Evidence map›Paper›PMID 33796604›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2021

Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features.

Marco Caballo, Andrew M Hernandez, Su Hyun Lyu, Jonas Teuwen, Ritse M Mann, Bram van Ginneken, John M Boone, Ioannis Sechopoulos

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024
    Review
  3. Article
  4. Article
  5. Article
  6. 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

8 authors at 4 institutions in 2 countries.

Marco CaballoRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0002-3700-2785
Andrew M HernandezUniversity of California Davis, Department of Radiology, Sacramento, California, United States.
Su Hyun LyuUniversity of California Davis, Department of Biomedical Engineering, Sacramento, California, United States.
Jonas TeuwenRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0002-1825-1428
Ritse M MannRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.
Bram van GinnekenRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.
John M BooneUniversity of California Davis, Department of Radiology, Sacramento, California, United States.
Ioannis SechopoulosRadboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0001-9615-8205
University Medical Center · USUniversity of California, Davis · USRadboud University Nijmegen · NLUniversity Hospital and Clinics · US

Funding

Breast CT: Final Steps to TranslationR01CA181081 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BOONE, JOHN M · 2015 to 2019
$2.9M
Image Quality Improvement and Performance Assessment of Dedicated Breast CTR01CA181171 · NCI · STICHTING RADBOUD UNIVERSITAIR MEDISCH CENTRUM I.O. · PI SECHOPOULOS, IOANNIS · 2015 to 2020
$1.1M
NCI NIH HHS R01 CA181081NCI NIH HHS R01 CA181171
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

breast cancerbreast computed tomographycomputer-aided diagnosisdeep learningradiomics

Identifiers

PMID33796604
PMCPMC8005916
OpenAlexW3148659025

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.