Evidence map›Paper›PMID 34367985›Full record

ArticleFrontiers in oncology2021

Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics.

Jiaru Li, Ziyi Yang, Bowen Xin, Yichao Hao, Lisheng Wang, Shaoli Song, Junyan Xu, Xiuying Wang

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

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 5 pooled it
3.5field-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

18 citing papers in PubMed, 5 syntheses or guidelines pooled it, 29 citations in OpenAlex.

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  10. Clinical application ofJapanese journal of radiology · 2024
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  11. Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023
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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 4 institutions in 2 countries.

Jiaru LiSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Ziyi YangDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
Bowen XinSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Yichao HaoSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Lisheng WangDepartment of Automation, Shanghai Jiao Tong University, Shanghai, China.
Shaoli SongDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
Junyan XuDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
Xiuying WangSchool of Computer Science, The University of Sydney, Sydney, NSW, Australia.
University of Sydney · AUShanghai Medical College of Fudan University · CNFudan University Shanghai Cancer Center · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMicrosatellite instability (MSI) status is an important hallmark for prognosis prediction and treatment recommendation of colorectal cancer (CRC). To address issues due to the invasiveness of clinical preoperative evaluation of microsatellite status, we investigated the value of preoperative

methodsA total of 173 patients that underwent

resultsThe ensemble model was constructed based on two radiomic features and achieved an 82.8% AUC for predicting the MSI status of colorectal cancer patients. The sensitivity, specificity, and accuracy were 83.3, 76.3, and 76.8%, respectively. The significant correlation of the selected two radiomic features with multiple effective clinical features was identified (p < 0.05).

conclusion

Indexed as

18F-FDG PET/CTcolorectal cancermachine learningmicrosatellite statusradiomics

Identifiers

PMID34367985
PMCPMC8339969
OpenAlexW3183972327

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.