Dynamic contrast enhanced MRI as a predictor of vascular-targeted photodynamic focal ablation therapy outcome in prostate cancer post failed external beam radiation therapy
Bibliographic record
Abstract
INTRODUCTION: Photodynamic therapy (PDT) can be employed as a focal therapy for prostate cancer. Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can potentially help identify tumour recurrence after failed external-beam radiotherapy (EBRT). The purpose of this study was to determine the ability of DCE-MRI to predict early response to PDT salvage treatment. METHODS: Patients with post-EBRT prostate cancer recurrence were prospectively enrolled into a Phase I/II trial of PDT using WST09. A 15-patient subgroup of this cohort undergoing 1.5T DCE-MRI at baseline and 1-week post-PDT was retrospectively analyzed. The reference standard was prostate biopsy obtained 6 months post-PDT. Analysis was performed on a patient-by-patient basis, by prostate gland halves, and by prostate sextants. RESULTS: Biopsy 6 months post-PDT identified cancer in 10/15 patients (66.7%), and in 24/90 sextants (26.7%). Residual cancer was identified in 22/37 sextants (59.5%) identified as being involved at baseline. DCE-MRI at 1 week correctly predicted recurrent disease with a sensitivity of 100% (10/10), specificity of 60% (3/5), positive predictive value of 83.3% (10/12), negative predictive value of 100% (3/3), and an overall accuracy of 86.7%, (13/15). When analysis was performed on prostate halves, the sensitivity and negative predictive value remained at 100%, with an improvement in specificity to 88.2% (15/17). The overall accuracy of DCE-MRI was similar regardless of analysis method: 86.7% on a patient-by-patient basis, 86.7% by prostate half and 83.3% by sextant. Changes in prostate-specific antigen (PSA) did not correlate to response. CONCLUSION: DCE-MRI shows promise as a tool to predict successful outcome when performed 1 week post-PDT and could potentially be used to inform the need for re-treatment at an early time-point.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".