Evidence map›Paper›PMID 34065981›Full record

ArticleDiagnostics (Basel, Switzerland)2021

A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours.

Alessandro Bevilacqua, Diletta Calabrò, Silvia Malavasi, Claudio Ricci, Riccardo Casadei, Davide Campana, Serena Baiocco, Stefano Fanti, Valentina Ambrosini

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 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

  1. Pooled it
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  11. European journal of nuclear medicine and molecular imaging · 2022
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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

9 authors.

Alessandro BevilacquaAdvanced Research Center for Electronic Systems (ARCES), University of Bologna, I-40125 Bologna, Italy.ORCID 0000-0003-2938-5058
Diletta CalabròDepartment of Nuclear Medicine, DIMES, Alma Mater Studiorum, University of Bologna, I-40126 Bologna, Italy.ORCID 0000-0001-7321-1966
Silvia MalavasiAdvanced Research Center for Electronic Systems (ARCES), University of Bologna, I-40125 Bologna, Italy.
Claudio RicciIRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy.
Riccardo CasadeiIRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy.
Davide CampanaIRCCS Azienda Ospedaliero Universitaria di Bologna, I-40138 Bologna, Italy.ORCID 0000-0002-4615-7340
Serena BaioccoAdvanced Research Center for Electronic Systems (ARCES), University of Bologna, I-40125 Bologna, Italy.ORCID 0000-0003-1980-9646
Stefano FantiDepartment of Nuclear Medicine, DIMES, Alma Mater Studiorum, University of Bologna, I-40126 Bologna, Italy.ORCID 0000-0003-1486-2624
Valentina AmbrosiniDepartment of Nuclear Medicine, DIMES, Alma Mater Studiorum, University of Bologna, I-40126 Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest

Indexed as

[68Ga]Ga-DOTANOCbiomarkermachine learningpancreatic neuroendocrine tumourstandardized uptake value

Identifiers

PMID34065981
PMCPMC8150289

What OpenQuestion holds

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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.