Post-Treatment Imaging Appearances Following Skull Base Therapy
Bibliographic record
Abstract
Background and Purpose: Skull base tumors are notoriously difficult to treat due to the inherent high risk of complications from a large number of neurovascular structures within an anatomically dense area. In the management of skull base lesions, craniofacial and endonasal approaches have become viable options either in conjunction or in isolation. However, accurate postoperative imaging remains an issue due to changes in anatomy from surgical defects, reconstructions, or any grafts. Hence a solid fundamental understanding of anticipated postoperative imaging features and expected deviations become crucial for subsequent imaging surveillance. Materials and Methods: We present a review of the imaging features involved in diagnosis and treatment surveillance for skull base tumors. Results: Each imaging modality has a unique range of practical applications. CT is often utilized for good soft tissue resolution, excellent assessment of bone, detection of cervical adenopathy, and rapid image acquisition for patients who are medically unstable or unable to maintain a still supine position. Similarly, magnetic resonance imaging (MRI) provides greater soft tissue resolution and is the modality of choice when investigating dural invasion or perineural spread. Diffusion weighted imaging (DWI) MRI is an adjunct to CT and/or MRI in the early post-treatment period to elucidate tumor recurrence from normal expected changes. FDG PET/CT has limited utility in the immediate 12 week post-treatment phase due to the higher likelihood of false positive and negatives from inflammation and vascular compromise respectively. The ability to detect asymptomatic recurrence is illustrated by frontal lobe edema and differentiation of recurrent or residual tumors from retained secretions for MRI of craniofacial resections, defect obliteration and gliosis in the adjacent cerebellar brain parenchymal from MRI of transpetrous approaches, and hypointense facial nerve visualization on T2-weighted MRI. On CT imaging, the tumor presents with similar attenuation to skeletal muscle and differentiation from granulation tissue is difficult. Thus any mass originating in the primary tumor bed, particularly those with progressive enlargement from serial imaging, should raise suspicion of recurrence. Following skull base reconstruction, the majority of postoperative complications occur acutely in the immediate period, including seroma/fluid retention, fistula formation, infection/abscess and flap necrosis. For fluid collections CSF leaks and chylous fistulas are the primary concern. On CT and MR imaging, particularly in the inferior lower left neck, a peripherally enhancing fluid collection may represent a chylous leak, a hematoma, an abscess or a seroma. MR is indicated in situations where fluid or soft tissue is subjacent to a bony defect it cannot be reliably distinguished or identified on CT, thus T2 weighted imaging complimented by fast imaging employing steady-state acquisition (FIESTA) sequence allows differentiation of herniation contents. Conclusion: Knowledge of the expected appearance following open and endoscopic surgical reconstruction, as well as radiation therapy, is necessary to successfully delineate complications and recurrences. An understanding of the latencies involving tumor recurrence and early versus late complications can be aligned with the clinical context to formulate a logical approach to equivocal findings.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".