Correlating Metabolic Activity on <sup>18</sup>F-FDG PET/CT With Histopathologic Characteristics of Osseous and Soft-Tissue Sarcomas: A Retrospective Review of 136 Patients
Bibliographic record
Abstract
OBJECTIVE: The objective of our study was to determine whether there is a statistically significant correlation between metabolic activity of osseous and soft-tissue sarcomas as measured by the maximum standardized uptake value (SUV(max)) on (18)F-FDG PET/CT and histopathologic characteristics such as mitotic counts, the presence of necrosis, and the presence of a myxoid component. MATERIALS AND METHODS: We retrospectively evaluated 238 consecutive patients with known soft-tissue or osseous sarcoma who underwent (18)F-FDG PET/CT for initial staging or assessment for recurrence of disease. The SUV(max) of each primary or of the most intense metastatic lesion was measured and was compared with the histologic data provided in the final pathology reports. RESULTS: Histopathologic data were available for 136 sarcomas. The median SUV(max) values of sarcomas with mitotic counts of less than 2.00 (per 10 high-power fields [HPF]), 2.00-6.99, 7.00-16.24, and 16.25 or greater were 5.0, 6.6, 10.3, and 13.0, respectively (p = 0.0003). The median SUV(max) for the sarcomas with necrosis (90 patients) was 8.6 and for those without necrosis (43 patients), 6.0 (p = 0.026). The median SUV(max) for the sarcomas without a myxoid component (118 patients) was 7.7 and with a myxoid component (16 patients) was 6.2 (p = 0.28). CONCLUSION: There was a statistically significant correlation between the mitotic count and the SUV(max) as well as between the presence of tumor necrosis and the SUV(max). Although a correlation between the presence of a myxoid component and SUV(max) was shown, it was not found to be statistically significant. These findings improve on the current information in the literature regarding the use of PET/CT for guidance in sarcoma biopsy. Correlating the SUV(max) with histologic markers that also feature prominently in major sarcoma grading systems may help improve the accuracy of grading and of prognostication by allowing the SUV(max) to potentially serve as a surrogate marker in these grading systems, particularly in cases in which there is interobserver disagreement in the pathologic diagnosis or in cases in which the sarcoma cannot be properly classified on the basis of histopathologic evaluation alone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| 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 | 0.001 |
| 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".