Artificial Intelligence for Tumor [18F]FDG PET Imaging: Advancements and Future Trends - Part II
Bibliographic record
Abstract
F]FDG PET/CT imaging continues to expand, offering new opportunities for more precise, consistent, and personalized oncologic evaluations. Building on the foundation established in Part I, this second part explores AI-driven innovations across a broader range of malignancies, including hematological, genitourinary, melanoma, and central nervous system tumors as well applications of AI in pediatric oncology. Radiomics and machine learning algorithms are being explored for their ability to enhance diagnostic accuracy, reduce interobserver variability, and inform complex clinical decision-making, such as identifying patients with refractory lymphoma, assessing pseudoprogression in melanoma, or predicting brain metastases in extracranial malignancies. Additionally, AI-assisted lesion segmentation, quantitative feature extraction, and heterogeneity analysis are contributing to improved prediction of treatment response and long-term survival outcomes. Despite encouraging results, variability in imaging protocols, segmentation methods, and validation strategies across studies continues to challenge reproducibility and remains a barrier to clinical translation. This review evaluates recent advancements of AI, its current clinical applications, and emphasizes the need for robust standardization and prospective validation to ensure the reproducibility and generalizability of AI tools in PET imaging and clinical practice.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".