Assessing the impact of a pre-processing pipeline on a tumor localization model in prostate MRI within a large multi-institutional dataset (NRG-GU005)
Bibliographic record
Abstract
Multi-parametric magnetic resonance imaging (mpMRI) is increasingly recognized as a valuable tool for characterizing prostate cancer, integrating T2-weighted (T2w), diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging. Despite its high sensitivity in localizing tumors, the specificity of mpMRI was shown to be hindered by benign conditions that mimic cancerous tissue features. The aim of this study is to investigate the effect of a pre-processing pipeline integrating state-of-the-art registration, bias field correction and normalization tools. We validated this pre-processing pipeline on a large multi-site dataset of 468 patients from 109 institutions. After performing all pre-processing, tumor localization was determined using a model-based tumor localization approach that takes both multi-parametric MRI and prior clinical knowledge features as input. Results show deformable image registration yielded a significant improvement in tumor localization accuracy, both for the diameter analysis as well as the area under the curve comparison for the subset of patients with ground truth tumor delineations.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".