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Enregistrement W2792597722 · doi:10.1111/bju.13978

Prostate cancer biochemical recurrence after salvage radiotherapy: first look into risk stratification and prognosis

2018· letter· en· W2792597722 sur OpenAlexaff
Félix Couture, Côme Tholomier, Kevin C. Zorn

Notice bibliographique

RevueBritish Journal of Urology · 2018
Typeletter
Langueen
DomaineMedicine
ThématiqueProstate Cancer Treatment and Research
Établissements canadiensMcGill University Health CentreUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésBiochemical recurrenceProstate cancerMedicineProstatectomyOncologyAndrogen deprivation therapyRadiation therapyInternal medicineMultivariate analysisCohortSalvage therapybreakpoint cluster regionCancerChemotherapy

Résumé

récupéré en direct d'OpenAlex

As salvage radiation therapy (SRT) is commonly offered as a treatment option to select patients with prostate cancer who have biochemical recurrence (BCR) after radical prostatectomy (RP), the uro-oncology community is in strong need of tangible data regarding patients who have a 'second' BCR after such therapy. The multi-institution team led by Tumati and Jackson 1 provides new insight into the outcomes of second biochemical failures, offering a novel risk stratification system with the help of prognostic factors. Their analysis of 286 patients offers retrospective prognostic clues that could guide further work trying to better understand the natural history and management of such patients. At the core of their findings is two risk stratification grouping systems predicting event rates for freedom from distant metastases (FFDM) and for prostate cancer-specific survival (PCSS), both based on multivariate analysis. Variables such as interval from RP to second BCR, Gleason score, and concurrent androgen-deprivation therapy (ADT) proved to be of significance. Of note, their cohort also allowed them to establish an overall survival from time of second BCR diagnosis of 13 years, a surprisingly hopeful figure for such treatment-resistant disease. Whilst the classification proposed by Tumati et al. 1 brings valuable numbers to this poorly understood patient group, some questions remain about parts of the analysis performed. First, it came as a surprise to our team that, on multivariate analysis, FFDM and PCSS did not significantly correlate with clinical staging or nodal involvement, two well-established predictors of poor outcomes after RP 2. Although both risk factors either reached or approached statistical significance on univariate analysis, we have difficulty explaining why such clear prognosticators would not reach significance with other factors controlled. Maybe the lack of systematic, complete lymph node sampling could partially explain the lack of significant correlation with N staging. Also, whilst some may find it surprising that positive surgical margins were insignificantly associated with better long-term outcomes, other published studies have actually shown similar results, sometimes with statistical significance 3, 4. This could have very important implications, as it suggests that treatment options for patients with positive margins might be applied more selectively, and that systematic SRT might not be necessary in most cases. We also would like to question the risk grouping proposed in the article to predict FFDM after 6 years (6-year FFDM). By combining patients with no risk factors (6-year FFDM = 75.8%) to those with either Gleason score 8–10 (6-year FFDM = 54.3%) or concurrent ADT (6-year FFDM = 64.0%) in a single group, the favourable prognosis of patients without any risk factor seems inappropriately merged with the two poorer-outcome risk factors, leading to an overall 6-year FFDM of 71% in that group. Based on the tables presented, separating patients with no risk factors from those with a high Gleason score or with concurrent ADT to create a separate risk group seems essential to optimise stratification given the striking difference in FFDM rates. We are also wondering why a similar weighted-risk grouping was not performed for PCSS, given that hazard ratios for the same variables were similar between FFDM and PCSS. Another important limitation, as acknowledged by the authors, lies in the lack of analysis based on the period of treatment over their 27-year review (1986–2013). Although they provide second BCR rates for every decade, they do not to perform complete subgroup analyses for separate periods. As discussed in the article, 2004 marked an important change in the technique of RT at their institutions (three-dimensional planning vs intensity-modulated RT) and could have been used as a threshold to stratify patients based on era, which would have removed this possible confounder. Other important factors, also mentioned by the authors (e.g. increase in CT sensitivity over the years, problems with older ADT records, new therapies for castration-resistant prostate cancer since 2010), may have significantly influenced outcomes in the sample. We understand that most of these subgroupings, even if theoretically necessary, would probably have made analyses underpowered. Furthermore, as discussed in the paper, it would have been relevant to study PSA doubling time, as it is a well-recognised surrogate for clinical progression and PCSS in primary BCR 5; maybe similar results could have been expected in second BCR. Overall, our team thinks that the study led by Tumati et al. 1 reached its primary objective of describing the natural history of second BCR following SRT after RP, whilst providing new, multi-centric data on this poorly explored topic. Moreover, the proposed risk stratification system for FFDM and PCSS after 6 years provides much needed prognostic insight for treatment-resistant disease. In addition, such data can help design future trials assessing new treatment options or novel diagnostic techniques and improve clinical management of second BCR. Dr Zorn reports personal fees from Boston Scientific, outside the submitted work. There are no other conflicts to disclose.

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)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,018

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,008
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
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)0,0020,001

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 Opus0,013
Tête enseignante GPT0,290
Écart entre enseignants0,277 · 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'étudeObservationnel
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

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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Même revueBritish Journal of UrologyMême sujetProstate Cancer Treatment and ResearchTravaux en français237 207