Current Imaging Techniques for Lymph Node Staging in Prostate Cancer: A Review
Bibliographic record
Abstract
Introduction: Lymph node metastases (LNM) represent a proven prognostic factor for biochemical recurrence (BCR)-free survival, metastatic free survival and overall survival in prostate cancer (PCa). Although pelvic node dissection remains the gold standard for the detection of LNM, novel imaging techniques are entering clinical practice, in the effort to improve LNM detection and spare unnecessary surgeries. Aim of the current review is to describe such imaging techniques and explore their advantages and limitations. Evidence Acquisition: The National Library of Medicine Database was searched for relevant articles published between January 2013 and August 2018. A wide search was performed including the combination of following words: “Prostate” AND “Cancer” AND “staging” AND “Lymph Node” AND “imaging” AND ("MRI" OR "PET"). The initial list of selected papers was enriched by individual suggestions of the authors of the present review. Evidence synthesis: DWI-MRI in detection of lymph node invasion has a sensitivity and specificity of 41% and 94% respectively. For SPIO MRI using ferumoxtran-10, the sensitivity for detection of LNM with short axis diameter of 5-10 mm is reported at 96.4%, compared to 28.5% with MRI alone. PSMA PET/CT is growing exponentially, both in the initial detection of LNM and for BCR evaluation. Fluciclovine PET could improve detection of subcentimetric pathologic lymph nodes. Sentinel lymph node techniques remain experimental and not validated in the field of PCa. Conclusions Molecular imaging, particularly PSMA ligand PET imaging, present interesting diagnostic accuracy in LN diagnosis even in subcentimetric LN. DWI-MRI yields good results in LN involvement evaluation and the use of contrast agent such SPIO may improve the detection rate. The SLN technique is limited to experimental protocols and for intermediate or high-risk PCa. Prospective trials are awaited to evaluate the true clinical impact of these imaging techniques on PCa oncologic outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".