Evidence map›Paper›PMID 34993060›Full record

ArticleQuantitative imaging in medicine and surgery2022

Efficacy of

Yi Nuo, Aimei Li, Lulu Yang, Hailin Xue, Feng Wang, Liwei Wang

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 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
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. The value ofDiscover oncology · 2024
    Article
  5. The Role of [Cancers · 2022
    Review
  6. Frontiers in oncology · 2022
    Article
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

6 authors.

Yi Nuo *Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Aimei Li *Department of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Lulu YangDepartment of Pathology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Hailin XueDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Feng WangDepartment of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Liwei WangDepartment of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis retrospective study aimed to investigate the efficacy of the combined application of biparametric magnetic resonance imaging (bpMRI) and

methodsThe 105 patients with suspected PCa included in the study underwent bpMRI and PET/CT. BpMRI examinations included conventional sequences and diffusion-weighted imaging (DWI) sequences. Major lesions were qualitatively diagnosed according to the Prostate Imaging Reporting and Data System (PI-RADS). A PET/CT scan was started 60 min after intravenous

resultsOf the 105 patients, 68 patients were diagnosed with PCa, and 37 patients had benign prostatic lesions. With a PI-RADS score ≥3 as the diagnostic threshold, the accuracy of bpMRI in identifying benign and malignant prostate lesions was similar to that of PET/CT (SUV

conclusionsThe combined application of bpMRI and PET improves the accuracy of the qualitative diagnosis of prostate lesions, and its diagnostic efficacy for risk stratification in patients with intermediate- to high-risk PCa is similar to that of PET/CT and higher than that of bpMRI alone.

Indexed as

magnetic resonance imaging (MRI)positron emission computed tomography (PET)Prostate cancer (PCa)prostate-specific membrane antigen (PSMA)

Identifiers

PMID34993060
PMCPMC8666784

What OpenQuestion holds

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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.