The search for CT imaging subtypes of colorectal liver metastases and the impacts of slice thickness
Bibliographic record
Abstract
This paper explores the potential for computed tomography (CT) imaging subtypes of colorectal liver metastases (CRLMs) through unsupervised analyses of classical radiomic, topological and convolutional neural network (CNN) imaging features while also investigating the influence slice thickness has on the forming of these subtype groupings. A multi-center cohort of 1,199 patients with preoperative, portal-venous phase CT imaging of resectable CRLMs was used for this analysis. PCA and t-SNE were used to visualize potential slice thickness associations with classical radiomic, topological, and CNN features. Clusters in PCA were defined using K-means clustering for each feature type before cluster association with overall survival and hepatic-disease free survival was quantified. We found that classical radiomic first order and second order texture features clustered by slice thickness using both PCA and t-SNE approaches. More distinct groupings were shown in these feature groups when they were extracted from the liver parenchyma. Both topological and CNN imaging features formed no visual patterns associated with slice thickness. Significant differences in hepatic disease-free survival were seen between clusters for liver parenchymal classical radiomic first order and texture features as well as topological persistent statistics features. In overall survival, clusters from classical radiomic shape and topological persistent landscape were significant. When grouping by slice thickness, significant differences in overall survival were seen. The significance seen from the classical radiomic and topological features suggests the existence of imaging subtypes of CRLMs. Our work demonstrates the potential for topological approaches to be used to develop biomarkers robust to protocol influences and the importance of accounting for slice thickness when using classical radiomic features.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".