Length of prostate biopsy cores: does it impact cancer detection?
Bibliographic record
Abstract
INTRODUCTION: The detection of prostate carcinoma relies on adequate sampling. We aimed to evaluate whether core length is a significant biopsy parameter in the detection of cancer, especially in the low risk cancer category group. MATERIALS AND METHODS: We retrospectively analyzed pathology reports of 197 patients (2196 biopsy cores) undergoing initial transrectal ultrasound guided biopsy. A multivariate analysis of age, total prostate-specific antigen (PSA) concentration, prostate gland volume, total number of cores and length of biopsy cores was performed. Secondary analyses included stratification by Gleason score. Single core analysis was done to calculate a workable cut off value for core length with optimal sensitivity and specificity in carcinoma detection. RESULTS: Mean age, PSA, prostate volume, and total number of cores were 66.9 years, 12.6 ng/mL, 47.2 cc and 11.1 cores, respectively. Whereas detection of cancer was significantly associated with advanced age (p < 0.01) and smaller prostate volumes (p < 0.001), PSA levels (p = 0.40) and number of cores (p = 0.20) were not significant predictive factors. Assessment of biopsy core lengths showed that cores harboring cancer (n = 307, average length 14.1 mm) were significantly longer than benign cores (n = 1889, average length = 13.2 mm) (p < 0.001). Core length analysis yielded 13 mm cores have an optimal sensitivity (42.8%) and specificity (76.5%) for detection of carcinoma (odds ratio: 2.43). Secondary analyses of Gleason score did not show any difference with respect to core length. CONCLUSION: This study suggests that core length is a biopsy parameter that affects detection of cancer and is an essential parameter for core biopsy quality.
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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".