From dimensions through dynamics to outcomes – lymph nodes and skin assessments predict CTCL outcomes
Bibliographic record
Abstract
Radiologic nodal staging in CTCL traditionally uses a 1.5 cm longest diameter (LDi) cutoff; however, this lacks validation and may misclassify risk. We conducted a retrospective analysis of 6,095 CT scans from 262 CTCL patients in the MAVORIC trial using unidimensional, bidimensional, and volumetric LN measurements and mSWAT scores. Optimal cutoffs were determined via ROC analysis and landmarking adjusted for informative censoring. Additionally, kinetic modeling growth rates (g) were calculated for both LN and skin scores. We demonstrated that LDi > 1.5 cm did not predict OS (p = 0.8). However, baseline volumetric cutoffs (3,945 mm3; AUC = 0.67) stratified OS (median 43.6 months vs NA, log-rank p = 0.035); post‑landmark analysis (6,930 mm3) enhanced discrimination. High g (volumetric or mSWAT) independently predicted shorter OS/PFS/TTF (p < 0.05). A combined model (volume + g) had C‑index 0.63 versus 0.60 for volume alone. We conclude that volumetric and dynamic metrics outperform conventional measures in predicting CTCL outcomes. Incorporating these methods into staging and trial criteria is warranted.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".