Evidence map›Paper›PMID 34618997›Full record

ArticleCancer science2021

Prediction of the effects of radiation therapy in esophageal cancer using diffusion and perfusion MRI.

Peiliang Wang, Xin Wang, Liang Xu, Jinming Yu, Feifei Teng

Abstract read
In one paragraph

Article in Cancer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Peiliang WangDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Cheeloo college of medicine, Shandong University, Jinan, China.ORCID https://orcid.org/0000-0001-7479-0277
Xin WangDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Liang XuDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Jinming YuDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Cheeloo college of medicine, Shandong University, Jinan, China.
Feifei TengDepartment of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.ORCID https://orcid.org/0000-0001-8101-9405

Funding

National Natural Science Foundation of China NSFC81803066Shandong Provincial Natural Science Foundation ZR2019BH046
6 · The paper itself

Abstract

Chemoradiation therapy (CRT) of locally advanced esophageal cancer (LAEC), although improving outcomes of patients, still results in 50% of local failure. An early prediction could identify patients at high risk of poor response for individualized adaptive treatment. We aimed to investigate physiological changes in LAEC using diffusion and perfusion magnetic resonance imaging (MRI) for early prediction of treatment response. In the study, 115 LAEC patients treated with CRT were enrolled (67 in the discovery cohort and 48 in the validation cohort). MRI scans were performed before radiotherapy (pre-RT) and at week 3 during RT (mid-RT). Gross tumor volume (GTV) of primary tumor was delineated on T2-weighted images. Within the GTV, the hypercellularity volume (V

Indexed as

ChemoradiotherapyDiffusion Magnetic Resonance ImagingEsophageal NeoplasmsFemaleHumansMagnetic Resonance AngiographyMalePrecision MedicineRadiographic Image Interpretation, Computer-AssistedSurvival AnalysisTreatment Outcomediffusionlocally advanced esophageal cancermagnetic resonance imagingperfusionradiation therapy

Identifiers

PMID34618997
PMCPMC8645758

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.