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Enregistrement W4392383680 · doi:10.1093/neuonc/noae002

Pushing the boundaries of radiation technology for the management of central nervous system tumors

2024· article· en· W4392383680 sur OpenAlexaff
Arjun Sahgal, Susan M. Chang

Notice bibliographique

RevueNeuro-Oncology · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueGlioma Diagnosis and Treatment
Établissements canadiensHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Organismes subventionnairesVarian Medical Systems
Mots-clésCentral nervous systemNeuroscienceMedicineBiology

Résumé

récupéré en direct d'OpenAlex

The pace of change in our knowledge of central nervous system (CNS) tumors has accelerated in the past 5 years with several tumor types reclassified according to molecular status. The clinical applicability of such molecular classifications is catching up with the application of CNS-specific targeted therapies into day-to-day management algorithms. Radiotherapy (RT) has also undergone major technological advances in the last decade, and the manuscripts in this supplement highlight the challenges and progress in the use of RT for CNS malignancies. In general, the incorporation of multifield and arc-based intensity modulation delivery, image guidance, stereotaxis, and MR simulation is considered routine.1 Treatment planning systems have also evolved to allow for multimodal image registration, auto-contouring using sophisticated segmentation algorithms with the incorporation of artificial intelligence and machine learning, and rapid dose calculation to allow for dose accumulation. In 2024, we are at a crossroads in determining the future directions of core RT-based research for glioma. A fundamental re-think is required to determine what we are trying to achieve, and how we will measure progress with novel endpoints as opposed to the emphasis on overall survival. Moving away from traditional treatment planning concepts by applying functional and metabolic MRI sequences to personalize glioma radiotherapy is one such direction. Incorporating new technologies that allow for treatment adaption is another strategy, as we now have the ability to routinely image with MRI during a course of RT and re-plan to accommodate tumor migration that has been recently described during a standard 6-week course of therapy.2,3 It is only through the coupling of the latest in adaptive radiation technologies, with advanced MR sequences and novel PET-MRI tracers, that we can achieve true personalization of radiation volumes to allow for safe clinical target volume (CTV) margin reduction and ultimately tip the therapeutic ratio toward improved efficacy and less toxicity. The first comprehensive review of the literature on how and why to incorporate online and offline adaptive radiation strategies for glioma is provided by Tseng and colleagues. The potential for the incorporation of multimodal imaging is further emphasized by Kim and colleagues to address emerging treatment resistance in rational combination with novel systemic therapies, to ultimately permit improvement in glioblastoma outcomes and true individualization of patient care. Prospective studies to validate the use of imaging biomarkers to optimize RT treatment are greatly needed. For glioma and other primary CNS malignancies, it is very disappointing that we have failed to move the needle within the radiotherapy space for our patients. In particular, clinical trials and routine practice still incorporate significant margins of “normal” brain tissue beyond the enhancing mass, despite the routine incorporation of MRI into treatment planning. In fact, the inertia that continues to prevent practice-changing approaches to the CTV may have adversely impacted patient outcomes, and the field must innovate to reduce the prohibitive volume of brain that we subject to therapeutic doses of radiation. With respect to dose escalation, this concept continues to fail in improving outcomes for patients with high-grade glioma, and this dates back two decades ago to the initial evaluation of stereotactic radiosurgery (SRS) and brachytherapy boosting. Although the application of proton therapy had initial promise due to its dosimetric properties of sparing normal brain tissue beyond the targeted volume, it too failed to improve the toxicity profile when treating with standard radiotherapy doses for high-grade glioma. However, proton technology continues to develop, and Kotecha and colleagues summarize the literature to better inform where this expensive and resource-intensive treatment may have an impact on the management of patients with CNS malignancies. They highlight the importance of well-conducted prospective clinical trials to provide high-level evidence to potentially support the use of protons in multiple settings. One of the most significant advances for patients with brain metastases in the past decade was the acceptance of SRS for patients with brain metastases. Confirmation of improved neurocognitive outcomes specific to SRS, for both intact and surgical cavities, essentially broke the back of whole-brain radiation therapy (WBRT) such that standard WBRT is considered a therapy of last resort for patients presenting with limited brain metastases.4,5 The field has since exploded with technological developments allowing for frameless SRS, treatment of multiple metastases such that the number of lesions is no longer a limiting factor, hypofractionated SRS which is currently being evaluated in randomized trials for larger intact metastases and surgical cavities to improve outcomes, and a new focus on clinical trials aimed at optimizing the sequencing of SRS with blood–brain barrier penetrant targeted agents, immunotherapy, and surgical management. Palmer and colleagues provide a review on the latest advances in the management of brain metastases. Balancing the potential benefit of SRS, Chao and colleagues extensively review the potential life-threatening complication of radiation necrosis, highlighting recent practice management guidelines by the International Stereotactic Radiosurgery Society.6 Leveraging the multimodal treatment for both primary and metastatic brain tumors, Wang and colleagues explore the current progress and the future of combination therapy with immune checkpoint inhibitors (ICIs) and RT. From theoretical and preclinical standpoints, the synergistic role of ICI and RT is quite logical; however, evidence-based approaches are critically needed to provide guidance as to how and when to use ICIs. As radiation technologies evolved to allow for millimetric precision without invasive immobilization for extracranial tumors, the spine was one of the first sites to develop what we now know as spine stereotactic body radiotherapy (SBRT). The global adoption of spine SBRT is in part due to recent level-1 evidence demonstrating an improvement in the complete response rate for pain,7 and those data that report far greater local control than previously achieved with conventional low-dose palliative spinal radiotherapy.8 The development of spine SBRT also pressed spine surgeons to develop minimally invasive technologies and surgical approaches to complement spine SBRT, which gave rise to the concept of separation surgery.9 Ultimately, patients with spinal metastases are now benefitting from far more thoughtful care and the potential for aggressive management with a low risk of serious adverse events when presenting with a complex “mass" type (epidural and/or paraspinal tumor extension) tumor. One day, we hope we can prevent patients from presenting with neurologic compromise from the potentially paralyzing condition of malignant epidural spinal cord compression. The current state-of-the-art in spine SBRT is summarized by Redmond and colleagues. We thank the authors for their valuable contributions and hope readers enjoy this supplement thematically aimed at providing an overview of how radiation technologies are evolving to improve the lives of CNS patients who need radiation. This article appears as part of the supplement “Pushing the Boundaries of Radiation Technology for the Central Nervous System,” sponsored by Varian Medical Systems. Arjun Sahgal: grants from Elekta, Varian, Seagen Inc., BrainLAB, consulting fees from Varian, Elekta, BrainLAB, Merck, Abbvie, Roche, honoraria from AstraZeneca, Elekta, Varian, BrainLAB, Accuray, Seagen Inc., travel expenses from Elekta, Varian, BrainLAB, roles on leadership board as Vice President of International Stereotactic Radiosurgery Society and other interests as member of Elekta MR-Linac Research Consortium, member of Elekta Clinical Steering Committee, chair of Elekta Oligometastases Group and Elekta Gamma Knife Icon Group.

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)0,018
score de la tête « metaresearch » (Gemma)0,026
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: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,097

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0180,026
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0010,006
Communication savante0,0080,012
Science ouverte0,0020,005
Intégrité de la recherche0,0040,009
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

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 Opus0,014
Tête enseignante GPT0,281
Écart entre enseignants0,267 · 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
GenreEmpirique

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

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentnon

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