Machine learning Nomogram for Predicting endometrial lesions after tamoxifen therapy in breast Cancer patients

In this study, we developed and evaluated three machine learning models to accurately predict the risk of endometrial lesions in premenopausal breast cancer patients undergoing TAM therapy. The LASSO regression combined with logistic regression achieved the best predictive performance, demonstrating an accuracy of 0.853 and precision of 0.917 using four easily accessible patient features. This model exhibited high diagnostic performance with an AUC of 0.891 (95% CI: 0.777–1.000). The findings confirm that ultrasonographic features, duration of TAM therapy, endometrial thickness, and colporrhagia symptoms are significant predictors of endometrial lesions.

A national retrospective study of 102 breast cancer patients treated with TAM postoperatively found that the duration of TAM use and symptoms of abnormal colporrhagia were significant risk factors for developing endometrial lesions, consistent with our findings. Additionally, substantial epidemiologic evidence suggests that TAM is associated with an increased risk of endometrial lesions, with the risk of developing endometrial carcinoma (EC) being 1.5–6.9 times higher in a dose- and time-dependent manner¹⁹. The ATLAS study found that patients using TAM for 10 years had a higher cumulative risk of endometrial cancer compared to those using it for 5 years⁹. However, only 10% of patients in the ATLAS study were premenopausal, which may limit the generalizability of its findings.

Our study showed that the duration of TAM was an independent risk factor for developing endometrial lesions, consistent with previous studies²⁰. Choi et al. demonstrated that benign endometrial disease incidence was highest in subjects under 40 years of age treated with TAM, significantly increasing the risk of endometrial cancer²¹. Similarly, Liu et al. found that the standardized incidence of endometrial cancer was elevated in breast cancer patients diagnosed after the age 40²². Younger patients treated with TAM have a higher risk of subsequent endometrial cancer, particularly those aged 40–49²³. Bergman’s study further indicated that TAM-induced endometrial cancers were more malignant and aggressive²⁰. Some studies, however, have shown no correlation between TAM and endometrial lesions. For instance, Takashima²⁴found no significant association between shorter TAM therapy duration and endometrial lesions. Chiofalo and Chu also reported no correlation between TAM and endometrial cancer development^23,25,26.

In our study, ultrasound characteristics emerged as the most important factor in predicting endometrial lesions, aligning with previous research. Ultrasound is the preferred monitoring tool, with abnormal occupancy or heterogeneous endometrial echogenicity on ultrasound increasing the likelihood of endometrial lesions and the need for endometrial biopsy. Previous NSABP studies, which included mainly postmenopausal women, suggested no additional monitoring for asymptomatic women to avoid unnecessary invasive procedures. However, this may underestimate the risk in premenopausal patients^27,28. Young breast cancer patients undergoing prolonged TAM therapy may require closer monitoring. Endometrial screening and evaluation should be conducted before TAM treatment, followed by regular transvaginal ultrasound monitoring to enable early detection and management of endometrial lesions.

Endometrial thickness was also a significant factor in endometrial lesion occurrence, with the optimal diagnostic threshold being 0.825 cm, consistent with previous findings by Zhouqi and Burkart^2,29. Since TAM stimulates endometrial gland hypertrophy, leading to pharmacological thickening, it is challenging to establish a TAM-related endometrial thickness threshold in young breast cancer patients.

Colporrhagia was identified as a significant risk factor. Patients with colporrhagia are more likely to develop endometrial lesions, and this symptom serves as a warning for early hospital visits, improving detection rates. However, Maria et al. found no difference in abnormal colporrhagia between the case group and patients with normal endometrium, emphasizing the need for further research³⁰.

Most current clinical prediction models rely on linear relationships between variables, which often limit their predictive accuracy. Machine learning applications in medicine are becoming increasingly common, providing innovative tools for clinical diagnosis and prediction. In our study, we applied machine learning techniques to visualize and predict the incidence of endometrial lesions, addressing a critical knowledge gap in evaluating premenopausal breast cancer patients undergoing endocrine therapy. By leveraging LASSO regression and multifactorial logistic regression, we mitigated the risk of overfitting and achieved validation results with an average absolute error of 0.014 between predicted and actual values. These findings highlight the potential of machine learning to revolutionize endometrial lesion prognosis, offering a significant step toward precision medicine in this field. This study holds substantial potential to improve clinical outcomes by enabling earlier detection and more accurate risk prediction of endometrial lesions, particularly in vulnerable populations such as breast cancer patients undergoing hormonal treatments. By providing a theoretical foundation for developing individualized treatment strategies, this research bridges a critical gap in understanding how endocrine therapy impacts endometrial health. Researchers addressed these gaps by integrating robust statistical methods with advanced machine learning algorithms, ensuring model reliability and clinical relevance. Over the next five years, we foresee this area evolving significantly as artificial intelligence and machine learning technologies advance. Future research will likely focus on integrating multi-modal data, including imaging, genomic, and biochemical markers, to enhance the comprehensiveness of predictive models. Such developments could lead to even more accurate tools for clinical decision-making, risk stratification, and personalized treatment strategies. Furthermore, as these technologies are validated and refined, their integration into routine clinical practice will become more widespread, reducing the risk of complications and improving the overall management of breast cancer patients. In parallel, the growing emphasis on personalized medicine will likely catalyze the development of artificial intelligence tools tailored to individual patient profiles, setting new standards for treatment precision and effectiveness.

Our study also has some limitations. First, as a single-center retrospective study, our findings are inherently constrained by limited data diversity and a small sample size, which may reduce the generalizability of our results and introduce potential selection and recall biases. To mitigate these issues, future research should incorporate larger, multicenter cohorts that reflect broader population variability and improve the robustness of the findings. Second, incorporating additional objective indicators, such as hormonal profiles or advanced imaging biomarkers, could enhance the predictive accuracy of our model and establish more precise criteria for clinical use. Third, although our machine learning approach demonstrated promising results, the lack of genetic or molecular data in our analysis represents a key gap. Recent research has identified specific genes associated with breast cancer recurrence, and emerging biomarkers³¹for breast cancer prognosis could offer valuable inputs to further refine predictive models. Integrating genetic testing results and biomarker data into future studies could significantly enhance the clinical utility and precision of prediction tools³². Lastly, as machine learning models are only as reliable as the data they are trained on, external validation using independent datasets is essential to confirm the reproducibility of our findings. Future studies should prioritize external validation and longitudinal data to strengthen the clinical applicability of these models. Addressing these limitations will ensure that predictive models evolve into robust tools capable of supporting personalized treatment strategies and improving outcomes for breast cancer patients.