Nuclear Medicine Imaging of Infection in Cancer Patients (With Emphasis on FDG-PET)
Bibliographic record
Abstract
Infections are a common cause of death and an even more common cause of morbidity in cancer patients. Timely and adequate diagnosis of infection is very important. This article provides clinicians as well as nuclear medicine specialists with a concise summary of the most important and widely available nuclear medicine imaging techniques for infectious and inflammatory diseases in cancer patients with an emphasis on fluorodeoxyglucose positron emission tomography (FDG-PET). ⁶⁷Ga-citrate has many unfavorable characteristics, and the development of newer radiopharmaceuticals has resulted in the replacement of ⁶⁷Ga-citrate scintigraphy by scintigraphy with labeled leukocytes or FDG-PET for the majority of conditions. The sensitivity of labeled leukocyte scintigraphy in non-neutropenic cancer patients is comparable with that in patients without malignancy. The specificity, however, is lower because of the uptake of labeled leukocytes in many primary tumors and metastases, most probably as a result of their inflammatory component. In addition, labeled leukocyte scintigraphy cannot be used for febrile neutropenia because of the inability to harvest sufficient peripheral leukocytes for in vitro labeling. FDG-PET has several advantages over these conventional scintigraphic techniques. FDG-PET has shown its usefulness in diagnosing septic thrombophlebitis in cancer patients. It has also been shown that imaging of infectious processes using FDG-PET is possible in patients with severe neutropenia. Although larger prospective studies examining the value of FDG-PET in cancer patients suspected of infection, especially in those with febrile neutropenia, are needed, FDG-PET appears to be the most promising scintigraphic technique for the diagnosis of infection in this patient group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".