Combination of FDG PET/CT radiomics and clinical parameters for outcome prediction in patients with non-Hodgkin’s lymphoma
Bibliographic record
Abstract
PURPOSE: The purposes was to build model incorporating PET + computed tomography (CT) radiomics features from baseline PET/CT + clinical parameters to predict outcomes in patients with non-Hodgkin lymphomas. METHODS: Cohort of 138 patients with complete clinical parameters and follow up times of 25.3 months recorded. Textural analysis of PET and manual correlating contouring in CT images analyzed using LIFE X software. Defined outcomes were overall survival (OS), disease free-survival, radiotherapy, and unfavorable response (defined as disease progression) assessed by end of therapy PET/CT or contrast CT. Univariable and multivariable analysis performed to assess association between PET, CT, and clinical. RESULTS: Male ( P = 0.030), abnormal lymphocytes ( P = 0.030), lower value of PET entropy ( P = 0.030), higher value of SHAPE sphericity ( P = 0.002) were significantly associated with worse OS. Advanced stage (III or IV, P = 0.013), abnormal lymphocytes ( P = 0.032), higher value of CT gray-level run length matrix (GLRLM) LRLGE mean ( P = 0.010), higher value of PET gray-level co-occurrence matrix energy angular second moment ( P < 0.001), and neighborhood gray-level different matrix (NGLDM) busyness mean ( P < 0.001) were significant predictors of shorter DFS. Abnormal lymphocyte ( P = 0.033), lower value of CT NGLDM coarseness ( P = 0.082), and higher value of PET GLRLM gray-level nonuniformity zone mean ( P = 0.040) were significant predictors of unfavorable response to chemotherapy. Area under the curve for the three models (clinical alone, clinical + PET parameters, and clinical + PET + CT parameters) were 0.626, 0.716, and 0.759, 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".