Evidence map›Paper›PMID 36736536›Full record

ArticleKidney international2023

Utility of new image-derived biomarkers for autosomal dominant polycystic kidney disease prognosis using automated instance cyst segmentation.

Adriana V Gregory, Fouad T Chebib, Bhavya Poudyal, Heather L Holmes, Alan S L Yu, Douglas P Landsittel, Kyongtae T Bae, Arlene B Chapman, Rahbari-Oskoui Frederic, Michal Mrug and 5 more

Open access · hybridAbstract read
In one paragraph

Article in Kidney international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
8.8field-weighted citation impact, top 2% 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

25 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Monogenic Etiologies of Kidney Cysts in the Pediatric Population: An Observational Cohort Study.Clinical journal of the American Society of Nephrology : CJASN · 2026
    Observational
  5. Improving Early Risk Prediction for Autosomal Dominant Polycystic Kidney Disease.Journal of the American Society of Nephrology : JASN · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. End Point Selection in ADPKD Clinical Trials.Kidney international reports · 2025
    Review
  13. Foam sclerotherapy for symptomatic cysts in ADPKD, ADPLD and solitary cysts.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025
    Observational
  14. Article
  15. Article
  16. Characterizing the ADPKD-Kidney international reports · 2025
    Article
  17. Biomarkers of Kidney Disease Progression in ADPKD.Kidney international reports · 2024
    Review
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 8 institutions in 2 countries.

Adriana V GregoryDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA.
Fouad T ChebibDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA.
Bhavya PoudyalDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Heather L HolmesDepartment of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA.
Alan S L YuJared Grantham Kidney Institute, Kansas University Medical Center, Kansas City, Kansas, USA; Division of Nephrology and Hypertension, Kansas University Medical Center, Kansas City, Kansas, USA.
Douglas P LandsittelDepartment of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Kyongtae T BaeDepartment of Diagnostic Radiology, University of Hong Kong, Hong Kong.
Arlene B ChapmanDivision of Nephrology, University of Chicago School of Medicine, Chicago, Illinois, USA.
Rahbari-Oskoui FredericDivision of Renal Medicine, Emory University School of Medicine, Atlanta, Georgia, USA.
Michal MrugDivision of Nephrology, University of Alabama and the Department of Veterans Affairs Medical Center, Birmingham, Alabama, USA.
William M BennettLegacy Transplant Services, Legacy Good Samaritan Hospital, Portland, Oregon, USA.
Peter C HarrisDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA.
Bradley J EricksonDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Vicente E TorresDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA.
Timothy L KlineDivision of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA; Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA. Electronic address: kline.timothy@mayo.edu.
Mayo Clinic · USEmory University · USLegacy Good Samaritan Medical Center · USUniversity of Alabama · USUniversity of Chicago · USUniversity of Hong Kong · HKUniversity of Kansas Medical Center · USUniversity of Pittsburgh · US

Funding

Using Integrated Omics to Identify Dysfunctional Genetic Mechanisms Influencing Schizophrenia and Sleep DisturbancesP20GM130423 · NIGMS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI ANDREW K. GODWIN · 2019 to 2026
$21.5M
ADPKD:Disease Spectrum &Genotype-Phenotype CorrelationsR01DK058816 · NIDDK · MAYO CLINIC ROCHESTER · PI Peter C. Harris · 2001 to 2026
$11.3M
Polycystin Function Resource Development CoreU54DK126126 · NIDDK · UNIVERSITY OF KANSAS MEDICAL CENTER · PI Alan S Yu · 2020 to 2026
$5.8M
Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) IV: Prognosis for End-Stage Renal Disease and Biomarker ValidationR01DK113111 · NIDDK · UNIVERSITY OF KANSAS MEDICAL CENTER · PI YU, ALAN S · 2017 to 2021
$3.5M
Advanced MR Imaging and Image Analytics as a Precision Medicine Tool to Manage ADPKDK01DK110136 · NIDDK · MAYO CLINIC ROCHESTER · PI KLINE, TIMOTHY LEE · 2017 to 2021
$732k
NIDDK NIH HHS K01 DK110136NIDDK NIH HHS R01 DK058816NIDDK NIH HHS R01 DK113111NIDDK NIH HHS U54 DK126126NIGMS NIH HHS P20 GM130423
6 · The paper itself

Abstract

New image-derived biomarkers for patients affected by autosomal dominant polycystic kidney disease are needed to improve current clinical management. The measurement of total kidney volume (TKV) provides critical information for clinicians to drive care decisions. However, patients with similar TKV may present with very different phenotypes, often requiring subjective decisions based on other factors (e.g., appearance of healthy kidney parenchyma, a few cysts contributing significantly to overall TKV, etc.). In this study, we describe a new technique to individually segment cysts and quantify biometric parameters including cyst volume, cyst number, parenchyma volume, and cyst parenchyma surface area. Using data from the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) study the utility of these new parameters was explored, both quantitatively as well as visually. Total cyst number and cyst parenchyma surface area showed superior prediction of the slope of estimated glomerular filtration rate decline, kidney failure and chronic kidney disease stages 3A, 3B, and 4, compared to TKV. In addition, presentations such as a few large cysts contributing significantly to overall kidney volume were shown to be much better stratified in terms of outcome predictions. Thus, these new image biomarkers, which can be obtained automatically, will have great utility in future studies and clinical care for patients affected by autosomal dominant polycystic kidney disease.

Indexed as

Polycystic Kidney, Autosomal DominantBiomarkersDisease ProgressionGlomerular Filtration RateHumansKidneyMagnetic Resonance ImagingPrognosisBiomarkersdisease prognosisglomerular filtration rateimaging biomarkersinstance segmentationoutcome predictionpolycystic kidney disease

Identifiers

PMID36736536
PMCPMC10363210
OpenAlexW4318677107

What OpenQuestion holds

Textmetadata
LicenceTDM
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.