Role of Systematic Biopsy in the Era of Targeted Biopsy: A Review
Bibliographic record
Abstract
Prostate cancer (PCa) is a major public health issue, as the second most common cancer and the fifth leading cause of cancer-related deaths among men. Many PCa cases are indolent and pose minimal risk, making active surveillance a suitable management approach. However, clinically significant prostate carcinoma (csPCa) can lead to serious health issues, including progression, metastasis, and death. Differentiating between insignificant prostate cancer (inPCa) and csPCa is crucial for determining appropriate treatment. Diagnosis of PCa primarily involves trans-perineal and transrectal systematic biopsies. Systematic transrectal prostate biopsy, which typically collects 10-12 tissue samples, is a standard method, but it can miss csPCa and is associated with some complications. Recent advancements, such as magnetic resonance imaging (MRI)-targeted biopsies, have been suggested to improve risk stratification and reduce overtreatment of inPCa and undertreatment of csPCa, thereby enhancing patient quality of life and treatment outcomes. Guided biopsies are increasingly recommended for their ability to better detect high-risk cancers while reducing identification of low-risk cases. MRI-targeted biopsies, especially when used as an initial biopsy in biopsy-naïve patients and those under active surveillance, have become more common. Utilization of MRI-TB alone can decrease septic complications; however, the combining of targeted biopsies with perilesional sampling is recommended for optimal detection of csPCa. Future advancements in imaging and biopsy techniques, including AI-augmented lesion detection and robotic-assisted sampling, promise to further improve the accuracy and effectiveness of PCa detection.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".