Candid choices: optimising patient selection in prostate cancer focal therapy
Notice bibliographique
Résumé
Focal therapy (FT) for prostate cancer (PCa) involves using both advanced imaging and ablative techniques to precisely target and treat specific areas of clinically significant PCa within the prostate. By focusing on the index lesion rather than the entire gland, it aims to reduce side-effects often associated with more aggressive treatments [1]. In contemporary urological practice, FT represents a groundbreaking advance that fundamentally alters the field of localised PCa treatment. This disruptive innovation has created a new market of technological interaction [2]. More importantly, FT implies a process of systematic risk assessment to identify, evaluate, and mitigate potential risks. Risk monitoring and review of variables start at the initial clinic and go through energy selection, treatment, and follow-up (Fig. 1). Precise selection of patients is crucial for accurate results. We were very pleased with the manuscript from Kaufmann et al. [3], which features a clear example of dedication and effort. This paper presents the outcomes of a 3-year study on focal high-intensity focused ultrasound (HIFU) for treating PCa. The fruit one harvests from the tree in this work is the major importance of selection for the eventual indication of FT. The authors deployed a solid approach of transperineal template saturation biopsies and MRI/TRUS fusion-targeted biopsies with targeted prostate cores taken from any lesion Prostate Imaging-Reporting and Data System (PI-RADS) ≥3. Although this diagnostic approach seems rather aggressive, it provides a solid base to indicate partial gland ablation. A total of 91 patients participated, primarily with Grade Group ≥2 disease and the authors strictly assessed them with follow-up biopsies, showing 44–65% of patients free of clinically significant cancer at 3 years. With Professor Eberli's group, we share a strong belief that at this point of FT development, follow-up biopsies remain essential as eventual detection of cancer recurrence using MRI and PSA might be still limited in most centres, as experienced by Kaufmann et al. [3]. Of utmost importance, in contrast to the previously published series [4, 5], this paper describes a striking 52% infield recurrence for patients with any Grade Group ≥2 within the first year, and this with the comprehensive selection process previously described. Technology deployed for treatment by the Swiss group features a pre-programmed ‘robotic’ single-tissue energy exposure that perhaps needs to be reassessed. To contemplate a ‘double-tap’ HIFU treatment or overlapping treatment zones seems desirable, as other technologies have shown adequate outcomes with this kind of approach [6]. Interestingly, in this series, although functional outcomes were adequate, 21% of patients experienced worsened erectile function, which is an uncommon feature in the FT literature. A more comprehensive evaluation of functional outcomes and quality of life is our responsibility in future experiences with FT to present the real impact of this technique [7]. The present study highlights the efficacy of accurate selection and rigorous follow-up in FT. As stated, FT remains a process of risk assessment and refined stratification; the latter requires dedicated time and attention to detail. Multidisciplinary discussion of potential FT cases is a solid pathway to improve outcomes. Today, the actual number of patients with formal indication for FT treatment remains low after the deployment of a strict and precise case-by-case evaluation. Unfortunately, FT has been embraced around the world by non-academic bound centres unable to provide the comprehensive and transparent assessment described by Kaufman et al. [3]. Consulting and Speaker Honoraria for the following: Abbvie Canada, Janssen Canada, Tolmar Canada, TerSera Canada, EDAP-TMS, ExactImaging, Angiodynamics.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,004 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».