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Enregistrement W1854065052 · doi:10.1002/cncr.29698

Defining the role of radiofrequency ablation and stereotactic ablative radiotherapy in patients with high‐risk, early‐stage non‐small cell lung cancer

2015· letter· en· W1854065052 sur OpenAlexaffabout
Alexander V. Louie, Shankar Siva, Suresh Senan

Notice bibliographique

RevueCancer · 2015
Typeletter
Langueen
DomaineMedicine
ThématiqueLung Cancer Diagnosis and Treatment
Établissements canadiensCancer Care Ontario
Organismes subventionnairesnon disponible
Mots-clésSABR volatility modelMedicineRadiofrequency ablationRadiation therapyAblative caseLung cancerRadiosurgeryComorbidityRadiologySurgeryAblationInternal medicine

Résumé

récupéré en direct d'OpenAlex

We read with interest the report by Dupuy et al describing 2-year outcomes in patients with medically inoperable, early-stage non-small cell lung cancer (ES-NSCLC) who were treated with computed tomography-guided radiofrequency ablation (RFA).1 Their data add to the growing body of evidence demonstrating a low incidence of regional disease recurrence in patients with ES-NSCLC after local ablative therapy without any invasive lymph node staging procedures. It is questionable, however, whether their data support the overarching suggestion that RFA is a reasonable first-line alternative to stereotactic ablative radiotherapy (SABR) for patients with medically inoperable ES-NSCLC. The authors argue that the 2-year overall survival (OS) between RFA and SABR is similar, “even in an older and sicker [RFA] cohort.” Although competing risks have a clear impact on survival in these high-risk patients,2 details regarding comorbidities were lacking. The importance of comorbidity in any survival comparison with historical SABR data is highlighted by the example of a 2-year actuarial OS rate of 100% noted among in potentially operable patients from 2 randomized trials who were undergoing lung SABR.3 A purported benefit of RFA over SABR is the preservation of lung function, and the authors cite the Radiation Therapy Oncology Group 0236 trial as reporting a 12% reduction in the diffusing capacity for carbon monoxide after SABR.4 We would like to point out that the article cited by Dupuy et al1 did not report any analysis of pulmonary function. In fact, a subsequent report on the Radiation Therapy Oncology Group 0236 trial noted a nonsignificant decline in the diffusing capacity for carbon monoxide of 6% at 2 years.5 The authors argue that local recurrence rates of 40% were acceptable in their study because of the ability to salvage failures with either SABR or repeat RFA, and because local failures did not appear to impact on OS. This finding needs to be considered within the context of the study's small sample size and risk of making a type II error (ie, failure to reject a false null hypothesis). It would be particularly enlightening if the authors had provided the number of patients at risk in the Kaplan-Meier analyses. Nonetheless, any perceived acceptability of local recurrence rates should be tempered by the psychological impact of recurrent disease, as well as the morbidity and resource implications of salvage treatments. We concur with the editorial accompanying the article that the high local recurrence rates after RFA decrease enthusiasm for its use as a first-line option for high-risk patients who are also eligible for SABR or surgery.6 RFA may have a role in previously irradiated, frail patients, although determining the efficacy of RFA versus best supportive care in a clinical trial would be challenging. Modeling studies can be helpful in such situations; for example, we previously constructed a Markov model to simulate quality-adjusted life years in extremely comorbid patients with ES-NSCLC who were receiving either SABR or best supportive care.7 Combining such data with robust cost information, which can differ greatly from reimbursement rates,8 is crucial to inform cost-effectiveness in the era of increasing awareness of the financial burdens associated with cancer treatment.9 The VU University Medical Center has a research agreement with Varian Medical Systems. Dr. Senan has received honoraria and travel support from Varian Medical Systems for work performed as part of the current study. He has also acted as a paid member of the Advisory Board for Lilly Oncology for work performed outside of the current study. Alexander V. Louie, MD, MSc, FRCPC Department of Radiation Oncology London Regional Cancer Program London, Ontario, Canada; Department of Radiation Oncology VU University Medical Center Amsterdam, the Netherlands Shankar Siva, MBBS, FRANZCR Department of Radiation Oncology Peter MacCallum Cancer Centre East Melbourne, Victoria, Australia Suresh Senan, MRCP, FRCR, PhD Department of Radiation Oncology VU University Medical Center Amsterdam, the Netherlands

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)0,007
score de la tête « metaresearch » (Gemma)0,032
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: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,039

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,032
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0020,003
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,007
Tête enseignante GPT0,249
Écart entre enseignants0,242 · 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
GenreCommentaire

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

En bref

Citations2
Publié2015
Routes d'admission2
Résumé présentoui

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