Combination of FDG PET/CT Radiomics and Clinical Parameters for Outcome Prediction in Patients with Hodgkin’s Lymphoma
Bibliographic record
Abstract
Abstract Purpose: The aim of the study is to evaluate the prognostic value of joint evaluation of PET- and CT radiomics combined with standard clinical parameters in patients with HL. Methods: Overall, 88 patients (42 female and 46 male) with a median age of 43.3 (range 21-85 years) were included. Textural analysis of the PET/CT images was performed using a freely available software (LIFE X). 65 radiomics features (RF) were evaluated. Univariate and Multivariate models were used to determine the value of clinical characteristics and FDG PET/CT radiomics in outcome prediction. A binary logistic regression model was used to determine potential predictors for radiotherapy and odds ratios (OR) with 95% confidence intervals (CI) were reported. Features relevant for survival outcome were assessed with Cox proportional hazards to calculate hazard ratios with 95% CI. Results: Overall, Albumin + ALP + CT radiomic features (Area under curve (AUC): 95.0 (86.9;100.0) – Brier: 3.9 (0.1;7.8) remained as significant independent predictors for outcome prediction. PET- SHAPE Sphericity (1.9 (1.05,3.42) p=0.033); CT grey level zone length matrix high gray-level zone emphasis (GLZLM SZHGE mean (2 (1.08,3.73) p=0.028)) and PARAMS XSpatial Resampling (2.1 (1.2,3.68) p=0.0091) as well as hemoglobin results (p=0.016) remained as independent features in the final model for binary outcome as predictors of need of radiotherapy. (AUC = 0.79) Conclusion: We evaluated the value of baseline clinical parameters as well as combined PET and CT radiomics in HL patients for survival and prediction of need of radiotherapy. We found that different combinations of all three factors/features were independently predictive of the here evaluated endpoints.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".