Magnetic Resonance Imaging of Peritoneal Carcinomatosis: Evaluation of High b-Value Computed Diffusion-Weighted Imaging
Bibliographic record
Abstract
Over the last few years, diffusion-weighted imaging (DWI) has become increasingly relevant in the diagnostic assessment of peritoneal carcinomatosis. The aim of this study was to investigate the benefits of high-b DWI (c-DWI) compared to standard DWI in patients with peritoneal carcinomatosis. A cohort of 40 patients with peritoneal carcinomatosis were included in this retrospective study. DWI was performed with b-values of 50, 400, and 800 or 1000 s/mm² on a 1.5-T magnetic resonance imaging (MRI) scanner. C-DWI was calculated using a mono-exponential model with high b-values of 1000, 2000, 3000, 4000, and 5000 s/mm². All c-DWI images with high b-values were compared in terms of volume, detectability of peritoneal lesions, and image quality with the DWI sequence acquired with a b-value of 800 or 1000 s/mm² by two readers. In the group with a b-value of 800 s/mm², there was no statistically significant difference in terms of lesion volume. In the second group with a b-value of 1000 s/mm², peritoneal carcinomatosis lesions were statistically significantly larger than in the c-DWI with a- high b-value of 2000 s/mm² (median 7 cm³, range 1−26 cm³vs. median 6 cm³, range 1−83 cm³, p < 0.05). In both groups, there was a marked decrease in the detectability of peritoneal lesions starting at b = 2000 s/mm². In addition, image quality decreased noticeably from c-DWI at b = 3000 s/mm². In both groups, all images with high b-values at b = 4000 s/mm² and 5000 s/mm² were not diagnostically valuable due to poor image quality. The c-DWI technique offers good diagnostic performance without additional scanning time. High c-DWI b-values up to b = 1000 s/mm² provide comparable detectability of peritoneal carcinomatosis compared to standard DWI. Higher b-values over 1500 s/mm² result in lower image quality, which might lead to misdiagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".