Evidence map›Paper›PMID 28812148›Full record

ArticleEuropean radiology2018

Local recurrence of squamous cell carcinoma of the head and neck after radio(chemo)therapy: Diagnostic performance of FDG-PET/MRI with diffusion-weighted sequences.

Minerva Becker, Arthur D Varoquaux, Christophe Combescure, Olivier Rager, Marc Pusztaszeri, Karim Burkhardt, Bénédicte M A Delattre, Pavel Dulguerov, Nicolas Dulguerov, Eirini Katirtzidou and 4 more

Open access · hybridAbstract read
In one paragraph

Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 3% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Review
  6. Role ofCancers · 2024
    Review
  7. Article
  8. International EANM-SNMMI-ISMRM consensus recommendation for PET/MRI in oncology.European journal of nuclear medicine and molecular imaging · 2023
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Diagnostic Accuracy ofContrast media & molecular imaging · 2022
    Observational
  15. Article
  16. The ability of post-chemoradiotherapy DWI ADCJournal of cancer research and clinical oncology · 2021
    Observational
  17. Correlations between DW-MRI andCancer reports (Hoboken, N.J.) · 2021
    Observational
  18. Are Gadolinium-Enhanced MR Sequences Needed in SimultaneousAJNR. American journal of neuroradiology · 2020
    Article
  19. Article
  20. 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

14 authors at 3 institutions in 2 countries.

Minerva BeckerDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland. Minerva.Becker@hcuge.ch.
Arthur D VaroquauxDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland.
Christophe CombescureCentre for Clinical Research, Geneva University Hospitals, Geneva, Switzerland.
Olivier RagerDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland.
Marc PusztaszeriDivision of Clinical Pathology, Geneva University Hospitals, Geneva, Switzerland.
Karim BurkhardtDivision of Clinical Pathology, Geneva University Hospitals, Geneva, Switzerland.
Bénédicte M A DelattreDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland.
Pavel DulguerovClinic of Otorhinolaryngology Head and Neck Surgery, Geneva University Hospitals, Geneva, Switzerland.
Nicolas DulguerovClinic of Otorhinolaryngology Head and Neck Surgery, Geneva University Hospitals, Geneva, Switzerland.
Eirini KatirtzidouDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland.
Francesca CaparrottiDivision of Radiation Oncology, Geneva University Hospitals, Geneva, Switzerland.
Osman RatibDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland.
Habib ZaidiDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland.
Christoph D BeckerDepartment of Imaging, Divisions of Radiology and Nuclear Medicine, Geneva University Hospitals, University of Geneva, Rue, Gabrielle Perret Gentil 4, CH-1211, Geneva 14, Switzerland.
University of Geneva · CHUniversity Hospital of Geneva · CHAix-Marseille Université · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo determine the diagnostic performance of FDG-PET/MRI with diffusion-weighted imaging (FDG-PET/DWIMRI) for detection and local staging of head and neck squamous cell carcinoma (HNSCC) after radio(chemo)therapy. MATERIALS AND

methodsThis was a prospective study that included 74 consecutive patients with previous radio(chemo)therapy for HNSCC and in whom tumour recurrence or radiation-induced complications were suspected clinically. The patients underwent hybrid PET/MRI examinations with morphological MRI, DWI and FDG-PET. Experienced readers blinded to clinical/histopathological data evaluated images according to established diagnostic criteria taking into account the complementarity of multiparametric information. The standard of reference was histopathology with whole-organ sections and follow-up ≥24 months. Statistical analysis considered data clustering.

resultsThe proof of diagnosis was histology in 46/74 (62.2%) patients and follow-up (mean ± SD = 34 ± 8 months) in 28/74 (37.8%). Thirty-eight patients had 43 HNSCCs and 46 patients (10 with and 36 without tumours) had 62 benign lesions/complications. Sensitivity, specificity, and positive and negative predictive value of PET/DWIMRI were 97.4%, 91.7%, 92.5% and 97.1% per patient, and 93.0%, 93.5%, 90.9%, and 95.1% per lesion, respectively. Agreement between imaging-based and pathological T-stage was excellent (kappa = 0.84, p < 0.001).

conclusionFDG-PET/DWIMRI yields excellent results for detection and T-classification of HNSCC after radio(chemo)therapy. KEY POINTS: • FDG-PET/DWIMRI yields excellent results for the detection of post-radio(chemo)therapy HNSCC recurrence. • Prospective one-centre study showed excellent agreement between imaging-based and pathological T-stage. • 97.5% of positive concordant MRI, DWI and FDG-PET results correspond to recurrence. • 87% of discordant MRI, DWI and FDG-PET results correspond to benign lesions. • Multiparametric FDG-PET/DWIMRI facilitates planning of salvage surgery in the irradiated neck.

Indexed as

AdultAgedCarcinoma, Squamous CellChemoradiotherapyDiffusion Magnetic Resonance ImagingFemaleFluorodeoxyglucose F18Head and Neck NeoplasmsHumansMaleMiddle AgedNeoplasm Recurrence, LocalPositron-Emission TomographyProspective StudiesRadiopharmaceuticalsSquamous Cell Carcinoma of Head and NeckFluorodeoxyglucose F18RadiopharmaceuticalsHead and neck neoplasmsImaging, hybridMagnetic resonance imagingMultimodal imagingNeoplasm recurrence, local

Identifiers

PMID28812148
PMCPMC5740208
OpenAlexW2747517420

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.