Evaluation of MRI-TRUS Fusion Versus Cognitive Registration Accuracy for MRI-Targeted, TRUS-Guided Prostate Biopsy
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is to compare transrectal ultrasound (TRUS) biopsy accuracies of operators with different levels of prostate MRI experience using cognitive registration versus MRI-TRUS fusion to assess the preferred method of TRUS prostate biopsy for MRI-identified lesions. SUBJECTS AND METHODS; One hundred patients from a prospective prostate MRI-TRUS fusion biopsy study were reviewed to identify all patients with clinically significant prostate adenocarcinoma (PCA) detected on MRI-targeted biopsy. Twenty-five PCA tumors were incorporated into a validated TRUS prostate biopsy simulator. Three prostate biopsy experts, each with different levels of experience in prostate MRI and MRI-TRUS fusion biopsy, performed a total of 225 simulated targeted biopsies on the MRI lesions as well as regional biopsy targets. Simulated biopsies performed using cognitive registration with 2D TRUS and 3D TRUS were compared with biopsies performed under MRI-TRUS fusion. RESULTS: Two-dimensional and 3D TRUS sampled only 48% and 45% of clinically significant PCA MRI lesions, respectively, compared with 100% with MRI-TRUS fusion. Lesion sampling accuracy did not statistically significantly vary according to operator experience or tumor volume. MRI-TRUS fusion-naïve operators showed consistent errors in targeting of the apex, midgland, and anterior targets, suggesting that there is biased error in cognitive registration. The MRI-TRUS fusion expert correctly targeted the prostate apex; however, his midgland and anterior mistargeting was similar to that of the less-experienced operators. CONCLUSION: MRI-targeted TRUS-guided prostate biopsy using cognitive registration appears to be inferior to MRI-TRUS fusion, with fewer than 50% of clinically significant PCA lesions successfully sampled. No statistically significant difference in biopsy accuracy was seen according to operator experience with prostate MRI or MRI-TRUS fusion.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".