RADIOLOGIC RESPONSES IN EPITHELIOID HEMANGIOENDOTHELIOMA: BEYOND RECIST?
Bibliographic record
Abstract
Objective: mTOR inhibitors (mTORi) represent the most effective therapy for advanced PEComa.However, options after progression are limited.We previously reported that the addition of aromatase inhibitors (AI) to mTORi may overcome resistance in female patients.Here we provide a long term updated results of all consecutive patients treated at our institutions.Methods: All cases of advanced PEComa treated with mTORi at Istituto Nazionale Tumori, Milan, since April 2018 were collected.Female patients progressing to mTORi received the addition of AI with or without LhRH analogues (based on their menopausal status).Survival analyses were performed using the Kaplan-Meier method.Results: Fifty-six patients with advanced PEComa treated with mTORi were identified.Thirty-nine (70%) treated in first line.All but one were evaluable for response.Seventeen out of 55 (31%) had an objective response: 16 partial response (29% PR) and 1 complete response (2% CR); twenty-two had a stable disease (40% SD) and 16 a progression disease (29% PD).Median PFS was 6.2 months (95% CI 5.2 -10.3).Upon progression, 23 female patients received a combination of mTORi and AI; all were evaluable for response: 13 patients (57%) achieved new disease control with 7 patients (30%) having PR and 6 patients (27%) a SD.Ten patients (43%) had PD.Median PFS for the combination of mTORi and AI was 5.7 (95% CI: 2.7-NR).In responding patients mPFS was 26 months (95% CI: 7-NR) with 3 patients free from progression after 4 years.NGS analysis is ongoing in collaboration with the Royal Marsden Hospital,London. Conclusion:In this updated series from a referral center, the addition of an AI to mTORi re-established disease control in a significant subset of patients progressing on prior mTORi therapy.These results highlight a potentially effective, well-tolerated, and accessible treatment strategy for selected patients with advanced PEComa.Prospective validation and biomarker-driven patient selection are warranted to optimize this clinical benefit.
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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 | 0.000 |
| 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) | 0.000 | 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".