An Individualized Prediction Model for Early-Stage Classic Hodgkin’s Lymphoma
Bibliographic record
Abstract
BACKGROUND: A predictive model for early-stage classic Hodgkin's lymphoma (cHL) does not exist. Leveraging patient-level data from large clinical trials and registries, we developed and validated a model that we term the Early-Stage cHL International Prognostication Index (E-HIPI) to predict 2-year progression-free survival (PFS). METHODS: We developed the model using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines in 3000 adults with newly diagnosed early-stage cHL from four international phase III clinical trials conducted from 1994 to 2011. External validation was performed in two cohorts, totaling 2360 treated patients from five international cHL registries (1996 to 2019). Two-year PFS was estimated using a Cox model with pretreatment variables selected using backward elimination. Internal validation corrected for overfitting. External validation assessed discrimination and calibration. The final model was also compared against European Organisation for Research and Treatment of Cancer (EORTC) favorable or unfavorable status. RESULTS: The median age in the development cohort was 31.2 years; 77.4% had stage II disease. The estimated 2-year PFS was 93.7%. Final variables retained in the model were sex and continuous values of maximum tumor diameter (MTD), and levels of hemoglobin and albumin. The optimism-corrected C statistic in the development cohort was 0.63 (95% confidence interval, 0.60 to 0.69). Two-year PFS was lower in the validation cohorts 1 (90.3%) and 2 (91.6%). In validation cohort 1, the C statistic was 0.63 and the calibration slope was near 1, but overall calibration indicated underprediction, which improved on updating the intercept. The performance was similar in validation cohort 2. In addition, higher-risk E-HIPI scores were associated with worse outcomes in both the EORTC unfavorable and favorable subgroups. When included altogether in one Cox model, the E-HIPI was associated with PFS, whereas EORTC favorable or unfavorable status was not. Online risk calculators were developed (https://rtools.mayo.edu/holistic_ehipi/). CONCLUSIONS: Utilizing objective, continuous, and readily available variables, we developed and validated a new prediction model for early-stage cHL. Male sex, lower hemoglobin or albumin levels, and higher MTDs were associated with worse PFS. (Funded by the National Cancer Institute; grant number, NCI R01 CA 262265-04.).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".