Comparison of FDG PET metabolic tumour volume <i>versus</i> ADC histogram: prognostic value of tumour treatment response and survival in patients with locally advanced uterine cervical cancer
Bibliographic record
Abstract
Objective: To evaluate the prognostic utility of volume-based parameters of fluorine-18 fludeoxyglucose positron emission tomography (18F-FDG PET) and apparent diffusion coefficient (ADC) histogram analysis for tumour response to therapy and event-free survival (EFS) in patients with uterine cervical cancer receiving chemoradiotherapy. Methods: The study included 21 patients diagnosed with locally advanced uterine cervical cancer who underwent pre-treatment MRI and 18F-FDG PET and were treated with concurrent chemoradiotherapy. 18F-FDG parameters: maximum and mean standardized uptake value; metabolic tumour volume (MTV); total lesion glycolysis (TLG); ADC parameters: maximum, mean and minimum values; percentile ADC values (10–90%); skewness and kurtosis of ADC were measured and compared between the responder and non-responder groups using a Wilcoxon rank-sum test. The Cox regression analysis and Kaplan–Meier survival curves were performed for EFS analysis. Results: MTV and TLG of the primary tumour were significantly higher in the non-responder group than in the responder group (p = 0.04 and p = 0.01). Applying Cox regression multivariate analysis, MTV [hazard ratio (HR), 4.725; p = 0.036], TLG (HR, 4.725; p = 0.036) and 10-percentile ADC (HR, 5.207; p = 0.048) showed a statistically significant association with EFS. With the optimal cut-off value, the EFS rates above the cut-off value for MTV and TLG were significantly lower than that below the cut-off value (p = 0.002 and p = 0.002). Conclusion: Pre-treatment volume-based quantitative parameters of 18F-FDG PET may have better potential than ADC histogram for predicting treatment response and EFS in patients with locally advanced cervical cancer. Advances in knowledge: In this study, pre-treatment volume-based quantitative parameters of 18F-FDG PET had better potential than ADC histogram for predicting treatment response and survival in patients with locally advanced cervical cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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".