Enhancing radiomics robustness using bayesian penalized likelihood PET reconstruction: application to Phantom and non-small cell lung cancer patient studies
Bibliographic record
Abstract
PURPOSE: F-FDG PET imaging of lung cancer, which, with non-small cell lung carcinoma (NSCLC) as its most prevalent form, continues to be a leading cause of cancer-related mortality worldwide. The early detection and precise staging of NSCLC are crucial for effectively managing and treating the disease. METHOD: We studied a NEMA image quality (IQ) phantom and 15 patient PET lesions (14 NSCLC patients selected from 30 patients originally considered). The study assessed the stability of radiomics features against various imaging parameters, emphasizing the impact of the BPL reconstruction algorithm with varying β-values (50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, and 700) and three phantom lesion to background ratios (LBRs) of 2:1, 4:1, and 8:1. Manual segmentation was performed, and subsequently, 130 radiomic features were extracted from the reconstructed images. The stability of radiomics features was assessed by calculating the coefficient of variation (COV) for each feature across variations in reconstruction parameters. A COV of ≤ 5% indicated high stability. RESULTS: Our results indicate that morphological and intensity-based features exhibit excellent stability, with a COV of less than 5%. Texture-based features, despite their complexity, also demonstrated robustness. Specifically, 32.3%, 39.2%, 42.3%, and 37.6% of features exhibited high stability in phantom LBR 2:1, phantom LBR 4:1, phantom LBR 8:1, and patient studies, respectively. Overall, 13 morphological, 8 intensity, 6 intensity-histogram, and 5 texture-based features were found to be highly stable against different LBRs and reconstruction parameters. CONCLUSIONS: The BPL reconstruction algorithm may enhance the robustness of PET radiomics features, supporting their use in clinical settings for non-invasive diagnosis and staging. The adoption of BPL towards improved PET radiomics robustness has the potential to transform NSCLC evaluation and management, but still needs standardization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".