Evidence map›Paper›PMID 34862558›Full record

ReviewActa neurochirurgica. Supplement2022

Radiomic Features Associated with Extent of Resection in Glioma Surgery.

Giovanni Muscas, Simone Orlandini, Eleonora Becattini, Francesca Battista, Victor E Staartjes, Carlo Serra, Alessandro Della Puppa

Abstract readReview
PubMed Publisher
In one paragraph

Review in Acta neurochirurgica. Supplement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.8field-weighted citation impact, top 12% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
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

7 authors at 3 institutions in 2 countries.

Giovanni MuscasNeurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy. muscasgi@aou-careggi.toscana.it.
Simone OrlandiniNeurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
Eleonora BecattiniNeurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
Francesca BattistaNeurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
Victor E StaartjesKlinik für Neurochirurgie, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Carlo SerraKlinik für Neurochirurgie, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
Alessandro Della PuppaNeurosurgery Clinic, Department of Neuroscience, Psychology, Pharmacology and Child Health, Careggi University Hospital and University of Florence, Florence, Italy.
University of Florence · ITUniversity Hospital of Zurich · CHUniversity of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.

Indexed as

Brain NeoplasmsGliomaHumansMachine LearningPrognosisRetrospective StudiesBrain gliomaExtent of resectionMachine learningRadiomicsSurgery

Identifiers

PMID34862558
OpenAlexW3215799477

What OpenQuestion holds

Textmetadata
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.