Evidence map›Paper›PMID 40987935›Full record

ArticleRadiological physics and technology2025

Dose reduction in radiotherapy treatment planning CT via deep learning-based reconstruction: a single‑institution study.

Keisuke Yasui, Yuri Kasugai, Maho Morishita, Yasunori Saito, Hidetoshi Shimizu, Haruka Uezono, Naoki Hayashi

Abstract read
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In one paragraph

Article in Radiological physics and technology, 2025. 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. 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.

Keisuke YasuiDivision of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. k-yasui@fujita-hu.ac.jp.ORCID http://orcid.org/0000-0002-5581-463X
Yuri KasugaiDepartment of Radiology, Fujita Health University Hospital, Toyoake, Japan.
Maho MorishitaFaculty of Radiological Technology, School of Medical Sciences, Fujita Health University, Toyoake, Japan.
Yasunori SaitoDepartment of Radiology, Fujita Health University Hospital, Toyoake, Japan.
Hidetoshi ShimizuDivision of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
Haruka UezonoDepartment of Radiation Oncology, Fujita Health University School of Medicine, Toyoake, Japan.
Naoki HayashiDivision of Medical Physics, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To quantify radiation dose reduction in radiotherapy treatment-planning CT (RTCT) using a deep learning-based reconstruction (DLR; AiCE) algorithm compared with adaptive iterative dose reduction (IR; AIDR). To evaluate its potential to inform RTCT-specific diagnostic reference levels (DRLs). In this single-institution retrospective study, 4-part RTCT scans (head, head and neck, lung, and pelvis) were acquired on a large-bore CT. Scans reconstructed with IR (n = 820) and DLR (n = 854) were compared. The 75th-percentile CTDI

Indexed as

Deep LearningImage Processing, Computer-AssistedRadiation DosageRadiotherapy Planning, Computer-AssistedTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRadiotherapy DosageRetrospective StudiesAiCECT dose indexCT reconstruction algorithmDeep learning-based reconstructionTreatment planning

Identifiers

PMID40987935

What OpenQuestion holds

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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.