Evidence map›Paper›PMID 37098632›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2023

Diagnostic performance of PET/CT in the detection of liver metastases in well-differentiated NETs.

Freba Grawe, Natalie Rosenberger, Maria Ingenerf, Leonie Beyer, Ralf Eschbach, Andrei Todica, Ricarda Seidensticker, Christine Schmid-Tannwald, Clemens C Cyran, Jens Ricke and 4 more

Open access · goldAbstract read
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2023. 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
2.8field-weighted citation impact, top 9% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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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

14 authors at 1 institution in 1 country.

Freba GraweDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Natalie RosenbergerDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Maria IngenerfDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Leonie BeyerDepartment of Nuclear Medicine, University Hospital, LMU Munich, 81377, Munich, Germany.
Ralf EschbachDepartment of Nuclear Medicine, University Hospital, LMU Munich, 81377, Munich, Germany.
Andrei TodicaDepartment of Nuclear Medicine, University Hospital, LMU Munich, 81377, Munich, Germany.
Ricarda SeidenstickerDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Christine Schmid-TannwaldDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Clemens C CyranDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Jens RickeDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Peter BartensteinDepartment of Nuclear Medicine, University Hospital, LMU Munich, 81377, Munich, Germany.
Christoph J AuernhammerDepartment of Internal Medicine 4, University Hospital, LMU Munich, 81377, Munich, Germany.
Johannes RuebenthalerDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Matthias P FabritiusDepartment of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany. matthias.fabritius@med.uni-muenchen.de.ORCID http://orcid.org/0000-0002-3471-4701
LMU Klinikum · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe aim of this retrospective study was to compare the diagnostic accuracy of somatostatin receptor (SSR)-PET/CT to liver MRI as reference standard in the evaluation of hepatic involvement in neuroendocrine tumors (NET).

methodsAn institutional database was screened for "SSR" imaging studies between 2006 and 2021. 1000 NET Patients (grade 1/2) with 2383 SSR-PET/CT studies and matching liver MRI in an interval of +3 months were identified. Medical reports of SSR-PET/CT and MRI were retrospectively evaluated regarding hepatic involvement and either confirmed by both or observed in MRI but not in SSR-PET/CT (false-negative) or in SSR-PET but not in MRI (false-positive).

resultsMetastatic hepatic involvement was reported in 1650 (69.2%) of the total 2383 SSR-PET/CT imaging studies, whereas MRI detected hepatic involvement in 1685 (70.7%) cases. There were 51 (2.1%) false-negative and 16 (0.7%) false-positive cases. In case of discrepant reports, MRI and PET/CT were reviewed side by side for consensus reading. SSR-PET/CT demonstrated a sensitivity of 97.0% (95%CI: 96.0%, 97.7%), a specificity of 97.7% (95%CI: 96.3%, 98.7%), a PPV of 99.0% (95%CI: 98.4%, 99.4%) and NPV of 93.0% (95%CI: 91.0, 94.8%) in identifying hepatic involvement. The most frequent reason for false-negative results was the small size of lesions with the majority < 0.6 cm.

conclusionThis study confirms the high diagnostic accuracy of SSR-PET/CT in the detection of hepatic involvement in NET patients based on a patient-based analysis of metastatic hepatic involvement with a high sensitivity and specificity using liver MRI imaging as reference standard. However, one should be aware of possible pitfalls when a single imaging method is used in evaluating neuroendocrine liver metastases in patients.

Indexed as

Liver NeoplasmsNeuroendocrine TumorsHumansMagnetic Resonance ImagingPositron-Emission TomographyPositron Emission Tomography Computed TomographyRetrospective StudiesSensitivity and SpecificityLiver metastasesMRINETPET/CTSSR

Identifiers

PMID37098632
PMCPMC10131442
OpenAlexW4366984223

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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.