Evidence map›Paper›PMID 31133445›Full record

ArticleUltrasound in medicine & biology2019

Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

Hersh Sagreiya, Alireza Akhbardeh, Dandan Li, Rosa Sigrist, Benjamin I Chung, Geoffrey A Sonn, Lu Tian, Daniel L Rubin, Jürgen K Willmann

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ultrasound in medicine & biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Review
  11. Review
  12. 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

9 authors.

Hersh SagreiyaDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Alireza AkhbardehDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Dandan LiDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Rosa SigristDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
Benjamin I ChungDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Geoffrey A SonnDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Department of Urology, Stanford University School of Medicine, Stanford, CA, USA.
Lu TianDepartment of Health, Research & Policy, Stanford University, Stanford, CA, USA.
Daniel L RubinDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Department of Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA, USA. Electronic address: dlrubin@stanford.edu.
Jürgen K WillmannDepartment of Radiology, Stanford University School of Medicine, Stanford, CA, USA.

Funding

STANFORD CANCER IMAGING TRAINING (SCIT) PROGRAMT32CA009695 · NCI · STANFORD UNIVERSITY · PI BRUCE L DANIEL, Jeremy Dahl · 1993 to 2026
$7.7M
NCI NIH HHS T32 CA009695
6 · The paper itself

Abstract

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naïve Bayes, quadratic discriminant analysis and support vector machines (SVMs)-was evaluated, using statistical inputs from the tumor, cortex and combined statistical inputs from tumor, cortex and medulla. After leave-one-out cross validation, models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Tumor median SWV performed poorly (AUC = 0.62; p = 0.23). Except logistic regression, all machine learning algorithms reached statistical significance using combined statistical inputs (AUC = 0.78-0.98; p < 7.1 × 10

Indexed as

Machine LearningAdolescentAdultAgedAged, 80 and overAngiomyolipomaCarcinoma, Renal CellDiagnosis, DifferentialElasticity Imaging TechniquesFemaleHumansImage Interpretation, Computer-AssistedKidneyKidney NeoplasmsMaleMiddle AgedAngiomyolipomaMachine learningPoint shear wave elastographyRenal cell carcinomaUltrasound

Identifiers

PMID31133445
PMCPMC6689386

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.