Evidence map›Paper›PMID 27678132›Full record

ArticleEuropean radiology2017

Contribution of mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging in the diagnosis and differentiation of uterine cervical carcinoma.

Meng Lin, Xiaoduo Yu, Yan Chen, Han Ouyang, Bing Wu, Dandan Zheng, Chunwu Zhou

Abstract readComparative StudyEvaluation Study
PubMed Publisher
In one paragraph

Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
3.7field-weighted citation impact, top 7% 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

23 citing papers in PubMed, 45 citations in OpenAlex.

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  13. Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2021
    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 at 2 institutions in 1 country.

Meng LinDepartment of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Xiaoduo YuDepartment of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Yan ChenDepartment of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Han OuyangDepartment of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China. houybj@126.com.
Bing WuGE MR Research China, Beijing, People's Republic of China.
Dandan ZhengGE MR Research China, Beijing, People's Republic of China.
Chunwu ZhouDepartment of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNBeijing General Research Institute of Mining and Metallurgy · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate the potential of various metrics derived from mono-exponential model (MEM), bi-exponential model (BEM) and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in diagnosing and differentiating the pathological subtypes and grades of uterine cervical carcinoma.

methods71 newly diagnosed patients with cervical carcinoma (50 cases of squamous cell carcinoma [SCC] and 21 cases of adenocarcinoma [AC]) and 32 healthy volunteers received DWI with multiple b values. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (alpha), and distributed diffusion coefficient (DDC) were calculated and compared between tumour and normal cervix, among different pathological subtypes and grades.

resultsAll of the parameters were significantly lower in cervical carcinoma than normal cervical stroma except alpha. SCC showed lower ADC, D, f and DDC values and higher D* value than AC; D and DDC values of SCC and ADC and D values of AC were lower in the poorly differentiated group than those in the well-moderately differentiated group.

conclusionCompared with MEM, diffusion parameters from BEM and SEM may offer additional information in cervical carcinoma diagnosis, predicting pathological tumour subtypes and grades, while f and D showed promising significance. KEY POINTS: • DWI-derived parameters by different models are related but provide diversified information. • Commonly used ADC by MEM of DWI overestimates the tissue water diffusivity. • DWI processed by BEM could separate blood perfusion from true diffusion effects. • The derived diffusion-related and perfusion-related parameters by BEM are superior to ADC.

Indexed as

AdenocarcinomaAdultAgedCarcinoma, Squamous CellCell Transformation, NeoplasticDiffusion Magnetic Resonance ImagingFeasibility StudiesFemaleHumansMiddle AgedModels, BiologicalObserver VariationSensitivity and SpecificityUterine Cervical NeoplasmsBi-exponential modelDiffusion-weighted imagingMono-exponential modelStretched exponential modelUterine cervical neoplasm

Identifiers

PMID27678132
OpenAlexW2526979927

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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.