Is it necessary for all patients with suspicious lesions undergo systematic biopsy in the era of MRI-TRUS fusion targeted biopsy?
Bibliographic record
Abstract
ABSTRACT Purpose Targeted biopsy (TB) combined with systematic biopsy (SB) is an optimized mode of prostate biopsy but can often lead to oversampling and overdiagnosis accompanied by potential biopsy-related complications and patient discomfort. Here, we attempted to reasonably stratify the patient population based on multi-parameter indicators with the aim of avoiding unnecessary SB. Methods In total, 340 biopsy-naïve men with suspected lesions, prostate-specific antigen (PSA) < 20 ng/mL and prostate imaging-reporting and data system (PI-RADS) ≥ 3 enrolled for study underwent both TB and SB. The primary outcome was to determine independent predictors for a valid diagnosis, assuming that only TB was performed and SB omitted (defined as mono-TB), taking TB + SB as the reference standard. The secondary outcomes were exploration of the predictive factors of mono-TB and TB + SB in detection of prostate cancer (PCa) and clinically significant PCa (csPCa). Results The mean PSA density (PSAD) of patient group was 0.27 ng/mL/mL. Multiparametric MRI PI-RADS scores were 3-5 in 146 (42.94%), 105 (30.88%), and 89 (26.18%) cases, respectively. PCa and csPCa were detected in 178/340 (52.35%) and 162/340 (47.65%) patients, respectively. Overall, 116/178 (65.17%) patients diagnosed with PCa displayed pathological consistencies between mono-TB and TB + SB modes. PSAD and PI-RADS were independent predictors of valid diagnosis using mono-TB. Conclusions PSAD combined with PI-RADS showed utility in guiding optimization of the prostate biopsy mode. Higher PSAD and PI-RADS values were associated with greater confidence in implementing mono-TB and safely omitting SB, thus effectively balancing the benefits and risks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".