
You have accessJournal of UrologyBladder Cancer: Epidemiology & Evaluation I1 Apr 2018MP06-06 DOES URINARY CYTOLOGY HAVE A ROLE IN HEMATURIA INVESTIGATIONS? RESULTS OF A PROSPECTIVE OBSERVATIONAL STUDY (DETECT I) Wei Shen Tan, Andrew Feber, Liqin Dong, Rachael Sarpong, Simon Rodney, Pramit Khetrapal, Patricia de Winter, Rumana Jalil, Norman Williams, Chris Brew-Graves, John Kelly, and DETECT I trial group Wei Shen TanWei Shen Tan More articles by this author , Andrew FeberAndrew Feber More articles by this author , Liqin DongLiqin Dong More articles by this author , Rachael SarpongRachael Sarpong More articles by this author , Simon RodneySimon Rodney More articles by this author , Pramit KhetrapalPramit Khetrapal More articles by this author , Patricia de WinterPatricia de Winter More articles by this author , Rumana JalilRumana Jalil More articles by this author , Norman WilliamsNorman Williams More articles by this author , Chris Brew-GravesChris Brew-Graves More articles by this author , John KellyJohn Kelly More articles by this author , and DETECT I trial groupDETECT I trial group More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.198AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The role of urinary cytology as part of hematuria investigations is debatable. The Dutch, Canadian and Japanese Urology Associations recommended that urinary cytology should be performed for selected patient groups presenting with gross hematuria (GH). The UK National Institute of Clinical Excellence (NICE) does not comment on the use of urinary cytology and American Urology Association does not recommend the use of urinary cytology for initially hematuria evaluation. We determine the diagnostic accuracy of urinary cytology in a multicentre prospective observational study of 567 patients investigated for hematuria. Primary outcome: the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of urinary cytology to diagnosed bladder cancer and/ or upper tract transitional cell carcinoma (TCC) in patients investigated with hematuria at secondary care. METHODS The DETECT I study (clinicaltrials.gov NCT02676180) recruited patients presenting with hematuria following referral to secondary case at 9 institutions. All patients had a cystoscopy and upper tract imaging (ultrasound and/ or CT intravenous urography) and urinary cytology. Patients with a suspicion of bladder cancer had transurethral resection of bladder cancer or bladder biopsy for histological confirmation of cancer. Urinary cytology results were defined as positive/ atypical or negative. RESULTS 567 patients with a median age of 68 years were recruited over a 14-month period. 37 (6.5%) bladder cancers and 8 upper tract TCC (1.4%) were identified. 13 urinary samples (2.3%) were excluded due to inadequate urinary cellular content for cytology analysis. The accuracy of urinary cytology for the diagnosis of bladder or upper tract TCC was: sensitivity 40%, specificity 95%, PPV 40% and NPV 95%. 20 bladder cancers and 6 upper tract TCC were missed. Bladder cancers missed according to grade and stage were: 4 (20%) G3= pT2, 3 (15%) G3 pT1, 9 (45%) G3/2 pTa, and 4 (20%) G1 pTa. 38% of patients were classified as high risk. When selecting for patients with GH, the diagnostic accuracy of urinary cytology CONCLUSIONS In clinical practice, urine cytology will miss a significant number of muscle invasive TCC and high risk NMIBC. The role of urinary cytology as part of routine hematuria investigations should not be recommended. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e54 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Wei Shen Tan More articles by this author Andrew Feber More articles by this author Liqin Dong More articles by this author Rachael Sarpong More articles by this author Simon Rodney More articles by this author Pramit Khetrapal More articles by this author Patricia de Winter More articles by this author Rumana Jalil More articles by this author Norman Williams More articles by this author Chris Brew-Graves More articles by this author John Kelly More articles by this author DETECT I trial group More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".