Multidisciplinary Treatment of Liver Metastases from Intracranial SFTs/HPCs: A Report of Three Consecutive Cases
Bibliographic record
Abstract
In the 2016 WHO classification of tumors of the central nervous system, hemangiopericytomas (HPCs) and solitary fibrous tumors (SFTs) were integrated into a new entity (SFT/HPC). Metastases to bone, liver, lung, and abdominal cavity are of concern. Only 37 cases of patients with liver metastases due to intracranial SFTs/HPCs have been reported. Herein, we present our experience in the management of patients with liver metastases from intracranial SFTs/HCPs. All consecutive patients who were treated for liver metastases from intracranial SFTs/HPCs from January 2014 to December 2020 were enrolled. Overall, three patients were treated for liver metastasis from SFTs/HPCs with curative intent. Two patients with bilobar metastases at presentation required surgical resection, transarterial embolization, stereotactic radiofrequency ablation (SRFA) and systemic therapy. One patient with a singular right liver lobe metastasis was treated with SRFA alone. This patient shows no evidence of liver metastases 39 months following diagnosis. Of the two patients with bilobar disease, one died 89 months following diagnosis, while one is still alive 73 months following diagnosis. Long-term survival can be achieved using a multimodal treatment concept, including surgery, loco-regional and systemic therapies. Referral to a specialized tertiary cancer center and comprehensive long-term follow-up examinations are essential.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".