Evidence map›Paper›PMID 33807370›Full record

ArticleDiagnostics (Basel, Switzerland)2021

Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight

Olof Jonmarker, Rimma Axelsson, Ted Nilsson, Stefan Gabrielson

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. European journal of nuclear medicine and molecular imaging · 2022
    Pooled it
  2. Article
  3. On the Way for Patients with Prostate Cancer to the Best Use of PSMA.International journal of molecular sciences · 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

4 authors.

Olof JonmarkerCLINTEC, Department of Radiology, Karolinska Institutet, 171 77 Stockholm, Sweden.ORCID 0000-0003-2654-5044
Rimma AxelssonCLINTEC, Department of Radiology, Karolinska Institutet, 171 77 Stockholm, Sweden.
Ted NilssonME Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, 141 86 Stockholm, Sweden.
Stefan GabrielsonCLINTEC, Department of Radiology, Karolinska Institutet, 171 77 Stockholm, Sweden.ORCID 0000-0002-8555-4402

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In prostate cancer, the early detection of distant spread has been shown to be of importance. Prostate-specific membrane antigen (PSMA)-binding radionuclides in positron emission tomography (PET) is a promising method for precise disease staging. PET diagnostics depend on image reconstruction techniques, and ordered subset expectation maximization (OSEM) is the established standard. Block sequential regularized expectation maximization (BSREM) is a more recent reconstruction algorithm and may produce fewer equivocal findings and better lesion detection.

methods

resultsSixty-one patients were reviewed. BSREM identified slightly fewer lesions overall and fewer equivocal findings. ICC was excellent with regards to definitive lymph nodes and bone metastasis identification and poor with regards to equivocal metastasis irrespective of the reconstruction algorithm. The median Cohen's kappa were 0.66, 0.74, 0.61 and 0.43 for OSEM and 0.61, 0.63, 0.66 and 0.53 for BSREM, with respect to the tumor, local lymph nodes, metastatic lymph nodes and bone metastasis detection, respectively.

conclusionsBSREM in the setting of

Indexed as

prostate cancerPSMA-PET/CTreconstruction algorithms

Identifiers

PMID33807370
PMCPMC8067147

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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.