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Record W4391051747 · doi:10.1111/bju.16270

Candid choices: optimising patient selection in prostate cancer focal therapy

2024· letter· en· W4391051747 on OpenAlexaffabout
Rafael Sanchez‐Salas

Bibliographic record

VenueBritish Journal of Urology · 2024
Typeletter
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsProstate cancerMedicineProstateHigh-intensity focused ultrasoundMedical physicsRadiologyCancerUltrasoundInternal medicine

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.065
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.277
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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