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Enregistrement W1513329038 · doi:10.5772/22202

Emerging Imaging and Operative Techniques for Glioma Surgery

2012· book-chapter· en· W1513329038 sur OpenAlexaff
Claude-Édouard Châtillon, Kevin Petrecc

Notice bibliographique

RevueInTech eBooks · 2012
Typebook-chapter
Langueen
DomaineMedicine
ThématiqueGlioma Diagnosis and Treatment
Établissements canadiensMcGill UniversityMontreal Neurological Institute and Hospital
Organismes subventionnairesnon disponible
Mots-clésGliomaMedicineRadiologyMedical physicsCancer research

Résumé

récupéré en direct d'OpenAlex

IntroductionMalignant gliomas are the most common adult primary brain cancers and are amongst the most devastating of human malignancies.These cancers are characterized by high proliferation and invasion into normal brain.Treatment consists of a combination of surgery, radiotherapy, and chemotherapy.Despite years of experience and refinement of these treatments, patients suffering from World Health Organization grade four gliomas have a mean survival of 14 months (Stupp et al., 2005).The goal of surgery is to remove the entirety of the tumor as strong emerging evidence suggests that completeness of resection improves cancer control and lengthens survival.Extent of resection, for malignant gliomas, is based on gadolinium-enhanced magnetic resonance imaging (MRI).In cases of complete resection, radiotherapy is then delivered to a 2 cm border along the resection cavity.In cases of incomplete resection, radiotherapy is delivered to the residual tumor and a 2 cm border along the residual tumor and resection cavity.The rational for this radiotherapy strategy is that invasive cancer cells can be found up to 2 cm distant from the main tumor mass.Studies examining the location of malignant glioma recurrence following surgery and adjuvant radiotherapy and chemotherapy have found that most cancers recur within a 1 cm border along the surgical resection cavity, even in cases in which no residual gadoliniumenhancing tumor was evident on immediate post-operative MRI.This suggests that gadolinium-enhanced MRI does not sufficiently reveal the entire tumor resulting in residual tumor post-operatively.Other common MRI sequences, including FLAIR and T2, do not adequately distinguish non-gadolinium enhancing cancer cells from peritumoral edema.The inability to accurately visualize the whole tumor, including invasive cells, on imaging decreases the likelihood of complete resection.Recently, attempts to visualize malignant gliomas with newer imaging techniques, including metabolic labeled positron emission tomography (PET), have identified tumor borders beyond those seen with gadoliniumenhanced MRI.These technologies may have profound implications regarding surgical planning in malignant glioma surgery.Historically, extent of tumor resection has been determined by the surgeon's qualitative assessment at the time of operation, often reporting a gross total resection.More recently, the use of immediate post-operative MRI has revealed that complete resection of the gadolinium-enhancing portion of the tumor is achieved at a much lower rate.This overestimation by surgeons is, in part, owing to the difficulty distinguishing cancer cells www.intechopen.comAdvances in Cancer Management 140 from normal brain.Since malignant gliomas are highly invasive tumors, the margin between tumor and normal brain is typically not obvious.Reluctant to cause an irreversible neurological deficit, surgeons will error on the side of caution.The downside is that malignant cancer cells will remain.Since adjuvant radiation and chemotherapies are only modestly effective (Stupp et al., 2005), these cancer cells that remain along the border of the original tumor mass will recur.Intraoperative tools designed to help surgeons distinguish cancer cells from normal brain include ultrasound and fluorescence guided surgical resection.Comparative studies using these tools have shown higher rates of complete resection compared to standard operating techniques.Here we review current and emerging imaging technologies designed to better visualize the tumor on preoperative imaging.We also review developing surgical technologies to help surgeons distinguish cancer cells from normal brain intraoperatively.The development of these technologies will lead to an increased rate of complete resection and thus improved cancer control. Preoperative glioma imaging Tumour delineation in gliomaThe accurate characterisation of tumour size and location is crucial to decisions in diagnosis, presurgical planning and adjuvant therapy, as well as assessment of treatment response or failure in patients with a glioma.The diagnostic and tumour delineation gold standard remains the MRI using T1 with and without gadolinium-enhancement, T2 and fluid attenuation inversion recovery (FLAIR) sequences.However, classical MRI sequences provide an indirect assessment of tumour grade by relying on tissue density, fluid content and blood-brain barrier breakdown patterns.Advanced MRI techniques aim to measure water molecule diffusion patterns (diffusion weighted imaging), cerebral blood volume (perfusion-weighted MRI) and metabolic tissue composition (magnetic resonance spectroscopy), which are more direct measures of tumour metabolism.Furthermore, PET using the classical metabolic tracer fluorodeoxyglucose (FDG) or novel amino acid tracers have been shown to provide complimentary information to MRI in presurgical planning and improve outcome in low and high grade gliomas. Conventional MRISince its first clinical use in the 1980s, MRI rapidly became the imaging modality of choice in cerebral tumors, owing to its resolution, grey-white matter distinction and radiation sparing advantages over computed tomography (CT).To image glioma invasion, changes in tissue density, fluid content and blood brain barrier breakdown underlie the abnormalities seen on classical MRI sequences.Tumour delineation in low grade gliomas (LGG) is usually determined by the extent of hyperintense signal on a T2 sequence.Edema is usually absent in LGGs and should therefore not be a confounder of T2 hyperintensity.Areas of enhancement after gadolinium enhancement are typically not seen in LGG and are, in fact, a marker of anaplastic transformation.In contrast, tumour delineation in high grade gliomas (HGG) cannot rely on the T2 sequence, due to the often significant edema surrounding the lesion.The area of enhancement on the gadolinium-enhanced T1 sequence is usually used for pre-operative planning, post-operative extent of resection assessment and evaluation of progression, treatment response or recurrence.www.intechopen.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,020

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0000,002
Communication savante0,0010,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0060,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,303
Écart entre enseignants0,275 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2012
Routes d'admission1
Résumé présentoui

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