3D histopathological grading of osteochondral tissue using contrast-enhanced micro-computed tomography
Bibliographic record
Abstract
OBJECTIVE: Histopathological grading of osteochondral (OC) tissue is widely used in osteoarthritis (OA) research, and it is relatively common in post-surgery in vitro diagnostics. However, relying on thin tissue section, this approach includes a number of limitations, such as: (1) destructiveness, (2) sample processing artefacts, (3) 2D section does not represent spatial 3D structure and composition of the tissue, and (4) the final outcome is subjective. To overcome these limitations, we recently developed a contrast-enhanced μCT (CEμCT) imaging technique to visualize the collagenous extracellular matrix (ECM) of articular cartilage (AC). In the present study, we demonstrate that histopathological scoring of OC tissue from CEμCT is feasible. Moreover, we establish a new, semi-quantitative OA μCT grading system for OC tissue. RESULTS: Pathological features were clearly visualized in AC and subchondral bone (SB) with μCT and verified with histology, as demonstrated with image atlases. Comparison of histopathological grades (OARSI or severity (0-3)) across the characterization approaches, CEμCT and histology, excellent (0.92, 95% CI = [0.84, 0.96], n = 30) or fair (0.50, 95% CI = [0.16, 0.74], n = 27) intra-class correlations (ICC), respectively. A new μCT grading system was successfully established which achieved an excellent cross-method (μCT vs histology) reader-to-reader intra-class correlation (0.78, 95% CI = [0.58, 0.89], n = 27). CONCLUSIONS: We demonstrated that histopathological information relevant to OA can reliably be obtained from CEμCT images. This new grading system could be used as a reference for 3D imaging and analysis techniques intended for volumetric evaluation of OA pathology in research and clinical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".