ArticleRevista da Associacao Medica Brasileira (1992)2026
Prognostic impact of adenomyosis in cervical cancer: insights from machine learning-driven survival analysis.
Article in Revista da Associacao Medica Brasileira (1992), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to determine whether adenomyosis is an independent prognostic factor in cervical cancer using integrated survival analysis and machine-learning models.
methodsThis retrospective cohort study included 131 patients with early-stage cervical cancer treated surgically between 2008 and 2020. Patients were stratified by the presence (n=28) or absence (n=103) of adenomyosis based on final histopathology. Kaplan-Meier curves and log-rank tests assessed overall survival. Independent prognostic factors were identified through multivariate Cox regression and logistic regression analyses, supplemented by machine-learning decision tree modeling to evaluate variable importance and model performance.
resultsWomen with adenomyosis had no significant differences in tumor size, histology, depth of stromal invasion, lymphovascular space invasion, parametrial involvement, lymph node metastasis, or International Federation of Gynecology and Obstetrics 2018 stage. Kaplan-Meier analysis demonstrated shorter overall survival in the adenomyosis group (median overall survival 31.3 vs. 64.8 months, log-rank p=0.045). Multivariate Cox regression identified age, tumor size, International Federation of Gynecology and Obstetrics stage III, histologic grade, lymphovascular space invasion, parametrial infiltration, and vaginal involvement as independent determinants of overall survival (all p<0.05). Adenomyosis status did not retain prognostic significance after adjustment (HR 0.91, p=0.818). Decision tree models corroborated these findings, with International Federation of Gynecology and Obstetrics stage and tumor size emerging as the most influential predictors of survival and recurrence.
conclusionAlthough adenomyosis was associated with shorter overall survival in univariate analysis, it did not function as an independent prognostic factor after adjustment for established clinicopathological variables in both multivariate Cox regression and machine-learning decision tree models. These findings indicate that prognostic stratification in cervical cancer should remain guided by tumor burden, stage, and histopathological risk factors.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.