Evidence map›Paper›PMID 42558426›Full record

ArticleFrontiers in endocrinology2026

Real-world safety profile of mitotane in adrenocortical carcinoma: a retrospective cross-sectional study integrating pharmacovigilance and interpretable machine learning.

Jun Wang, Yang Yang, Ke Xu, Ning Hou, Chenyu Guo

Abstract read
In one paragraph

Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jun WangDepartment of Pharmacy, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Yang YangDepartment of Pharmacy, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China.
Ke XuDepartment of Reproductive Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Ning HouDepartment of Pharmacy, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Chenyu GuoDepartment of Pharmacy, Yantai Yuhuangding Hospital Affiliated to Qingdao University, Yantai, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mitotane remains the cornerstone of adrenocortical carcinoma. However, its real-world safety profile is poorly defined. This study systemically evaluated mitotane-associated adverse events (AEs) and characterized multivariable patterns of serious outcomes using a combined pharmacovigilance and machine learning approach. Methods: Mitotane-related AE reports (2004-2025) were extracted from the FDA Adverse Event Reporting System (FAERS). Disproportionality analyses were performed via four algorithms. Detected signals were categorized using a clinical priority scoring system based on signal strength, mortality, seriousness, and existing evidence. Time-to-onset was evaluated utilizing Kaplan-Meier methods. Elastic net logistic regression and Extreme Gradient Boosting (XGBoost) models characterized features associated with serious outcomes, with performance assessed by the area under the receiver operating characteristic curve (AUROC). Results: Among 870 identified reports, 45.7% involved serious outcomes. We detected 75 safety signals, predominantly involving gastrointestinal, neurological, endocrine, and metabolic systems. Notably, 21 signals (28.0%) were assigned moderate clinical priority, with adrenal insufficiency as the highest-priority signal. Time-to-onset analysis showed that endocrine AEs had a significantly longer onset time than non-endocrine AEs. Both models demonstrated consistent characterization of multivariable patterns associated with serious outcomes (elastic net AUROC = 0.830, XGBoost AUROC = 0.869). Key contributors to serious outcomes included reporting country, medical event designation, organ system involvement, and medication complexity. Conclusion: This large-scale study provides a real-world safety assessment of mitotane. Integrating signal detection, clinical prioritization, and interpretable machine learning reveals multivariable patterns underlying serious AEs. These findings support a risk-stratified monitoring approach to enhance the clinical safety of mitotane therapy.

Indexed as

Adrenal Cortex NeoplasmsAdrenocortical CarcinomaAntineoplastic Agents, HormonalMachine LearningMitotanePharmacovigilanceAdultAdverse Drug Reaction Reporting SystemsAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedRetrospective StudiesAntineoplastic Agents, HormonalMitotaneclinical prioritizationcross-sectional studydisproportionality analysisFAERSmachine learningmitotane

Identifiers

PMID42558426
PMCPMC13437372

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.