MR Imaging Correlates of Intratumoral Tissue Types within Colorectal Liver Metastases: A High-Spatial-Resolution Fresh ex Vivo Radiologic-Pathologic Correlation Study
Bibliographic record
Abstract
PURPOSE: To analyze the direct relationship between complex internal magnetic resonance (MR) signal intensity (SI) patterns observed in colorectal liver metastases and their microscopic tissue characteristics. MATERIALS AND METHODS: The institutional ethics board approved this study. In seven consecutive patients undergoing hepatic resection for liver metastases (primary colorectal in six, breast mistaken for colorectal in one), the resected fresh ex vivo liver specimen was examined with T1-weighted (repetition time msec/echo time msec, 9/4.4-4.8) and T2-weighted (2500/90) MR imaging by using a voxel size of 0.47 x 0.7 x 2 mm. The liver was sectioned in a concordant plane, and individual histologic slides were scanned and reconstructed to form a whole-mount pathologic image of the metastases. A pathologist identified the regions of interest for intraacinar necrosis (IAN), loose or dense fibrosis, and moderately and poorly differentiated cells within the metastases, and these regions were matched to the corresponding MR image. The morphologic and SI patterns were noted. The normalized ratio between the SI of these regions and that of the background liver was determined on T1- and T2-weighted images. Pairwise differences between tissue types were calculated by using linear mixed model, with the P values adjusted for multiple comparisons by using the method of Sidak. RESULTS: A total of 98 zones were defined after pathologic analysis. On T2-weighted images, IAN was significantly lower in SI (P < .05) than the other tissues types. On T1-weighted images, IAN was significantly higher in SI than the other tissues types (P < .001). The type of necrosis encountered in these specimens was exclusively IAN. Qualitatively IAN had a specific pattern of SI (hypointense on T2-weighted and hyperintense on T1-weighted images). Other tissues types, including fibrosis, showed a pattern of hyperintensity on T2-weighted and hypointensity on T1-weighted images. CONCLUSION: IAN seen in colorectal metastases exhibits high T1-weighted SI and mixed T2-weighted SI. This SI pattern is unusual for common benign liver lesions and may be helpful in the MR imaging diagnosis of colorectal liver metastases. (c) RSNA, 2010.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".