Postoperative Recurrences in Patients Operated for Pheochromocytomas and Paragangliomas: New Data Supporting Lifelong Surveillance
Bibliographic record
Abstract
At least 10% of pheochromocytomas (PHEOs) and paragangliomas (PGLs) (PPGLs) may recur after the initial surgery. Guidelines recommend annual screening for recurrence in non-metastatic tumors for at least 10 years after the initial surgical resection and lifelong screening in high-risk patients. However, recent data suggest that a shorter follow-up might be appropriate. We performed a retrospective analysis on patients with PPGLs who had local and/or metastatic recurrences between 1995 and 2020 in our center. Data were available for 39 cases of recurrence (69.2% female) including 20 PHEOs (51.3%) and 19 PGLs (48.7%) (13 head and neck (HNPGL) and 6 thoracoabdominal (TAPGL)). The overall average delay of recurrence was 116.6 months (14-584 months) or 9.7 years and the median was 71 months or 5.9 years. One-third of the cohort had a recurrence more than 10 years after the initial surgery (10-48.7 years). The average tumor size at initial diagnosis was 8.2 cm for PHEOs, 2.7 cm for HNPGLs, and 9.6 cm for TAPGLs. Interestingly, 17.6% of PHEOs were under 5 cm at the initial diagnosis. Metastatic recurrence was identified in 75% of PHEOs, 15.4% of HNPGLs, and 66.7% of TAPGLs. Finally, 12/23 (52.2%) patients with recurrence who underwent genetic testing carried a germline mutation. Overall, the safest option remains a lifelong follow-up.
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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".