Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database

Comparative effectiveness of surgical modalities

The findings of our study underscore the superiority of BCS + RT over mastectomy in terms of both BCSS (p < 0.001) and OS (p < 0.001) in EBC patients post-NST. The observed improvement in outcomes aligns with several observational studies that have consistently reported favorable survival outcomes associated with BCS + RT in comparison to mastectomy^21,22,23. Additionally, in a meta-analysis including 16 studies with a combined total of 3531 patients, Sun et al. showed that no significant difference in local recurrence and regional recurrence (p = 0.26 and p = 0.03), while they figured out a lower distant recurrence (p < 0.01), a higher disease-free survival (p < 0.01) and a higher OS (p < 0.01) in BCS + RT compared with mastectomy²⁴. This could be due to various factors, including the biological behavior of the residual disease, the impact of radiotherapy, and the overall management strategy associated with BCS + RT. Furthermore, a rigorous and detailed postoperative follow-up regimen for these patients might facilitate the early detection and management of potential recurrences²⁵. The evolving landscape of breast cancer management, particularly in the realm of EBC following NST, necessitates a nuanced understanding of the comparative effectiveness of treatment modalities^26,27. Notably, our analysis, after PSM to balance relevant covariates, reinforces the robustness of these findings.

In this analysis, several factors associated with survival outcomes were identified, including namely, age, race, marital status, rural–urban status, grade, tumor site, T stage, N stage, molecular subtype, and response to neoadjuvant therapy. This is in accordance with several previous studies^22,28,29. Considering these factors, we conducted subgroup analyses for T stage, N stage, and response to neoadjuvant therapy, respectively. Firstly, in our analysis of T1 stage tumors, we observed no statistically significant difference in BCSS between patients who underwent BCS + RT and those who underwent mastectomy. Although there is some difference in OS, it is less pronounced compared to the more evident differences observed in T2 and T3 stages. We deduced that patients at T1 stage generally present with smaller tumors, characterized by limited local invasion. In such cases, both of these surgical approaches may effectively control the disease. As the tumor size increases, BCS + RT may provide better local control, improving long-term outcomes for patients. We acknowledge that patients with T1 tumors and node-negative status are not typically recommended for NST in routine clinical practice. However, these patients might receive NST due to specific tumor biology, patient preference, or other clinical factors. Including these patients ensures a comprehensive reflection of diverse treatment decisions in real-world clinical settings. According to NCCN guidelines³⁰, in patients with triple-negative or HER2-positive breast cancer, we may consider neoadjuvant therapy even if the tumor size is less than 2 cm and the axillary lymph node is negative. Treatment response provides important prognostic and adjuvant therapy information at an individual patient level, particularly in patients with triple-negative or HER2-positive breast cancer. In the T1 subgroup, there were no significant differences in BCSS and OS between the two groups, further supporting the idea that BCS + RT may provide better local control in patients with a higher tumor burden. Secondly, it is noteworthy that in the analysis of different subgroups based on N stage, we observed significant differences between the two surgical approaches. This may reflect the impact of lymph node involvement on surgical choices. BCS + RT might have an advantage in controlling lymph node involvement more comprehensively, especially in patients with higher N stages, resulting in better survival outcomes. Lastly, this finding is consistent across various subgroups of responses to NST, even in those who do not achieve a complete response. The survival benefit observed across all response categories to NST highlights the importance of considering surgical options beyond the extent of tumor response. BCS + RT may provide better local control in patients with higher tumor burden. Understanding the specific scenarios in which one surgical modality may confer a survival advantage over the other enables a more nuanced and personalized approach to breast cancer management.

Machine learning augmentation

The choice between BCS + RT and mastectomy remains a complex decision, influenced by clinical factors, patient preferences, and the evolving landscape of therapeutic options. However, there is a lack of accurate prediction models in the clinic. As a result, a more accurate and powerful model is needed. To our knowledge, the current study is the largest one to analyze the choice of surgical procedures in EBC patients following NST. Beyond traditional survival analyses, our study introduces six machine learning models to predict long-term outcomes for EBC patients post-NST. The RSF model, based on ten prognostic variables identified through Cox regression and LASSO regression, outperforms other machine learning models, including Rpart, Xgboost, Glmboost, Survctree, and Survsvm, in terms of C-index in both training and validation cohorts.

The RSF algorithm, first proposed in 2008³¹, demonstrates superior predictive performance compared to the classical Cox model, highlighting the potential of machine learning to improve prognostic accuracy. This extension of traditional survival analysis leverages ensemble learning by constructing multiple survival trees. Through random sampling and feature selection, each tree predicts survival outcomes, and their collective results enhance robustness and reduce overfitting. This method integrates the advantages of random forests, offering a powerful tool for predicting time-to-event outcomes. Implementation involves random sampling during tree construction, yielding a diverse set of survival trees. The final prediction is an aggregation of individual tree predictions. Notably, the model’s ability to handle high-dimensional data, capture non-linear relationships, and account for complex interactions positions it as a valuable tool for clinicians navigating the nuanced landscape of breast cancer treatment decisions³².

In addition, the importance of predictors can be calculated on the basis of the model to identify the factors that are closely related to prognosis for EBC after NST. This information might facilitate the surgery management and reduce the medical burden. So, we observed that clinical features, including N stage, response to neoadjuvant therapy, molecular subtype, grade, surgery type, and T stage, sequentially play significant roles in long-term prognosis, which were also referred to in prior study^22,28,29.

The RSF risk stratification enables the evaluation of a patient’s prognosis according to their clinicopathological profile. The high-risk cut-off (risk score > 21.56) was determined using the calibration curve to identify patients with lower predicted survival rates. This cut-off serves to distinguish between patients with different survival probabilities and to provide actionable information for surgeons and patients when considering surgical options. When applying the model, if the ‘surgical type’ variable for a high-risk patient is changed to BCS + RT, the RSF model may predict longer survival or move them to a lower-risk category. This indicates that opting for BCS + RT instead of mastectomy could potentially benefit these patients. Through individualized survival probability curves, the prognosis is presented with greater precision, providing a more detailed perspective on patients’ outcomes. However, this change in surgical approach might not necessarily lower the patient’s predicted risk score, as it also depends on other clinicopathologic features.

Web-based recommendation system

To bridge the gap between research findings and real-world clinical applications, we developed a cloud-based recommendation system. This system, accessible through a web interface, facilitates dynamic and data-driven decision-making by visualizing survival curves for each treatment plan. By deploying this system on the internet, we empower clinicians to make informed and personalized treatment decisions based on individual patient profiles.

Study limitation

The retrospective nature of our study relies on data extracted from SEER. While SEER provides a wealth of information, it is essential to recognize inherent limitations. Variability in data collection methods, potential coding errors, and the absence of certain clinical variables may introduce biases or limit the granularity of our analysis. Despite employing PSM to mitigate confounding factors, inherent selection biases may persist. Unmeasured or inadequately controlled variables, such as patient menopausal status or detailed information on the neoadjuvant therapy, could impact the observed outcomes. The retrospective design introduces challenges in fully accounting for all relevant clinical variables. Additionally, the study predominantly includes patients from the United States, potentially limiting the applicability of results to diverse healthcare settings. Furthermore, both training and test sets are from the same database, possibly introducing overlap and compromising the model’s generalization capabilities. This implication should be interpreted with caution, as the model’s predictions need external validation in other cohorts to ensure its reliability and generalizability. We strongly recommend further validation studies before applying the web-based prediction model to other patient populations.