Positron Emission Tomography Computed Tomography: A Guide for the General Radiologist
Bibliographic record
Abstract
Cancer remains a leading cause of death in Canada and worldwide. Whilst advances in anatomical imaging to detect and monitor malignant disease have continued over the last few decades, limitations remain. Functional imaging, such as positron emission tomography (PET), has improved the sensitivity and specificity in detecting malignant disease. In combination with computed tomography (CT), PET is now commonly used in the oncology setting and is an integral part of many cancer patients' pathways. Although initially the CT component of the study was purely for attenuation of the PET imaging and to provide anatomical coregistration, many centers now combine the PET study with a diagnostic quality contrast enhanced CT to provide one stop staging, thus refining the patient's pathway. The commonest tracer used in everyday practice is FDG (F18-fluorodeoxyglucose). There are many more tracers in routine clinical practice and those with emerging roles, such as 11C-choline, useful in the imaging of prostate cancer; 11C-methionine, useful in imaging brain tumours; C11-acetate, used in imaging hepatocellular carcinomas; 18F-FLT, which can be used as a marker of cellular proliferation in various malignancies; and F18-DOPA and various 68Ga-somatostatin analogues, used in patients with neuroendocrine tumours. In this article we concentrate on FDG PETCT as this is the most commonly available and widely utilised tracer now used to routinely stage a number of cancers. PETCT alters the stage in approximately one-third of patients compared to anatomical imaging alone. Increasingly, PETCT is being used to assess early metabolic response to treatment. Metabolic response can be seen much earlier than a change in the size/volume of the disease which is measured by standard CT imaging. This can aid treatment decisions in both in terms of modifying therapy and in addition to providing important prognostic information. Furthermore, it is helpful in patients with distorted anatomy from surgery or radiotherapy when there is suspicion of recurrent or residual disease. FDG PETCT is not specific for malignancy and can also be used for diagnosing and monitoring a number of inflammatory and infectious conditions that can be difficult to diagnose on anatomical imaging, some of which carry significant morbidity. FDG PETCT is increasingly used in patients with pyrexia of unknown origin and in patients with metastatic malignancies of unidentified primary on conventional imaging. This article reviews the uses of PETCT including an overview of the more common incidental lesions and conditions. It also provides guidance of how to approach a PETCT as a nonradionuclide radiologist and how to interpret a study in the multidisciplinary team setting.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".