Evidence map›Paper›PMID 35813249›Full record

ArticleEuropean urology open science2022

Site-specific Risk Stratification Models for Postoperative Recurrence and Survival Prediction in Patients with Upper Tract Urothelial Carcinoma Undergoing Radical Nephroureterectomy: Better Stratification for Adjuvant Therapy.

Makito Miyake, Kota Iida, Nobutaka Nishimura, Takashi Inoue, Hiroaki Matsumoto, Hideyasu Matsuyama, Yuya Fujiwara, Kazumasa Komura, Teruo Inamoto, Haruhito Azuma and 13 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in European urology open science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors at 7 institutions in 1 country.

Makito MiyakeDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Kota IidaDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Nobutaka NishimuraDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Takashi InoueInstitute for Clinical and Translational Science, Nara Medical University, Kashihara, Nara, Japan.
Hiroaki MatsumotoDepartment of Urology, Graduate School of Medicine, Yamaguchi University, Ube, Yamaguchi, Japan.
Hideyasu MatsuyamaDepartment of Urology, Graduate School of Medicine, Yamaguchi University, Ube, Yamaguchi, Japan.
Yuya FujiwaraDepartment of Urology, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan.
Kazumasa KomuraDepartment of Urology, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan.
Teruo InamotoDepartment of Urology, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan.
Haruhito AzumaDepartment of Urology, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan.
Hiroaki YasumotoDepartment of Urology, Shimane University School of Medicine, Izumo, Shimane, Japan.
Hiroaki ShiinaDepartment of Urology, Shimane University School of Medicine, Izumo, Shimane, Japan.
Masaya YonemoriDepartment of Urology, Graduate School of Medical and Dental Sciences, Kagoshima University, Sakuragaoka, Kagoshima, Japan.
Hideki EnokidaDepartment of Urology, Graduate School of Medical and Dental Sciences, Kagoshima University, Sakuragaoka, Kagoshima, Japan.
Masayuki NakagawaDepartment of Urology, Graduate School of Medical and Dental Sciences, Kagoshima University, Sakuragaoka, Kagoshima, Japan.
Hideo FukuharaDepartment of Urology, Kochi Medical School, Kohasu, Oko-cho, Nankoku-shi, Kochi, Japan.
Keiji InoueDepartment of Urology, Kochi Medical School, Kohasu, Oko-cho, Nankoku-shi, Kochi, Japan.
Takashi YoshidaDepartment of Urology and Andrology, Kansai Medical University, Osaka, Japan.
Hidefumi KinoshitaDepartment of Urology and Andrology, Kansai Medical University, Osaka, Japan.
Tadashi MatsudaDepartment of Urology and Andrology, Kansai Medical University, Osaka, Japan.
Tomomi FujiiDepartment of Diagnostic Pathology, Nara Medical University, Kashihara, Nara, Japan.
Kiyohide FujimotoDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Nishinihon Uro-oncology Extensive Collaboration Group
Nara Medical University · JPOsaka University of Pharmaceutical Sciences · JPKagoshima University · JPKansai Medical University · JPKōchi University · JPShimane University · JPYamaguchi University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Site-specific postoperative risk models for localized upper tract urothelial carcinoma (UTUC) are unavailable. Objective: To create specific risk models for renal pelvic urothelial carcinoma (RPUC) and ureteral urothelial carcinoma (UUC), and to compare the predictive accuracy with the overall UTUC risk model. Design setting and participants: A multi-institutional database retrospective study of 1917 UTUC patients who underwent radical nephroureterectomy (RNU) between 2000 and 2018 was conducted. Outcome measurements and statistical analysis: A multivariate hazard model was used to identify the prognostic factors for extraurinary tract recurrence (EUTR), cancer-specific death (CSD), and intravesical recurrence (IVR) after RNU. Patients were stratified into low-, intermediate-, high-, and highest-risk groups. External validation was performed to estimate a concordance index of the created risk models. We investigated whether our risk models could aid decision-making regarding adjuvant chemotherapy (AC) after RNU. Results and limitations: The UTUC risk models could stratify the risk of cumulative incidence of three endpoints. The RPUC- and UUC-specific risk models showed better stratification than the overall UTUC risk model for all the three endpoints, EUTR, CSD, and IVR (RPUC: concordance index, 0.719 vs 0.770, 0.714 vs 0.794, and 0.538 vs 0.569, respectively; UUC: 0.716 vs 0.767, 0.766 vs 0.809, and 0.553 vs 0.594, respectively). The UUC-specific risk model can identify the high- and highest-risk patients likely to benefit from AC after RNU. A major limitation was the potential selection bias owing to the retrospective nature of this study. Conclusions: We recommend using site-specific risk models instead of the overall UTUC risk model for better risk stratification and decision-making for AC after RNU. Patient summary: Upper tract urothelial carcinoma comprises renal pelvic and ureteral carcinomas. We recommend using site-specific risk models instead of the overall upper tract urothelial carcinoma risk model in risk prediction and decision-making for adjuvant therapy after radical surgery.

Indexed as

Adjuvant therapyDeathNephroureterectomyPredictionPrognosisRecurrenceRiskUpper urinary tract carcinomaUrinary bladder neoplasmUrinary tract

Identifiers

PMID35813249
PMCPMC9257658
OpenAlexW4281750381

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.