<sup>18</sup>FDG‐PET/CT: 21st century approach to leukemic tumors in 124 cases
Bibliographic record
Abstract
Extramedullary tumors remain an obstacle to curing more acute leukemia patients. Their incidence is unknown because the presence of occult tumors that contribute to relapse is not routinely sought as in other cancers. No standard approach exists for treating tumors at most sites, apparent clinical response is typically followed by further tumors, and achievement of lengthy remission is uncommon. Body scanning with (18) FDG PET/CT now provides a means to identify the extent of occult tumors that enables directed tumor eradication and a way to evaluate tumor response. To evaluate its potential benefits, analysis was undertaken of 124 published cases scanned after apparent tumors were diagnosed. Clinical and radiologic exams underestimated extent of disease in over half of 100 cases. Among 70 cases that reported scans after various treatments, 70% achieved negative scans. Half relapsed subsequently but disease-free survivals up to 6 years were documented. These reported cases add to our knowledge of extramedullary leukemia in showing that further tumors are more likely than marrow relapse, clinical and radiologic evaluation of response is inadequate, intensive chemotherapy alone generally does not prevent progression and is associated with significant mortality, and tumor-directed plus systemic therapies appears the most effective approach, particularly to AML tumors. This analysis suggests this technology could increase our ability to eradicate all foci of leukemia, and identify tumors responsible for refractory, residual, and relapsed disease.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".