Analysis by TeloView® Technology Predicts the Response of Hodgkin’s Lymphoma to first line ABVD Therapy
Bibliographic record
Abstract
Abstract Classic Hodgkin’s lymphoma (cHL) is a curable cancer with disease-free survival rate of over 10 years. Over 80% of diagnosed patients respond favorably to first line chemotherapy. However, 15-20% of patients experience refractory or early relapsed disease. To date, the identification of such patients is still not possible using traditional clinical risk factors. The three-dimensional (3D) telomere analysis has been shown to be a reliable structural biomarker to quantify genomic instability, inform on disease progression, and predict patients’ response to therapy in several cancers, particularly hematological disorders. The 3D telomere analysis previously also elucidated biological mechanisms related to cHL disease progression. Here we report results of a multicenter retrospective clinical study including 156 cHL patients. We used the cohort data as a training dataset and identified significant 3D telomere parameters suitable to predict individual patient outcome at point of diagnosis. Multivariate analysis allowed for developing a predictive model using four telomeric parameters as predictors, including the proportion of t-stumps (very short telomeres). The percentage of t-stumps was the most prominent predictor to identify refractory/relapsing cHL prior to the initiation of ABVD therapy. The model characteristics include AUC of 0.83 in ROC analysis, sensitivity, and specificity of 0.8 and 0.75 respectively.
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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 | 0.003 |
| 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) | 0.002 | 0.001 |
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".