Evidence map›Paper›PMID 38270720›Full record

ArticleEuropean radiology experimental2024

Grating-based x-ray dark-field CT for lung cancer diagnosis in mice.

Peiyuan Guo, Li Zhang, Jincheng Lu, Huitao Zhang, Xiaohua Zhu, Chengpeng Wu, Xiangwen Zhan, Hongxia Yin, Zhenchang Wang, Yan Xu and 1 more

Open access · goldAbstract read
In one paragraph

Article in European radiology experimental, 2024. 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
2.4field-weighted citation impact, top 14% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  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

11 authors at 4 institutions in 1 country.

Peiyuan GuoDepartment of Engineering Physics, Tsinghua University, Beijing, China.
Li ZhangDepartment of Engineering Physics, Tsinghua University, Beijing, China.
Jincheng LuDepartment of Engineering Physics, Tsinghua University, Beijing, China.
Huitao ZhangSchool of Mathematical Sciences, Capital Normal University, Beijing, China.
Xiaohua ZhuDepartment of Engineering Physics, Tsinghua University, Beijing, China.
Chengpeng WuDepartment of Engineering Physics, Tsinghua University, Beijing, China.
Xiangwen ZhanNHC Key Laboratory of Human Disease Comparative Medicine, Beijing Engineering Research Center for Experimental Animal Models of Human Critical Diseases, Institute of Laboratory Animal Sciences, Chinese Academy of Medical Sciences (CAMS) and Comparative Medicine Center, Peking Union Medical College (PUMC), Beijing, China.
Hongxia YinDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Zhenchang WangDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Yan XuDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China. yanxu@ccmu.edu.cn.
Zhentian WangDepartment of Engineering Physics, Tsinghua University, Beijing, China. wangzhentian@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-1646-1405
Tsinghua University · CNCapital Medical University · CNCapital Normal University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

National Natural Science Foundation of China 62227804Tsinghua Precision Medicine Foundation 2022PY002Tsinghua University 20213080039Tsinghua University 20221080066
6 · The paper itself

Abstract

backgroundThe low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.

methodsEight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.

resultsThe nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).

conclusionsCompared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules. RELEVANCE STATEMENT: Dark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer. KEY POINTS: • Lung cancer progression was observed using x-ray dark-field CT. • Dark-field modality complements with attenuation modality in lung nodule detection. • Dark-field modality showed a detectability index higher than that attenuation in nodule detection.

Indexed as

Lung NeoplasmsAnimalsLungMiceTomography, X-Ray ComputedX-RaysDisease progressionEarly detection of cancerLung diseasesTomography (x-ray computed)X-rays

Identifiers

PMID38270720
PMCPMC10810771
OpenAlexW4391226825

What OpenQuestion holds

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LicenceCC BY
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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.