Evidence map›Paper›PMID 42177732›Full record

ArticleEJNMMI physics2026

Evaluation of segmentation accuracy and the improvement of time effectiveness using deep learning-based segmentation in

Tetsu Nakaichi, Yuhei Shimizu, Satoshi Nakamura, Yasunori Shuto, Yusaku Kasai, Atsushi Shishido, Kouji Kunito, Hiroki Nakayama, Mihiro Takemori, Masashi Kawamura and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

15 authors.

Tetsu NakaichiSection of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.ORCID http://orcid.org/0000-0002-6885-4892
Yuhei ShimizuDepartment of Radiological Technology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Satoshi NakamuraSection of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. satonaka@ncc.go.jp.ORCID http://orcid.org/0009-0001-6205-4296
Yasunori ShutoSection of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Yusaku KasaiDepartment of Radiological Technology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Atsushi ShishidoDepartment of Radiological Technology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Kouji KunitoEuro MediTech Co., Ltd, 2-20-4, Higashigotanda, Shinagawa-ku, Tokyo, 141-0022, Japan.
Hiroki NakayamaSection of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Mihiro TakemoriDepartment of Radiology, Hyogo Cancer Center, 13-70 Kitaouji-cho, Akashi City, Hyogo, 673-8588, Japan.
Masashi KawamuraDepartment of Radiological Technology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Takahito ChibaSection of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Hiroyuki OkamotoSection of Radiation Safety and Quality Assurance, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Tairo KashiharaDivision of Research and Development for Boron neutron capture therapy, National Cancer Center Exploratory Oncology Research & Clinical Trial Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Hiroshi IgakiDivision of Research and Development for Boron neutron capture therapy, National Cancer Center Exploratory Oncology Research & Clinical Trial Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Kimiteru ItoDepartment of Diagnostic Radiology, National Cancer Center Hospital, 5- 1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.

Funding

Cancer Intelligence Care Systems. Inc Cancer Intelligence Care Systems. IncNational Cancer Center Research and Development Fund 2022-A-18Young Scientists from the Ministry of Education, Culture, Sports, Science and Technology 19K17218Young Scientists from the Ministry of Education, Culture, Sports, Science and Technology 22K15812
6 · The paper itself

Abstract

backgroundThe efficacy of deep learning-based artificial intelligence segmentation (AI-seg) in

methodsWe analyzed 23 patients treated with

resultsOnly one liver delineation by an MP required correction. Median (IQR) DSCs for liver, spleen, and kidneys were 0.955 (0.950-0.961), 0.929 (0.898-0.944), and 0.929 (0.918-0.942) for manual, 0.988 (0.946-0.998), 0.965 (0.858-0.995), and 0.994 (0.985-0.999) for AI-seg, respectively. All organs met the acceptable DSC threshold ([Formula: see text]0.8). Median HD in liver exceeded 10.0 mm for both methods, with AI-seg exceeding 30.0 mm in a few cases (liver: 3/23; spleen: 2/23) due to hepatomegaly. The median MDA in manual and AI-seg was below 2.0 mm. Median mean absorbed doses in the reference was 4.03 (1.69-5.81) Gy for liver, 1.55 (1.30-2.50) Gy for spleen, and 2.03 (1.45-2.44) Gy for kidneys. Only the kidneys absorbed dose from AI-seg differed significantly, showing a 1.4% increase. Dosimetry using the reference method took significantly less time than the manual approach (47.0 [30.0-58.0] min vs. 54.3 [49.5-67.0] min, p = 0.014).

conclusionsAI-seg with minor adjustment enables faster yet accurate absorbed dose estimation in

Indexed as

177Lu-DOTATATEArtificial intelligenceDosimetryMedical physicistTheranostics

Identifiers

PMID42177732
PMCPMC13221549

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LicenceCC BY
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Registered trials

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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.