Metabolic Tumor Volume and Total Lesion Glycolysis in PET/CT Correlate With the Pathological Findings of Colorectal Cancer and Allow Its Accurate Staging
Bibliographic record
Abstract
INTRODUCTION: PET/CT plays an important role in cancer diagnosis. Recently, novel metabolic parameters in PET/CT such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been reported to be diagnostic and prognostic biomarkers of various cancers. We evaluated the diagnostic value of these metabolic parameters in colorectal cancer (CRC). METHODS: The study included 138 patients who underwent surgical resection of CRCs between August 2012 and March 2014. The MTVs and TLGs of tumors were measured using various SUV thresholds. The diagnostic abilities of the metabolic parameters were analyzed using ROC curves and classification and regression trees. RESULTS: The AUCs of the MTVs and TLGs for predicting T stage (0.881-0.892) were significantly higher than the AUC of the SUVmax (0.824). In the M stage, the AUCs of MTVs and TLGs (0.688-0.723) were significantly higher than that of the SUVmax (0.606). Recursive partitioning applying classification and regression trees demonstrated that the optimal cutoff values of the most important variables for discriminating T, N, and M stages are MTV2.5 = 9.35 and 63.33 mL, TLG50% = 328.1, and TLG50% = 94.81, respectively. CONCLUSION: Metabolic tumor volumes and TLGs in PET/CT are reliable diagnostic biomarkers. Using these parameters, more accurate preoperative diagnoses for CRC can be made.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".