Candid choices: optimising patient selection in prostate cancer focal therapy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".