Evidence map›Paper›PMID 38165579›Full record

ArticleRadiological physics and technology2024

Development of an individual display optimization system based on deep convolutional neural network transition learning for somatostatin receptor scintigraphy.

Shun Matsumoto, Yuki Nakahara, Teppei Yonezawa, Yuto Nakamura, Masahiro Tanabe, Mayumi Higashi, Junji Shiraishi

Abstract read
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In one paragraph

Article in Radiological physics and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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.

Shun MatsumotoDivision of Radiology, Yamaguchi University Hospital, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan. matsu-s@yamaguchi-u.ac.jp.ORCID http://orcid.org/0009-0009-3691-4596
Yuki NakaharaDivision of Radiology, Yamaguchi University Hospital, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan.
Teppei YonezawaDivision of Radiology, Yamaguchi University Hospital, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan.
Yuto NakamuraDivision of Radiology, Yamaguchi University Hospital, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan.
Masahiro TanabeDepartment of Radiology, Yamaguchi University School of Medicine, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan.
Mayumi HigashiDepartment of Radiology, Yamaguchi University School of Medicine, 1-1-1 Minamikogushi, Yamaguchi, 755-8505, Japan.
Junji ShiraishiFaculty of Life Sciences, Kumamoto University, 4-24-1 Kuhonji, Kumamoto, 862-0976, Japan.
Yamaguchi University Hospital · JPYamaguchi University · JPKumamoto Health Science University · JPKumamoto University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Somatostatin receptor scintigraphy (SRS) is an essential examination for the diagnosis of neuroendocrine tumors (NETs). This study developed a method to individually optimize the display of whole-body SRS images using a deep convolutional neural network (DCNN) reconstructed by transfer learning of a DCNN constructed using Gallium-67 (

Indexed as

Neural Networks, ComputerReceptors, SomatostatinDiagnosis, Computer-AssistedRadionuclide ImagingTomography, X-Ray ComputedReceptors, SomatostatinAverage psychological measureNeuroendocrine tumorPaired comparison methodSomatostatin receptor scintigraphyTransfer learning

Identifiers

PMID38165579
OpenAlexW4390512543

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.