Evidence map›Paper›PMID 31725650›Full record

Observational studyMedicine2019

Intravoxel incoherent motion diffusion-weighted imaging evaluated the response to concurrent chemoradiotherapy in patients with cervical cancer.

Hao Bian, Fenghai Liu, Sha Chen, Guoce Li, Yancheng Song, Min Sun, Honghuan Dong

Abstract readObservational Study
In one paragraph

Observational study in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
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  5. Review
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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.

Hao BianDepartment of Radiology.
Fenghai LiuDepartment of Radiology.
Sha ChenDepartment of Ultrasound, Cangzhou Central Hospital, Cangzhou, Hebei, China.
Guoce LiDepartment of Radiology.
Yancheng SongDepartment of Radiology.
Min SunDepartment of Radiology.
Honghuan DongDepartment of Radiology.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To evaluate the application of multiple b values diffusion-weighted imaging based on biexponential signal decay model to predict the response to concurrent chemoradiotherapy in cervical cancer patients.This prospective study enrolled 28 patients (mean age: 50.89 ± 10.70 years) with cervical cancer confirmed by biopsy who received concurrent chemoradiotherapy. Pelvic magnetic resonance scans were performed 2 weeks before, 7 days and 21 days after the initiation of therapy, and 1 month after the end of the treatment. Diffusion-weighted imaging with b values of 0, 50, 450, and 850 s/mm were performed, and tumor volume, means of tumor apparent diffusion coefficient (ADC)min, ADCmean, ADCslow, ADCfast, and Ffast were measured.Pretreatment ADCmin and ADCslow of good outcome group were significantly higher than those of poor outcome group (P < .05). At the 7th day of the treatment, Ffast and its change rate of good outcome group were significantly higher than those of poor outcome group (P < .05). At the 7th day and 21st day of the treatment, Ffast showed a slowly increasing tendency with no significant difference compared with pretreatment value in poor outcome group (P < .05). One month post-treatment, only ADCslow change rate was significantly higher in good outcome group than that in poor outcome group.Intravoxel incoherent motion-related ADC values could be utilized to better predict the outcome of cervical cancer chemoradiotherapy.

Indexed as

AdultChemoradiotherapyDiffusion Magnetic Resonance ImagingFemaleHumansImage Interpretation, Computer-AssistedMiddle AgedProspective StudiesTumor BurdenUterine Cervical Neoplasms

Identifiers

PMID31725650
PMCPMC6867768

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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.