Evidence map›Paper›PMID 39992545›Full record

ArticlePhysical and engineering sciences in medicine2025

Evolved size-specific dose estimates for patient-specific organ doses from chest CT scans based on hybrid patient size vectors.

Wencheng Shao, Ke Yang, Lizhi Lou, Xin Lin, Liangyong Qu, Weihai Zhuo, Haikuan Liu

Abstract read
PubMed Publisher
In one paragraph

Article in Physical and engineering sciences in medicine, 2025. 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
–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

0 citing papers in PubMed.

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.

Wencheng Shao *Institute of Radiation Medicine, Fudan University, Shanghai, China.
Ke Yang *ShanDong Center for Disease Control and Prevention, Jinan, China.
Lizhi LouAnQiu People's Hospital, Shandong, China.
Xin LinInstitute of Radiation Medicine, Fudan University, Shanghai, China.
Liangyong QuDepartment of Radiology, Shanghai Zhongye Hospital, Shanghai, China.
Weihai ZhuoInstitute of Radiation Medicine, Fudan University, Shanghai, China. whzhuo@fudan.edu.cn.
Haikuan LiuInstitute of Radiation Medicine, Fudan University, Shanghai, China. liuhk@fudan.edu.cn.ORCID http://orcid.org/0000-0002-2781-2637

Funding

National Key R&D Program of China 2019YFC0117304National Natural Science Foundation of China 12075064
6 · The paper itself

Abstract

This study aims to develop a neural network-based method for predicting patient-specific organ doses from chest CT scans, utilizing hybrid patient size vectors for enhanced computational efficiency, accuracy, and generality. A dataset of 705 chest CT scans was retrospectively analyzed to construct predictive models for organ dose estimation. The proposed approach employs high dimensional hybrid vectors to represent patient size, combining muti-slice parameters regarding lateral dimension, anteroposterior dimension, and water-equivalent diameter (D

Indexed as

Radiation DosageRadiography, ThoracicTomography, X-Ray ComputedFemaleHumansLungMaleMiddle AgedMonte Carlo MethodNeural Networks, ComputerChest CTHigh dimensionalNeural networksOrgan dosePatient size

Identifiers

PMID39992545

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.