The Impact of MRI-TRUS Cognitively Targeted Biopsy on the Incidence of Pathologic Upgrading After Radical Prostatectomy
Bibliographic record
Abstract
Background: The aim of the study was to evaluate the utility of multiparametric magnetic resonance imaging (mp-MRI)-transrectal ultrasound (TRUS) cognitively targeted biopsy in identifying the most significant cancerous lesion in the prostate to decrease the incidence of pathologic upgrading after radical prostatectomy. Methods: We conducted a retrospective review of all radical prostatectomies at the American University of Beirut Medical Center between January 2016 and 2017. Pathology reports for both, TRUS biopsy and surgically resected specimens were analyzed and compared using SPSS. Results: Among 66 patients who underwent radical prostatectomy, 44 patients underwent a standard random 12-core biopsy of the prostate, while 22 patients underwent 4 - 5 cognitively targeted biopsies. Biopsy Gleason scores were compared to surgically resected specimens. Of mp-MRI targeted biopsies, 86% were identical to the surgical specimen, while 14% were upgraded. Of the random biopsy, 55% patients upgraded after surgery, while 38% were concordant with the random biopsy result. Moreover, 13/24 patients who upgraded after random biopsy, did so from Gleason 6 (3+3) to Gleason 7 (3+4). The difference in pathological upgrading among both groups is statistically significant, and confirms the importance of MRI-TRUS cognitively targeted biopsy in identifying the highest risk lesion. This may have significant implications on the choice of treatment prior to embarking on surgical resection of prostate cancer. Conclusion: MRI-TRUS targeted biopsy is more accurate than random biopsy in identifying the most significant cancerous lesion, resulting in a decreased incidence of pathologic upgrading after prostatectomy. This may have significant implications on the choice of treatment especially in low risk prostate cancer. Larger scale multicenter studies are required. World J Nephrol Urol. 2018;7(1):12-16 doi: https://doi.org/10.14740/wjnu285w
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".