Thalidomide for the treatment of hemangiosarcoma in dogs
Bibliographic record
Abstract
Introduction: Hemangiosarcoma is a malignant neoplasm of vascular endothelium occurring most frequently in older large breed dogs. Noncutaneous hemangiosarcoma is highly metastatic (>80% at diagnosis). Surgery is the primary method of treatment; however, even with adjuvant chemotherapy the prognosis for long‐term survival is low. Hence, new therapies are needed. The sedative‐hypnotic drug thalidomide (alpha‐N‐phthalimidoglutarimide) was withdrawn from general distribution in the 1960's after recognition of its teratogenicity and association with phocomelia. Recently, discredited thalidomide is making a comeback for its immunomodulatory and antiangiogenesis properties to treat inflammatory, infectious, and neoplastic diseases in people. Thalidomide can inhibit the proliferation of blood vessels associated with tumour development, thereby stopping or slowing tumour growth (similar to the devastating effect of the in utero interference with the blood supply of the developing limbs of the fetus). Therefore, the purpose of this study was to retrospectively obtain the results of thalidomide therapy for canine hemangiosarcoma. A prospective study for the use of thalidomide therapy for the treatment of canine hemangiosarcoma was also planned. Materials and Methods: Fourteen dogs with histologically diagnosed hemangiosarcoma were retrospectively entered into the study. Dogs were treated at 100–400 mg per day. Unfortunately, a legal, consistent source of thalidomide for treatment of dogs could not be obtained; therefore, the prospective study was postponed. Results: The median survival was 61 days with a range of 0 days to 2 years. Conclusions: Only limited efficacy data are available so far to define the clinical utility of thalidomide in canine hemangiosarcoma. However, in this pilot study there were prolonged responses to thalidomide in some patients which prompts a phase 2 investigation of thalidomide in canine hemangiosarcoma. The optimal dose and schedule of administration in dogs remains to be determined but the absence of myelosuppressive and other important adverse effects suggests thalidomide could be used in combination with chemotherapy. We conclude that thalidomide (or its analogues) could open the possibility for novel treatment that targets tumours and their microenvironment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".