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Enregistrement W2101360217 · doi:10.1093/ejcts/ezu486

Improving decision-making regarding oesophagectomy after preoperative therapy

2015· letter· en· W2101360217 sur OpenAlexaff
Biniam Kidane, Gail Darling

Notice bibliographique

RevueEuropean Journal of Cardio-Thoracic Surgery · 2015
Typeletter
Langueen
DomaineMedicine
ThématiqueEsophageal Cancer Research and Treatment
Établissements canadiensToronto General HospitalUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésEsophagectomyMedicineGeneral surgeryEsophageal cancerInternal medicineCancer

Résumé

récupéré en direct d'OpenAlex

The report by Stiles et al. in this issue of the journal addresses an important issue for all oesophageal surgeons [1]. Early recurrence of cancer after neodjuvant therapy and surgery is devastating for patients but also for surgeons. If the surgeon could predict such early failure, it is likely that they would not have proceeded with resection. The majority of patients with oesophageal cancer present with locally advanced disease. Level I evidence demonstrates significant survival advantage if they are treated with oesophagectomy and neoadjuvant therapy [2–4]. However, oesophagectomy may be associated with significant morbidity and the combination of neoadjuvant therapy and oesophagectomy is associated with a decreased quality of life up to 3 months following surgery [5, 6]. Health-related quality of life (HRQOL) returns to baseline at about 3–6 months after surgery and appears to surpass baseline levels at about 1 year after surgery [5, 6]. However, in a subset of patients who experience early progression of cancer or death due to cancer within a year of completing their treatment, their HRQOL never returns to baseline levels following therapy [5, 6]. Such patients do not derive benefit from their therapy either in terms of survival or HRQOL. These patients may not have elected to undergo therapy and experience the associated morbidities if they had been aware of such a trajectory. Unfortunately, it has proved challenging to identify such patients in an objective and consistent manner. The work of Stiles et al., published in this issue, addresses this issue using retrospective cohort data from a single large-volume centre [1]. They found clinically defined preoperative risk factors which predict early mortality following neoadjuvant therapy and oesophagectomy. These include poor performance status, poor tumour differentiation and minimal or no clinical response to neodjuvant therapy [1]. Furthermore, they showed that 29% patients with at least two of these risk factors will die within 1 year of surgery [1]. Although limited by its low event rate (36 early deaths within 1 year of surgery), this study provides important data to inform the challenging decision-making that faces both the clinician and the patient when they discuss moving forwards to surgery after having completed neoadjuvant therapy. In these high-risk patients with clearly identifiable risk factors, several options exist as the next step: (i) palliative care, (ii) definitive chemoradiation therapy or (iii) surgery. Given that the natural history of oesophageal cancer may mimic their trajectory with or without treatment, palliative care may be a reasonable option and may actually provide the highest quality of life. In squamous cell cancer, a meta-analysis of three randomized trials did not show any significant difference in overall survival or morbidity between definitive chemoradiation therapy and combined therapy involving neoadjuvant therapy and surgery; furthermore, this meta-analysis showed lower treatment-related mortality in the definitive chemoradiation group [7]. Thus, definitive chemoradiation is a reasonable option, especially for patients with squamous cell cancer. However, in appropriately selected patients, combined therapy including surgery results in improved overall survival compared with other treatment modalities. [2, 8–10] Even in the high-risk patients in Stiles' series, 71% survived beyond 1 year [1]. Thus, proceeding to surgery would also be a reasonable option if the high-risk patient valued cure to the point of risking the 29% chance of early death. Although Stiles et al. suggest that definitive chemoradiation may be a better option for these high-risk patients rather than proceeding to surgery, they astutely point out two issues that further complicate this decision. Firstly, they point out that the 5-year overall survival for this high-risk group is 28%, which implies that the proportion of high-risk patients having early death is roughly the same as those achieving cure despite their high risk. The second issue is related to the difficulty in denying surgery for an otherwise resectable cancer based solely on prediction models. In the high-risk group of patients identified by the model of Stiles et al., 71% of patients survived at least 1 year following surgery and 28% survived 5 years. What other factors distinguish those who die early and those who survive more than 1 year? This study provides a good starting point to advise surgeons when evaluating patients prior to proceeding to surgery after neoadjuvant therapy. Further study is warranted to more clearly identify which patients will develop early recurrence and thereby not benefit from oesophagectomy before surgeons may justifiably not offer surgery to a patient with a resectable cancer. Although the sample size and event rate in the study by Stiles et al. is too small to provide this level of confidence, this study provides some evidence on which to base discussions with the patient and provide opportunity for the patient to apply their own values and preferences to these difficult decisions.

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)0,003
score de la tête « metaresearch » (Gemma)0,036
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: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,019

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,036
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0020,001
Communication savante0,0020,002
Science ouverte0,0010,001
Intégrité de la recherche0,0080,011
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,042
Tête enseignante GPT0,332
Écart entre enseignants0,290 · 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
GenreÉditorial

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'admission1
Résumé présentnon

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