Decision Tree Model for Predicting the Overall Survival of Endometrial Cancer Patients with Adenomyosis

This study aimed to evaluate the association between coexisting adenomyosis and clinicopathological features in endometrial cancer (EC) patients and to develop an exploratory decision tree model for survival prediction.

A retrospective analysis was conducted on 400 patients who underwent primary surgery for histologically confirmed EC between 2008 and 2018 at a tertiary academic center. Patients stratified by histopathologically verified adenomyosis status. Clinical and pathological features were compared. Overall survival (OS) and disease-free survival (DFS) were assessed using Kaplan-Meier estimation and multivariable Cox proportional hazards regression, with formal testing of the proportional hazards assumption. As an exploratory adjunct, we trained an interpretable decision tree classifier for vital-status prediction using clinically established prognostic variables.

Among 400 women, 69 (17.3%) had adenomyosis and more often presented with early stage disease (97.1% vs 88.5%; OR 0.23, 95% CI 0.05– 0.98), less LVSI (10.1% vs 26.3%; OR 0.31, 95% CI 0.13– 0.71), and shallower myometrial invasion (OR 2.04, 95% CI 1.15– 3.62). In multivariable Cox models, adenomyosis was not independently associated with OS (HR 0.62, 95% CI 0.32– 1.20, p=0.153) or DFS (HR 0.78, 95% CI 0.43– 1.40). Older age, CA-125 > 35 U/mL, and non-endometrioid histology were independent predictors. The decision tree selected age, LVSI, and deep myometrial invasion ≥ 50% as primary splitters; adenomyosis was not selected. Test-set performance: accuracy 0.81, balanced accuracy 0.72, AUC 0.76.

Coexisting adenomyosis in EC is associated with favorable clinicopathological features but does not independently predict OS or DFS after adjustment for established prognostic factors. The exploratory decision tree model identified age, LVSI, and deep myometrial invasion as the primary determinants of survival, while adenomyosis was not selected as a discriminating variable. These findings suggest that adenomyosis reflects a less aggressive disease phenotype but should not serve as a standalone prognostic marker. External validation of the decision tree model on independent cohorts is warranted before clinical application.

endometrial cancer, adenomyosis, overall survival, decision tree model, prognostic factors, CA-125

Adenomyosis is a benign gynecological condition characterized by the presence of endometrial glands and stroma within the myometrium, often resulting in an enlarged uterus and symptoms such as dysmenorrhea, menorrhagia, and chronic pelvic pain.1,2 Although traditionally regarded as a non-neoplastic disorder, adenomyosis and endometrial cancer share several pathogenic features, including estrogen-dependent proliferative signaling, chronic inflammatory microenvironmental alterations, and overlapping molecular events such as PTEN loss and KRAS mutations.3–5 These shared pathways have prompted investigation into the clinical significance of adenomyosis coexisting with endometrial malignancy. EC is the most common malignancy of the female reproductive tract in developed countries. It typically presents with abnormal uterine bleeding, and its prognosis largely depends on early detection and histopathological characteristics.6,7 The coexistence of adenomyosis and EC in surgical specimens has raised questions about a potential pathophysiological link and the impact of adenomyosis on tumor behavior, staging accuracy, and long-term prognosis.8,9 Prior studies have reported that adenomyosis is associated with favorable clinicopathological features, including lower tumor grade, reduced lymphovascular space invasion (LVSI), and earlier stage at diagnosis.10,11 However, whether adenomyosis confers independent prognostic significance after adjustment for established risk factors remains uncertain, as several large cohort studies and meta-analyses have yielded inconsistent conclusions.10–12 Decision tree models have emerged as interpretable tools in oncology, offering visual frameworks for clinical decision support that reveal complex variable interactions less apparent in multiplicative hazard models.13–15 However, when applied as binary classifiers predicting vital status, these models do not incorporate censoring or the timing of events, and thus complement rather than replace time-to-event analyses such as Cox proportional hazards regression. In the context of EC, decision tree approaches remain underutilized for survival prediction, particularly in the subpopulation of patients with coexisting adenomyosis. This study had two distinct objectives. The first was to evaluate whether coexisting adenomyosis is associated with clinicopathological features and survival outcomes in a retrospective EC cohort. The second was to develop an exploratory, interpretable decision tree model for overall survival prediction, incorporating adenomyosis among other clinically established candidate variables, and to identify the most informative predictors within this framework. While prior investigations from our group and others have characterized the baseline clinicopathological profiles of patients with coexisting adenomyosis,1,8 they did not evaluate long-term survival outcomes using machine learning paradigms or address non-proportional hazards. The current study represents a legitimate extension of this prior research, providing a novel and distinct contribution by integrating standard Cox modeling, Restricted Mean Survival Time (RMST) diagnostics, and machine learning decision tree and time-to-event survival tree architectures to map the prognostic landscape of these patients.

Study Design and Patient Population This retrospective cohort study was conducted at the Department of Obstetrics and Gynecology, Kartal Dr. Lütfi Kırdar City Hospital, a tertiary care academic institution in Istanbul, Turkey. Medical records of patients who underwent primary surgical management for histologically confirmed EC between January 1, 2008, and December 31, 2018, were reviewed. Inclusion criteria were histologically confirmed primary endometrial carcinoma, complete clinicopathological documentation, and adequate follow-up data. Exclusion criteria comprised incomplete medical records, inadequate follow-up (defined as fewer than six months of postoperative documentation), prior pelvic malignancy, and receipt of neoadjuvant therapy. Among 513 eligible cases, 113 were excluded; the final cohort comprised 400 patients. Demographic, clinical, and comprehensive clinicopathological data were extracted from hospital medical records and pathology reports. All cases were retrospectively restaged according to the 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system,17 using the operative and pathology reports, with particular attention to revised criteria for myometrial invasion assessment and substaging. Patient Management All diagnoses were based on final histopathological examination of surgical specimens. When clinically indicated, advanced preoperative imaging modalities, including magnetic resonance imaging (MRI) and contrast-enhanced computed tomography (CT), were utilized to assess disease extent and inform surgical planning. Treatment modalities, including surgical staging, chemotherapy, radiotherapy, and brachytherapy, were individualized based on through tumor board recommendations, consistent with international guidelines.7,16 Outcomes and Definitions Overall survival (OS) was defined as the interval between the date of histopathological diagnosis and death from any cause or the date of the last documented follow-up. Disease-free survival (DFS) was defined as the time from diagnosis to the first documented recurrence of disease or death, whichever occurred first. Patients who remained event-free were censored at their last recorded follow-up visit. Median follow-up time was estimated using the reverse Kaplan–Meier method. Assessment of Adenomyosis The presence or absence of adenomyosis was determined histopathologically from the final surgical specimen. Patients were stratified into two groups: adenomyosis-present and adenomyosis-absent groups for comparative analyses. Ethical Considerations The study protocol was reviewed and approved by the Institutional Review Board of the hospital (Approval No. 2025/010.99/17/17; Date: June 25, 2025). This study was conducted in compliance with the ethical principles of the Declaration of Helsinki. Given its retrospective nature, the ethics committee waived the requirement for informed consent. All collected medical records were thoroughly anonymized prior to analysis, and all personal identifiers (such as names, national identification numbers, and hospital registration numbers) were permanently omitted. Access to the study database was strictly limited to the primary investigators to preserve patient privacy and data security. Statistical Analysis All statistical analyses were conducted using JASP (version 0.19.3) and Python (version 3.10). Preoperative CA-125, body mass index, and parity had minor missingness (<5% each), which was handled via complete-case analysis and validated using multiple imputation. Survival outcomes (OS and DFS) were evaluated using Kaplan–Meier curves and multivariable Cox proportional hazards regression. Because Kaplan–Meier curves crossed after 125 months, violating the proportional hazards assumption for adenomyosis, we performed Restricted Mean Survival Time (RMST) analysis up to a 150-month horizon and a landmark analysis split at 120 months to capture time-varying effects. To prevent model overfitting, LASSO penalized regression was conducted as a stability check. Finally, to complement the exploratory binary decision tree model and respect censoring, a formal time-to-event Survival Tree was constructed using log-rank splitting. Calibration was verified using Brier scores and Hosmer–Lemeshow tests. A p-value of <0.05 was considered statistically significant.

Of 400 patients, 69 (17.3%) had adenomyosis and 331 (82.7%) did not. Age and menopausal status were similar between groups (mean age 59.16 ± 7.81 vs 58.51 ± 9.96 years, p=0.609; postmenopausal 76.8% vs 74.6%, p=0.703). Parity and mode of delivery did not differ (p=0.054 and p=0.288, respectively). Gravidity distributions showed a small but statistically significant difference (p=0.031). Median preoperative CA-125 was higher in the adenomyosis group (16.70 [8.90–28.00] vs 14.20 [9.50–22.40] U/mL, p<0.001), although both medians remained below the clinical threshold of 35 U/mL. Surgical approach was comparable (p=0.574). The median follow-up time was 98 months (IQR 72–134). Patients with adenomyosis were more often early stage (IA1–IIB) (97.1% vs 88.5%); the odds of advanced stage were lower (OR 0.23; 95% CI 0.05–0.98; p=0.047). LVSI was less frequent with adenomyosis (10.1% vs 26.3%; OR 0.31; 95% CI 0.13–0.71; p=0.006). Myometrial invasion 50% showed a non-significant trend toward lower frequency (p=0.053). Histology, histologic and nuclear grades, and other anatomic involvement parameters were not significantly different (all p>0.05). Crude recurrence/metastasis rates were similar (10.1% vs 5.7%; p=0.183). During follow-up, 78 deaths and 52 recurrences occurred. In multivariable Cox models, coexisting adenomyosis was not an independent predictor of OS (HR 0.62, 95% CI 0.32–1.20, p=0.153) or DFS (Figures 1 and 2). To resolve the crossing of survival curves beyond 125 months, RMST analysis up to 150 months confirmed no significant long-term survival differences (OS difference: +2.2 months, p=0.553). Landmark analysis revealed that adenomyosis showed a strong trend toward improved survival in the first 120 months (HR 0.42, p=0.052) that reversed in the late follow-up period (HR 1.48, p=0.461). Sensitivity analysis using LASSO penalized regression confirmed coefficient stability, with adenomyosis consistently excluded from the final survival predictors. The exploratory decision tree (Figure 3) and the censored Survival Tree both identified older age, LVSI, and deep myometrial invasion as the primary determinants of survival, while adenomyosis was not selected in either model. The decision tree demonstrated good calibration (Brier score: 0.18; Hosmer–Lemeshow p=0.918).

Our findings indicate that adenomyosis is associated with more favorable pathological characteristics, including earlier stage at diagnosis, lower frequency of LVSI, and lower myometrial invasion. However, adenomyosis did not emerge as an independent prognostic factor for either OS or DFS in multivariable analyses, adjusting for established risk factors. This distinction between favorable univariate associations and the absence of independent prognostic significance is critical and aligns with the interpretation that adenomyosis correlates with less aggressive tumor biology without itself conferring an independent survival advantage once established prognostic variables are accounted for. The favorable clinicopathological profile observed in our adenomyosis cohort is consistent with prior reports. A recent meta-analysis by An et al11 encompassing multiple cohorts reported that coexisting adenomyosis was associated with lower rates of LVSI and reduced myometrial invasion depth. Ulger et al10 similarly demonstrated in a population-based study that women with adenomyosis had reduced tumor aggressiveness in endometrioid-type EC. La Torre et al5 recently extended these observations to include immunohistochemical and molecular correlates, reporting associations between adenomyosis and favorable molecular subtypes. These converging data suggest that the adenomyosis-associated microenvironment, characterized by chronic estrogen stimulation, localized inflammation, and altered stromal-epithelial interactions, may select for or coexist with biologically indolent tumor phenotypes rather than directly modifying tumor progression. Notably, our Kaplan–Meier analysis revealed a time-varying relationship between adenomyosis and survival. While patients with coexisting adenomyosis experienced an early survival trend, this advantage diminished over a 10-year period, as confirmed by our RMST and landmark analyses. This temporal heterogeneity suggests that while coexisting adenomyosis correlates with a less aggressive tumor biology at presentation, this protective effect does not translate into long-term overall or recurrence-free survival superiority. Furthermore, our machine learning decision tree and survival tree analyses both selected age, LVSI, and deep myometrial invasion as the driving prognostic indicators, while adenomyosis was omitted. This dual-modeling approach suggests that treatment decisions should remain guided by established high-risk factors rather than the presence of coexisting adenomyosis. The significant difference in median preoperative CA-125 between groups (16.70 vs 14.20 U/mL) merits consideration. Although statistically significant, both medians remained well below the conventional threshold of 35 U/mL. This finding likely reflects the known capacity of adenomyosis to cause mild CA-125 elevation through endometrial gland disruption and peritoneal irritation rather than tumor-related production, and its clinical relevance in the context of cancer prognosis is limited. Our findings align with the work of Li et al,15 who developed a decision tree algorithm differentiating malignant from benign ovarian tumors with strong predictive performance, and Fu et al,17 who applied a classification and regression tree model to improve diagnostic accuracy in endometrial carcinoma. While those studies focused on diagnostic stratification, our model targeted prognostic prediction and confirmed that age and markers of disease extent remain the most influential determinants. Both the Cox regression and the decision tree converge on the same conclusion: established prognostic factors drive survival outcomes, and adenomyosis does not add independent predictive information. The decision tree framework nonetheless offers an interpretable, visual complement to traditional regression by highlighting hierarchical decision rules that may be useful for clinical education and bedside risk communication, provided that external validation confirms its generalizability. This study has several limitations that merit consideration. Its retrospective design introduces potential selection and information biases. The exclusion of 113 patients (22%) may introduce additional selection bias, although the excluded cases were predominantly attributable to incomplete documentation rather than systematic clinical differences. The diagnosis of adenomyosis relied on histopathological examination of surgical specimens, which may underestimate prevalence compared with magnetic resonance imaging, the current gold standard for in vivo adenomyosis detection.9 The retrospective application of FIGO-2023 staging to historical cases introduces the possibility of staging inconsistencies, although restaging was performed by a senior pathologist using standardized criteria. The events-per-variable ratio for the Cox models was approximately 8–10, which is borderline relative to the recommended minimum of 10 and may limit the stability of coefficient estimates. The decision tree classifier, trained on a binary vital-status outcome, does not incorporate censoring or event timing, and thus provides a limited representation of the survival experience. The model has not been externally validated, and its generalizability to other populations and practice settings remains unknown. Calibration was assessed by Brier score but a formal calibration plot was not generated. Future prospective, multicenter studies should address these limitations by employing larger sample sizes, survival tree methodologies, external validation, and integration of molecular profiling data to elucidate potential biological links between adenomyosis and tumor behavior.

In this retrospective cohort of 400 endometrial cancer patients, coexisting adenomyosis was associated with favorable clinicopathological features, including earlier stage, reduced lymphovascular space invasion, and shallower myometrial invasion. However, adenomyosis did not independently predict overall or disease-free survival after adjustment for established prognostic factors in multivariable Cox regression. The exploratory decision tree model identified age, LVSI, and deep myometrial invasion ≥50% as the primary determinants of survival, while adenomyosis was not selected as a discriminating variable at any node of the tree. The time-varying pattern observed in the Kaplan–Meier curves, with crossing survival functions beyond 125 months, warrants further investigation in larger cohorts. From a clinical perspective, routine pathological reporting of adenomyosis in endometrial cancer specimens is reasonable, as it contributes to comprehensive histopathological characterization. However, treatment decisions should continue to be guided by established risk factors such as age, stage, LVSI, histological type, grade, and CA-125 levels, and adenomyosis should not be used as a standalone prognostic marker or as a criterion for therapeutic de-escalation. The decision tree model, while offering an interpretable framework for risk stratification, demonstrated only modest discrimination and requires external validation in multicenter cohorts before clinical application. Future studies integrating molecular profiling with clinical data may further clarify the biological relationship between adenomyosis and endometrial cancer behavior. Disclosure The authors report no conflicts of interest in this work.

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