EVALUATION OF CLINICAL VARIABLES, RADIOLOGICAL VISUAL ANALOG SCORING, AND RADIOMICS FEATURES ON CT ENTEROGRAPHY FOR CHARACTERIZING SEVERE INFLAMMATION AND FIBROSIS IN STRICTURING CROHN’S DISEASE
Bibliographic record
Abstract
Abstract PURPOSE 25% of patients with Crohn’s disease (CD) develop severe stricturing disease which is non-responsive to standard-of-care medication. Early and non-invasive determination of the extent of inflammation and fibrosis within the stricture via CT enterography (CTE) could facilitate the selection of targeted therapy or earlier surgical resection to improve patient outcomes; but currently there is no validated and reliable approach for this differentiation. We present initial results for machine-reader evaluation of severe inflammation and fibrosis in CD strictures via quantitative radiomic features and expert radiologist scoring on CTE. METHODS AND MATERIALS IRB approved, retrospective, single center study. 100 patients (n=66 for discovery; n=34 for hold-out validation) confirmed with stricturing CD on histopathology and CTE within 15 weeks of surgery. Histopathological Stenosis Therapy & Research (STAR) scoring of specimens (range 0-100, scores > 50 =severe) used as reference standard for inflammation and fibrosis each. An expert radiologist annotated the resected strictures on CTE and provided a global assessment of inflammation and chronic non-inflammatory findings (fibrosis) using a 0-100 visual analog score (VAS). Radiomics features to capture severe inflammation and fibrosis were separately extracted from the annotated strictures. Radiomics models and VAS scores evaluated against pathology-defined severe inflammation and fibrosis, via ROC analysis. RESULTS Two distinct sets of radiomic features capturing textural heterogeneity (patterns, wavelets, local entropy) within strictures were significantly associated (p<0.01) with severe inflammation and severe fibrosis; across both discovery (AUC=0.69, 0.69) and hold-out validation (AUCs =0.72,0.67). Radiological VAS had an AUC=0.64 for identifying severe inflammation and AUC =0.62 for identifying severe fibrosis (Figure 1). Clinical variables including sex, age, Montreal classification and stricture type were not significantly associated with severe inflammation or fibrosis, across discovery and validation groups (Table 1). CONCLUSIONS Radiomic analysis shows improved performance in identifying severe inflammation and severe fibrosis in CD strictures on CTE compared to radiological visual assessment scoring. Supplementing radiological visual assessment with quantitative radiomics could enable more accurate phenotyping of CD strictures potentially improving outcomes by personalizing treatment pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| 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 | 0.001 | 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".