Wie viele MRT-Sequenzen der Prostata werden benötigt?
Bibliographic record
Abstract
Multiparametric magnetic resonance imaging (mpMRI) is an established imaging modality for prostate cancer. In this context, "multiparametric" refers to the combination of anatomical sequences (T1- and T2-weighted) with functional (diffusion-weighted) and contrast-enhanced sequences. Anatomical sequences offer high spatial resolution, while diffusion-weighted imaging (DWI) assesses the movement of water molecules within tissues, providing information on tissue composition. The contrast-enhanced sequence (Dynamic Contrast-Enhanced, DCE) evaluates tissue perfusion to identify potential tumour angiogenesis. This combination of sequences allows a comprehensive assessment of various aspects of prostate tissue. However, growing evidence suggests that not all sequences are always required. For early detection of prostate cancer, MRI without DCE (=biparametric MRI, bpMRI) should be the standard, because it exhibits similar detection rates for clinically significant prostate cancer. In special cases, such as after previous prostate treatments (e.g., after focal therapy), radiological challenges (e.g., hip replacement), or in cases of negative bpMRI findings with persistent suspicion of prostate cancer, adding DCE may be helpful. MRI screening without DCE is safer, less expensive, and reduces gadolinium emissions. The final results from the prospective, multicentre PRIME study (bpMRI vs. mpMRI before biopsy) are still pending and will further clarify the role of DCE in early 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".