Precision Medicine in the Treatment of Malignancies Involving the Ventral Skull Base: Present and Future
Bibliographic record
Abstract
Abstract Cancers involving the ventral skull base are rare and exceedingly heterogeneous. The variety of malignant tumors that arise in the nasal cavity, paranasal sinuses, nasopharynx, and adjacent mesenchymal tissues translates into a proportionally vast spectrum of prognoses, with some histologies such as olfactory neuroblastoma being associated with rare disease-specific death to other histologies such as mucosal melanoma for which survival beyond 5 years is considered a fortunate exception. Parallel to prognosis, treatment of sinonasal cancers is complex, controversial, and deeply dependent upon the putative pretreatment diagnosis. Given their heterogeneity, cancers of the ventral skull base are particularly prone to multidisciplinary management, which is indispensable. The therapeutic options available to date for these cancers include surgery, which currently remains the mainstay of treatment in most cases, along with radiotherapy and chemotherapy. Biotherapy and immunotherapy are only anecdotally and compassionately used. For each histology, a careful selection of modalities and their timing is paramount to ensure the best chance of cure. In keeping with the principles of precision medicine, several nuances displayed by malignancies of the ventral skull base are being considered as treatment-driving characteristics. This current trend arose from the observation that a remarkable variability of behavior can be observed even within a single histology. Although evidence is lacking in this field and several potential customizations of treatment are still at a theoretical level, understanding of these cancers is rapidly evolving and practical applications of this increasing knowledge is the much-needed step forward in the management of such rare cancers. This chapter highlights the tumor characteristics that may serve as treatment-driving factors in the most relevant cancers invading the ventral skull base.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".