Evidence map›Paper›PMID 36417020›Full record

ArticleInsights into imaging2022

Value of texture analysis based on dynamic contrast-enhanced magnetic resonance imaging in preoperative assessment of extramural venous invasion in rectal cancer.

Junjie Fang, Wei Sun, Dan Wu, Peipei Pang, Xiuyu Guo, Chunyao Yu, Wei Lu, Guangyu Tang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Junjie FangDepartment of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No. 301 Yanchang Road, Shanghai, 200072, People's Republic of China.
Wei SunDepartment of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Haishu District, Ningbo, 315010, Zhejiang, People's Republic of China.
Dan WuDepartment of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Haishu District, Ningbo, 315010, Zhejiang, People's Republic of China.
Peipei PangDepartment of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, 310002, People's Republic of China.
Xiuyu GuoDepartment of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Haishu District, Ningbo, 315010, Zhejiang, People's Republic of China.
Chunyao YuDepartment of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Haishu District, Ningbo, 315010, Zhejiang, People's Republic of China.
Wei LuDepartment of Radiology, Hwa Mei Hospital, University of Chinese Academy of Sciences, No. 41 Northwest Street, Haishu District, Ningbo, 315010, Zhejiang, People's Republic of China. luwei19@ucas.ac.cn.
Guangyu TangDepartment of Radiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, No. 301 Yanchang Road, Shanghai, 200072, People's Republic of China. tgy17@tongji.edu.cn.
University of Chinese Academy of Sciences · CNShanghai Tenth People's Hospital · CN

Funding

Key Laboratory of Diagnosis and Treatment of Digestive System Tumors of Zhejiang Province 2019E10020Ningbo Clinical Research Center for Medical Imaging 2021L003Ningbo Medical Science and Technology Plan Project 2021Y09Provincial and Municipal Coconstruction Key Discipline for Medical Imaging 2022-S02the Medical Science and Technology Project of Zhejiang Province 2019RC263the Ningbo Natural Science Foundation, China 202003N4200the Ningbo Natural Science Foundation, China 202003N4218
6 · The paper itself

Abstract

objectiveAccurate preoperative assessment of extramural vascular invasion (EMVI) is critical for the treatment and prognosis of rectal cancer. The aim of our research was to develop an assessment model by texture analysis for preoperative prediction of EMVI. MATERIALS AND

methodsThis study enrolled 44 rectal patients as train cohort, 7 patients as validation cohort and 18 patients as test cohort. A total of 236 texture features from DCE MR imaging quantitative parameters were extracted for each patient (59 features of K

resultsThe T-score calculated using the 4 selected key features were significantly correlated with EMVI (p < 0.010). The area under the receiver operating characteristic curve (AUC) was 0.797 for discriminating between EMVI-positive and EMVI-negative patients with a sensitivity of 88.2% and specificity of 70.4%. The conventional MRI assessment of EMVI had a sensitivity of 23.53% and a specificity of 96.30%. The assessment model showed a greatly improved performance with an AUC of 0.954 (sensitivity, 88.2%; specificity, 92.6%) in train cohort, 0.833 (sensitivity, 66.7%; specificity, 100%) in validation cohort and 0.877 in test cohort, respectively.

conclusionsThe assessment model showed an excellent performance in preoperative assessment of EMVI. It demonstrates strong potential for improving the accuracy of EMVI assessment and provide a reliable basis for individualized treatment decisions.

Indexed as

Machine learningMagnetic resonance imagingNeoplasm stagingRectal neoplasms

Identifiers

PMID36417020
PMCPMC9684393
OpenAlexW4309700098

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.