Clinicogenetic risk models predict early progression of follicular lymphoma after first-line immunochemotherapy
Bibliographic record
Abstract
Follicular lymphoma (FL) is a clinically and molecularly heterogeneous disease. Posttreatment surrogate end points, such as progression of disease within 24 months (POD24) are promising predictors for overall survival (OS) but are of limited clinical value, primarily because they cannot guide up-front treatment decisions. We used the clinical and molecular data from 2 independent cohorts of symptomatic patients in need of first-line immunochemotherapy (151 patients from a German Low-Grade Lymphoma Study Group [GLSG] trial and 107 patients from a population-based registry of the British Columbia Cancer Agency [BCCA]) to validate the predictive utility of POD24, and to evaluate the ability of pretreatment risk models to predict early treatment failure. POD24 occurred in 17% and 23% of evaluable GLSG and BCCA patients, with 5-year OS rates of 41% (vs 91% for those without POD24, P < .0001) and 26% (vs 86%, P < .0001), respectively. The m7-FL International Prognostic Index (m7-FLIPI), a prospective clinicogenetic risk model for failure-free survival, had the highest accuracy to predict POD24 (76% and 77%, respectively) with an odds ratio of 5.82 in GLSG (P = .00031) and 4.76 in BCCA patients (P = .0052). A clinicogenetic risk model specifically designed to predict POD24, the POD24-PI, had the highest sensitivity to predict POD24, but at the expense of a lower specificity. In conclusion, the m7-FLIPI prospectively identifies the smallest subgroup of patients (28% and 22%, respectively) at highest risk of early failure of first-line immunochemotherapy and death, including patients not fulfilling the POD24 criteria, and should be evaluated in prospective trials of precision medicine approaches in FL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".