The efficacy of adjuvant mitotane therapy and radiotherapy following adrenalectomy in patients with adrenocortical carcinoma: A systematic review and meta-analysis
Bibliographic record
Abstract
Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy with a high recurrence rate after surgical therapy with curative intent. Adjuvant radiotherapy (RT) and mitotane therapy have been proposed as options following the adrenalectomy. However, the efficacy of adjuvant RT or mitotane therapy remains controversial. We aimed to evaluate the efficacy of adjuvant therapy in patients who underwent adrenalectomy for localised ACC. The PubMed, Scopus, and Web of Science databases were queried on March 2024 for studies evaluating adjuvant therapies in patients treated with surgery for localized ACC (PROSPERO: CRD42024512849). The endpoints of interest were overall survival (OS) and recurrence-free survival (RFS). Hazard ratios (HR) with 95% confidence intervals (95%CI) were pooled in a random-effects model meta-analysis. One randomized controlled trial (n = 91) and eleven retrospective studies (n = 4,515) were included. Adjuvant mitotane therapy was associated with improved RFS (HR: 0.63, 95%CI: 0.44-0.92, p = 0.016), while adjuvant RT did not reach conventional levels of statistical significance (HR:0.79, 95%CI:0.58-1.06, p = 0.11). Conversely, Adjuvant RT was associated with improved OS (HR:0.69, 95%CI:0.58-0.83, p<0.001), whereas adjuvant mitotane did not (HR: 0.76, 95%CI: 0.57-1.02, p = 0.07). In the subgroup analyses, adjuvant mitotane was associated with better OS (HR:0.46, 95%CI: 0.30-0.69, p < 0.001) and RFS (HR:0.56, 95%CI: 0.32-0.98, p = 0.04) in patients with negative surgical margin. Both adjuvant RT and mitotane were found to be associated with improved oncologic outcomes in patients treated with adrenalectomy for localised ACC. While adjuvant RT significantly improved OS in general population, mitotane appears as an especially promising treatment option in patients with negative surgical margin. These data can support the shared decision-making process, better understanding of the risks, benefits, and effectiveness of these therapies is still needed to guide tailored management of each individual patient.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".