Evidence map›Paper›PMID 34745510›Full record

ArticleJournal of healthcare engineering2021

Multi-Slice Spiral Computed Tomography Image Features under Hybrid Iterative Reconstruction Algorithm in Staging Diagnosis of Bladder Cancer.

Lan Zang

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Lan ZangDepartment of Radiology, Hangzhou Future Sci-tech City Hospital, Hangzhou 311100, Zhejiang, China.ORCID 0000-0001-7602-631X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study was aimed to explore the accuracy of multi-slice spiral computed tomography (CT) scan in preoperative staging diagnosis of bladder cancer based on hybrid iterative reconstruction algorithm, so as to provide a more reasonable supporting basis for guiding clinical work in the future. Methods: Retrospectively, 120 patients admitted to hospital from July 2019 to April 2021, who were confirmed to be with urothelial carcinoma of the bladder by pathological examination after surgical treatment, were selected. CT images before processing were set as the control group and those after processing were set as the observation group according to whether they were processed by the hybrid iterative algorithm. Postoperative pathological examination was utilized as the standard for analysis. The accuracy and consistency of the two methods were compared. Results: The accuracy of the results of each stage of the observation group (T1 stage: 91.09%, T2 stage: 89.66%, T3 stage: 88.89%, and T4 stage: 88.89%) and consistency (T1 stage: 0.66, T2 stage: 0.69, T3 stage: 0.71, and T4 stage: 0.82) were higher than those of the control group (accuracy: T1-57.01%, T2-48.28%, T3-44.44%, and T4-44.44%). The consistency was as follows: T1-0.32, T2-0.24, T3-0.37, and T4-0.43, and the comparison was statistically significant ( Conclusion: The adoption value of the image features based on the hybrid iterative reconstruction algorithm in the diagnosis of bladder cancer staging was higher than that of the conventional multi-slice spiral CT, indicating that the hybrid iterative reconstruction algorithm had a good adoption prospect in clinical examination.

Indexed as

Carcinoma, Transitional CellUrinary Bladder NeoplasmsAlgorithmsHumansNeoplasm StagingRetrospective StudiesTomography, Spiral ComputedTomography, X-Ray Computed

Identifiers

PMID34745510
PMCPMC8566054
OpenAlexW3209529406

What OpenQuestion holds

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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.