Evidence map›Paper›PMID 39280670›Full record

ArticleTranslational andrology and urology2024

Establishing a model predicting Gleason grade group upgrading in prostate cancer.

Jian Chen, Qiming Chen, Ze Wang, Xuzhi Yan, Yapeng Wang, Yao Zhang, Jun Zhang, Jing Xu, Qiang Ma, Peng Zhong and 4 more

Abstract read
In one paragraph

Article in Translational andrology and urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

14 authors.

Jian Chen *Department of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Qiming Chen *Department of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Ze WangDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Xuzhi YanDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Yapeng WangDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Yao ZhangDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Jun ZhangDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Jing XuDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Qiang MaDepartment of Pathology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Peng ZhongDepartment of Pathology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Dianzheng ZhangDepartment of Bio-Medical Sciences, Philadelphia College of Osteopathic Medicine, Philadelphia, PA, USA.
Qiuli LiuDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Weihua LanDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.
Jun JiangDepartment of Urology, Institute of Surgery Research, Daping Hospital, Army Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Gleason grade group (GG) upgrading is associated with increased biochemical recurrence (BCR), local progression, and decreased cancer-specific survival (CSS) in prostate cancer (PCa). However, descriptions of the risk factors of GG upgrading are scarce. The objective of this study was to identify risk factors and establish a model to predict GG upgrading. Methods: There were 361 patients with PCa who underwent radical prostatectomy between May 2011 and February 2022 enrolled. Univariate and multivariate logistic regression analyses were identified and nomogram further narrowed down the contributing factors in GG upgrading. The correction curve and decision curve were used to assess the model. Results: In the overall cohort, 141 patients had GG upgrading. But the subgroup cohort (GG ≤2) showed that 68 patients had GG upgrading. Multivariate logistic regression analysis showed that in the overall cohort, total prostate-specific antigen (tPSA) ≥10 ng/mL, systemic immune-inflammation index (SII) >379.50, neutrophil-lymphocyte ratio (NLR) >2.13, the GG of biopsy ≥3, the number of positive cores >3 were independent risk factors in GG upgrading. In the cohort of biopsy GG ≤2, multivariate logistic regression showed that the tPSA ≥10 ng/mL, SII >379.50 and the number of positive cores >3 were independent risk factors in GG upgrading. A novel model predicting GG upgrading was established based on these three parameters. The area under the curve (AUC) of the prediction model was 0.759. The C-index of the nomogram was 0.768. The calibration curves of the model showed good predictive performance. Clinical decision curves indicated clinical benefit in the interval of 20% to 90% of threshold probability and good clinical utility. Conclusions: Combined levels of tPSA, SII and the positive biopsy cores distinguish patients with high-risk GG upgrading in the group of biopsy GG ≤2 and are helpful in the decision of treatment plans.

Indexed as

Gleason grade group upgradingprediction modelProstate cancer (PCa)systemic immune-inflammation index (SII)

Identifiers

PMID39280670
PMCPMC11399042

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.