Evidence map›Paper›PMID 36422645›Full record

ArticleEuropean radiology2023

A machine-learning-based combination of criteria to detect bladder cancer lymph node metastasis on [

Antoine Girard, Laurent Dercle, Helena Vila-Reyes, Lawrence H Schwartz, Astrid Girma, Marc Bertaux, Camelia Radulescu, Thierry Lebret, Olivier Delcroix, Mathieu Rouanne

Abstract read
PubMed Publisher
In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.2field-weighted citation impact, top 12% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. PET Imaging in Bladder Cancer: An Update and Future Direction.Pharmaceuticals (Basel, Switzerland) · 2023
    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

10 authors at 5 institutions in 2 countries.

Antoine GirardDepartment of Nuclear Medicine, Amiens-Picardy University Hospital, 1 Rue du Professeur Christian Cabrol, Amiens, France. antoine.girard.89@gmail.com.ORCID http://orcid.org/0000-0002-9472-9980
Laurent DercleDepartment of Radiology, New York Presbyterian Hospital - Columbia University Medical Center, New York, NY, 10032, USA.
Helena Vila-ReyesDepartment of Radiology, New York Presbyterian Hospital - Columbia University Medical Center, New York, NY, 10032, USA.
Lawrence H SchwartzDepartment of Radiology, New York Presbyterian Hospital - Columbia University Medical Center, New York, NY, 10032, USA.
Astrid GirmaDepartment of Nuclear Medicine, Hôpital Foch, 40 Rue Worth, 92150, Suresnes, France.
Marc BertauxDepartment of Nuclear Medicine, Hôpital Foch, 40 Rue Worth, 92150, Suresnes, France.
Camelia RadulescuDepartment of Pathology, Hôpital Foch, 40 Rue Worth, 92150, Suresnes, France.
Thierry LebretDepartment of Urology, Hôpital Foch, UVSQ-Université Paris-Saclay, 40 Rue Worth, 92150, Suresnes, France.
Olivier DelcroixDepartment of Nuclear Medicine, CHRU de Brest, 2, avenue Foch, 29609, Brest Cedex, France.
Mathieu RouanneDepartment of Urology, Hôpital Foch, UVSQ-Université Paris-Saclay, 40 Rue Worth, 92150, Suresnes, France.
Hôpital Foch · FRNewYork–Presbyterian Hospital · USUniversité de Versailles Saint-Quentin-en-Yvelines · FRCentre Hospitalier Régional Universitaire de Brest · FRCentre Hospitalier Universitaire Amiens-Picardie · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesInitial pelvic lymph node (LN) staging is pivotal for treatment planification in patients with muscle-invasive bladder cancer (MIBC), but [

methodsConsecutive patients with localized MIBC who performed preoperative [

resultsThe overall prevalence of pelvic LN involvement was 24% (n = 41/173). In the training set, the top 3 features were derived from pelvic LNs (SUVmax of the most intense LN, and product of diameters of the largest LN) and primary bladder tumor (product of diameters). In the validation set, diagnostic performance did not differ significantly between the combination of criteria (AUC = 0.59 95%CI [0.43-0.73]) and the consensus of experts (AUC = 0.64 95%CI [0.48-0.78], p = 0.54). The interrater agreement was equally good with Κ = 0.66 for both.

conclusionThe developed machine-learning-based combination of criteria performs as well as a consensus of experts to detect pelvic LN involvement on [ KEY POINTS: • The developed machine-learning-based combination of criteria performs as well as experts to detect pelvic LN involvement on [

Indexed as

Positron Emission Tomography Computed TomographyUrinary Bladder NeoplasmsFluorodeoxyglucose F18HumansLymphatic MetastasisLymph NodesNeoplasm StagingRadiopharmaceuticalsRetrospective StudiesFluorodeoxyglucose F18RadiopharmaceuticalsArtificial intelligenceLymph nodesMachine learningPositron emission tomography computed tomographyUrinary bladder neoplasms

Identifiers

PMID36422645
OpenAlexW4309884185

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.