Evidence map›Paper›PMID 38639770›Full record

ArticleClinical neuroradiology2024

Multi-Dynamic-Multi-Echo-based MRI for the Pre-Surgical Determination of Sellar Tumor Consistency: a Quantitative Approach for Predicting Lesion Resectability.

Mehmet Salih Yildirim, Victor Ulrich Schmidbauer, Alexander Micko, Lisa Lechner, Michael Weber, Julia Furtner, Stefan Wolfsberger, Intesar-Victoria Malla Houech, Anna Cho, Gregor Dovjak and 3 more

Abstract read
In one paragraph

Article in Clinical neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

13 authors.

Mehmet Salih YildirimDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Victor Ulrich SchmidbauerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Alexander MickoDepartment of Neurosurgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.
Lisa LechnerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Michael WeberDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Julia FurtnerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Stefan WolfsbergerDepartment of Neurosurgery, Medical University of Graz, Auenbruggerplatz 29, 8036, Graz, Austria.
Intesar-Victoria Malla HouechDepartment of Diagnostic Imaging, Medical University of Sofia, Sveti Georgi Sofiyski 1, 1431, Sofia, Bulgaria.
Anna ChoDepartment of Neurosurgery, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Gregor DovjakDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Gregor KasprianDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Daniela PrayerDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
Wolfgang MarikDepartment of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria. wolfgang.marik@meduniwien.ac.at.ORCID http://orcid.org/0000-0002-1193-0970

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePre-surgical information about tumor consistency could facilitate neurosurgical planning. This study used multi-dynamic-multi-echo (MDME)-based relaxometry for the quantitative determination of pituitary tumor consistency, with the aim of predicting lesion resectability.

methodsSeventy-two patients with suspected pituitary adenomas, who underwent preoperative 3 T MRI between January 2020 and January 2022, were included in this prospective study. Lesion-specific T1-/T2-relaxation times (T1R/T2R) and proton density (PD) metrics were determined. During surgery, data about tumor resectability were collected. A Receiver Operating Characteristic (ROC) curve analysis was performed to investigate the diagnostic performance (sensitivity/specificity) for discriminating between easy- and hard-to-remove by aspiration (eRAsp and hRAsp) lesions. A Mann-Whitney-U-test was done for group comparison.

resultsA total of 65 participants (mean age, 54 years ± 15, 33 women) were enrolled in the quantitative analysis. Twenty-four lesions were classified as hRAsp, while 41 lesions were assessed as eRAsp. There were significant differences in T1R (hRAsp: 1221.0 ms ± 211.9; eRAsp: 1500.2 ms ± 496.4; p = 0.003) and T2R (hRAsp: 88.8 ms ± 14.5; eRAsp: 137.2 ms ± 166.6; p = 0.03) between both groups. The ROC analysis revealed an area under the curve of 0.72 (95% CI: 0.60-0.85) at p = 0.003 for T1R (cutoff value: 1248 ms; sensitivity/specificity: 78%/58%) and 0.66 (95% CI: 0.53-0.79) at p = 0.03 for T2R (cutoff value: 110 ms; sensitivity/specificity: 39%/96%).

conclusionMDME-based relaxometry enables a non-invasive, pre-surgical characterization of lesion consistency and, therefore, provides a modality with which to predict tumor resectability.

Indexed as

AdenomaPituitary NeoplasmsAdultAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedPreoperative CareProspective StudiesSensitivity and SpecificityMultiparametric Magnetic Resonance ImagingPituitary AdenomaProspective StudiesReference StandardsSellar Lesions

Identifiers

PMID38639770
PMCPMC11339083

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.