Perineural Invasion and Spread in Common Abdominopelvic Diseases: Imaging Diagnosis and Clinical Significance
Bibliographic record
Abstract
Malignancies and other diseases may spread by multiple pathways, including direct extension, hematogenous spread, or via lymphatic vessels. A less-well-understood route is the peripheral nervous system, which is known as perineural spread (PNS). In addition to accounting for pain and other neurologic symptoms, PNS affects both disease prognosis and management. Although PNS is commonly discussed in relation to head and neck tumors, there is emerging data regarding PNS in abdominopelvic malignancies and other conditions such as endometriosis. Due to improved contrast and spatial resolution, perineural invasion, a finding heretofore diagnosed only at pathologic examination, can be detected at CT, MRI, and PET/CT. PNS most commonly manifests as abnormal soft-tissue attenuation extending along neural structures, and diagnosis of it is aided by optimizing imaging parameters, understanding pertinent anatomy, and becoming familiar with the typical neural pathways of spread that largely depend on the disease type and location. In the abdomen, the celiac plexus is a central structure that innervates the major abdominal organs and is the principal route of PNS in patients with pancreatic and biliary carcinomas. In the pelvis, the lumbosacral plexus and inferior hypogastric plexus are the central structures and principal routes of PNS in patients with pelvic malignancies. Although the imaging findings of PNS may be subtle, a radiologic diagnosis can have a substantial effect on patient care. Knowledge of anatomy and known routes of PNS and optimizing imaging parameters is of utmost importance in providing key information for prognosis and treatment planning. © RSNA, 2023 Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available through the Online Learning Center.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".