Detection and Characterization of Tumor Changes in <sup>18</sup>F-FDG PET Patient Monitoring Using Parametric Imaging
Bibliographic record
Abstract
UNLABELLED: In PET-based patient monitoring, metabolic tumor changes occurring between PET scans are most often assessed visually or by measuring only a few parameters (tumor volume or uptake), neglecting most of the image content. We propose and evaluate a parametric imaging (PI) method to assess tumor changes at the voxel level. METHODS: Seventy-eight pairs of tumor images obtained from baseline and follow-up (18)F-FDG PET/CT for 28 patients with metastatic colorectal cancer were considered. For each pair, after CT-based registration of the PET volumes, the 2 PET datasets were subtracted. A biparametric graph of subtracted voxel values versus voxel values in the first PET scan was obtained. A model-based analysis of this graph was used to identify the tumor voxels in which significant changes occurred between the 2 scans and yielded indices characterizing these changes. The Response Evaluation Criteria in Solid Tumors (RECIST) based on the CT images obtained 5-8 wk after the second PET/CT scan were used to classify tumor masses as responding or progressive. On the basis of this classification, we compared the sensitivity and specificity of PI and an approach based on recommendations from the European Organization for Research and Treatment of Cancer (EORTC). RESULTS: For tumor-based classification, the EORTC-based approach had a sensitivity and specificity of 85% and 52%, respectively, for detecting responding lesions, whereas PI had a sensitivity and specificity of 100% and 53%, respectively. None of responding tumors using RECIST was classified as progressive with the PI or EORTC-based criteria. Among the 14 progressive lesions according to RECIST, 12 were identified as progressive with PI whereas EORTC-based criteria classified only 1 as progressive and 13 as stable tumors. Considering the patient-based classification, none of the responders according to RECIST was classified as having progressive disease with the PI and EORTC-based criteria. PI has the advantage of showing a parametric image of the patient response to therapy, indicating potential heterogeneity in tumor response. CONCLUSION: The PI method has been successfully applied to characterize early metabolic tumor changes in 78 lesions from (18)F-FDG PET/CT scans of patients with metastatic colorectal cancer during chemotherapy. The PI findings correlated well with the standard RECIST-based response assessment.
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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 | 0.001 | 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 | 0.000 |
| 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".