Evidence map›Paper›PMID 36717727›Full record

ArticleScientific reports2023

Mask R-CNN assisted 2.5D object detection pipeline of

Di Xu, Martin Ma, Minsong Cao, Amar U Kishan, Nicholas G Nickols, Fabien Scalzo, Ke Sheng

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.3field-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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

7 authors at 4 institutions in 1 country.

Di XuComputer Science, University of California, Los Angeles, CA, 90035, USA.
Martin MaRadiation Oncology, University of California, Los Angeles, CA, 90035, USA.
Minsong CaoRadiation Oncology, University of California, Los Angeles, CA, 90035, USA.
Amar U KishanRadiation Oncology, University of California, Los Angeles, CA, 90035, USA.
Nicholas G NickolsDepartment of Radiation Oncology, VA Greater Los Angeles Healthcare System, Los Angeles, CA, 90035, USA.
Fabien ScalzoComputer Science, Pepperdine University, 24255 Pacific Coast Hwy, Los Angeles, CA, 90263, USA.
Ke ShengRadiation Oncology, University of California, Los Angeles, CA, 90035, USA. ke.sheng@ucsf.edu.
University of California, Los Angeles · USPepperdine University · USUniversity of California, San Francisco · USVA Greater Los Angeles Healthcare System · US

Funding

Development of A High Throughput Image-Guided IMRT System forPreclinical ResearchR01CA259008 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SHENG, KE · 2021 to 2025
$2.3M
NCI NIH HHS R01CA259008
6 · The paper itself

Abstract

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) is a molecular and functional imaging modality with better restaging accuracy over conventional imaging for detecting prostate cancer in men suspected of lymph node (LN) progression after definitive therapy. However, the availability of PSMA PET/CT is limited in both low-resource settings and for repeating imaging surveillance. In contrast, CT is widely available, cost-effective, and routinely performed as part of patient follow-up or radiotherapy workflow. Compared with the molecular activities, the morphological and texture changes of subclinical LNs in CT are subtle, making manual detection of positive LNs infeasible. Instead, we harness the power of artificial intelligence for automated LN detection on CT. We examined

Indexed as

Positron Emission Tomography Computed TomographyProstatic NeoplasmsArtificial IntelligenceGallium IsotopesGallium RadioisotopesHumansLymph NodesMaleProstateProstatectomyTomography, X-Ray Computedgallium 68 PSMA-11Gallium IsotopesGallium Radioisotopes

Identifiers

PMID36717727
PMCPMC9886937
OpenAlexW4318484049

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.