Does Computed Tomography or Positron Emission Tomography/Computed Tomography Contribute to Detection of Small Focal Cancers in the Prostate?
Bibliographic record
Abstract
Prostate cancer is considered to be a multifocal tumor in the majority of patients. Based on histologic data after prostatectomy, there is a growing insight that a considerable number of men who receive a diagnosis in the contemporary setting of prostate-specific antigen screening have unilateral or unifocal disease. With this, the current concept of whole-gland therapy has come into discussion. The need for improvement of intraprostatic tumor characterization is clear. Molecular imaging is one of the areas of research on this aspect. The clinical indications for positron emission tomography (PET)/CT have increased rapidly in the field of oncology and are largely based on fluorodeoxyglucose (FDG) PET. Both conventional CT and FDG PET, however, cannot detect prostate cancer foci <5 mm within the prostate. Dynamic contrast-enhanced CT involves imaging a region of interest rapidly (usually <10 seconds between images) during a bolus intravenous injection of a contrast agent. Through analysis of the contrast enhancement time curves, it is possible to distinguish tissues with different microvascular properties such as cancer. The technologic aspects of both imaging techniques and the clinical results of 11C-choline PET/CT for intraprostatic tumor characterization are discussed. Based on preliminary studies, dynamic contrast-enhanced (DCE)-CT may be a useful tool for localization of prostate tumors and, perhaps more importantly, quantification of therapeutic response in prostate cancer. Validation work is necessary, however, to define its accuracy and role in therapeutic paradigms such as focal therapies, particularly given the current accuracy of MRI. In the future, combining DCE-CT with CT or (11)C-choline PET/CT may be an alternative to MRI, offering a combination of quantitative parameters that may correlate to tumor prognosis as well as cancer localization for focal therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".