Breast cancer is the most common cancer in women. Although the ten-year survival rate of breast cancer has reached 82% (1, 2), there are still 20% to 60% of surviving patients experiencing chronic postsurgical pain (CPSP) after breast cancer surgery, resulting in a reduced quality of life and functional impairments (3–5). Predicting the risk of CPSP after breast cancer surgery can help clinicians identify those with a higher risk of CPSP and leading to earlier therapeutic interventions. In addition, identifying patients with a lower risk of CPSP could also prevent unnecessary therapy, saving limited medical resources.

Numerous factors have been found to be associated with CPSP after breast cancer surgery in the past decade, including social-demographic, intraoperative, and postoperative factors (6–8). Several models to predict CPSP have also been developed, mostly in European breast cancer patients (9–12). However, despite the acceptable discrimination and calibration of those models, the low clinical utility, especially positive predictive values (PPVs) around 0.2 at a 20~60% risk level of CPSP, limits their application in clinical practice, making it still difficult to identify high-risk patients early (9–12). Moreover, considering the different genetic, cultural and social backgrounds between different ethnic groups, which may also play an important role in this complex pathophysiological disease status, the extrapolation of those tools in Asian patients may be potentially limited.

Machine learning is a form of artificial intelligence that uses computer algorithms to identify nonlinear data patterns within large datasets to formulate outcome prediction and indicate improved prediction performance compared to the traditional prediction methods, which may be more suitable for the prediction of CPSP (13, 14). For example, random forest (RF) designs meta estimators that fit a number of decision tree classifiers on various sub-samples of the dataset and uses average to improve the predictive accuracy and control over-fitting. Gradient boosting decision tree (GBDT) gives a prediction model in the form of an ensemble of weak prediction models, which has strong generalization ability and performed well in both classification and regression tasks. Extreme gradient boosting (XGBoost) is an algorithm built on the GBDT framework and processed the missing data efficiently and flexibly (15). These three algorithms were also deep learning algorithms, the architecture of machine learning designed to mimic the neurological structure of the human brain, which might be more powerful than traditional algorithms in data analysis and prediction (16). Therefore, in this study, we intend to select these three algorithms with good fitting ability to develop prediction models for CPSP in Asian patients with breast cancer, and compare these models with logistic regression, hoping to improve the performance and clinical utility of the prediction models.

Contributions of this study:

1 This time we use machine learning approaches to develop prediction models, intending to improve the clinical utility and help clinicians identify high-risk CPSP patients more accurately and confidently.

2 Considering the different genetic, cultural and social backgrounds between different ethnic groups, which may also play an important role in CPSP, we focused on Chinese patients to explore predictors and models more suitable for Chinese population.

Breast cancer is considered to be the most prevalent cancer in women. As survival improves primarily due to earlier detection and improvements in the therapeutic approaches (28), new challenges emerge. Consistent with previous reports, 22.1% patients in our study developed CPSP at 12 months after breast cancer surgery, which could reduce their quality of life (3–5).

At present, more and more prediction models are established to identify the high-risk CPSP patients in advance (9–12). Sipilä R et al. created a 6-factor risk index to predict persistent pain at 6 months after surgery using Bayesian model in Finland population prospectively (9). Another 4-item preoperative risk score for persistent pain at 4 months after surgery was developed with multivariable logistic regression model in Switzerland (10). Meretoja TJ et al. created a web-based risk calculator using logistic regression analyses to assess the risk of persistent pain at 1 year after surgery in European breast cancer cohorts (11). However, most of these models are conducted in European patients, with a lack of studies on Asian patients, and the low clinical utility limits their application in clinical practice. Machine learning is a form of artificial intelligence that uses computer algorithms to identify nonlinear data patterns within large datasets to formulate outcome prediction (13), which may be more suitable for the prediction for CPSP. Considering the poor performance of machine learning algorithms chosen in previous studies, this time we selected three representative machine learning methods with good fitting ability, including RF, GBDT, and XGBoost to develop prediction models, based on a high-quality dataset and rigorous model evaluation, intending to improve their clinical utility.

Our results suggested that, although the superiority of machine learning models over logistic regression model in discrimination and calibration are not particularly prominent, they performed better in specificity, PLR, and PPV. We found that posterior positive probability, reflected by PPV, was around 0.8 using machine learning methods, which reflects a large increase from the prior probability of 0.2 (22.1%). That means the probability of developing CPSP is as high as 80% if the patient was categorized as high risk using our prediction model. This indeed could improve the models’ clinical utility and help clinicians identify high-risk CPSP patients more accurately and confidently, so that appropriate treatments could be taken promptly. Considering the high quality and low lost to follow-up rate (<1%) of our dataset, these results were reliable and convincing.

According to the feature selection, acute postoperative pain which reflected by a higher NRS within 2 days after surgery, is one of the risk factors for CPSP, consistent with previous research (29). It has been reported that the paravertebral block could improve early postoperative analgesia and prevent CPSP by impacting the transition from acute to chronic pain (7, 8, 30). This may explain why the patients under paravertebral block with propofol general anesthesia (PPA group) had a lower risk of CPSP, compared with those who under fentanyl with sevoflurane general anesthesia (GA group).

Menstruation status was another important predictor for CPSP after breast cancer surgery. It was found in our results that patients under post-menopausal stage seemed to experience a higher risk of CPSP. The decline of estrogen and various musculoskeletal and climacteric symptoms may be the possible explanations (31). However, some reports suggested a younger age was associated with greater risk of CPSP (25). This might be related to the social background difference in different ethnic groups. Post-menopausal women are generally over 55 years old and tend to be retired in China. In most cases, their children, now adults, have left home for work or study, making them experience low hormone levels and the loneliness due to unaccompanied simultaneously. These might lead to a more anxious mental status which could cause or aggravate the feel of pain (32).

Recently Wang Y, et al. also constructed prediction models of chronic pain after breast cancer surgery using a variety of machine learning techniques (33). However, these models not only have lower discriminatory power, sensitivity and PPV, but also do not include psychosocial factors. Pain is a strongly subjective feeling that is affected by the disease itself and relevant clinical factors, as well as social factors, cultural backgrounds, psychological state, and so on. Therefore, in this study, we also took medical insurance level into consideration to represent patients’ social backgrounds and reflect their psychosocial status. According to the current forms of medical insurance in China, we divided patients’ medical insurance level into no medical insurance, rural medical insurance, which is the new cooperative medical scheme (NCMS) for rural residents, and urban medical insurance, which includes UEBMI, URMI and CMI (34). Our results demonstrated that patients with urban medical insurance suffered a higher risk of CPSP. Urban medical insurance could represent the urban residents. They usually have higher economic and educational level and pay more attention about their physical health, life quality, psychological condition and self-feeling, therefore more prone to exacerbate the feeling of pain. Conversely, patients living in rural areas, often with NCMS, may suffer from a heavier burden of life, have a lower level of education and self-focus, and thus seldom care about inconspicuous pain.

Our study also had several limitations. First, it was a secondary analysis based on the dataset in one single center of a multicenter RCT. Although the data in this study were accurate and complete with low drop-out rate, relatively strict inclusion and exclusion criteria in the original trial limited the external validity of our results. Second, despite the machine learning models showed better specificity and PPV, their sensitivity and NPV were not good. Further studies were urgently needed to improve models’ general performance. Third, our prediction model included some specific cultural differences such as insurance in China, which might make the global applicability of the model limited. Finally, psychosocial factors may play an important role in CPSP after breast cancer surgery. Although some psychosocial factors were included in our analyses and model development, they are not enough, especially lacking of data on underlying anxiety. Studies incorporating a more comprehensive analysis of psychosocial factors should be conducted in the future.

This study developed prediction models for CPSP at 12 months after breast cancer surgery based on machine learning approaches, which could assist clinicians to identify high-risk patients more accurately and conduct clinical interventions in advance. Using machine learning methods could be a novel approach to predict CPSP and help tailoring precise management for patients with breast cancer, leading to a better prognosis and an increased quality of life.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of our hospital (Number: S-638). The patients/participants provided their written informed consent to participate in the study.

CS: data acquisition and analysis, manuscript preparation. ML: data acquisition and analysis, tables and figures preparation. LL: data acquisition and analysis, manuscript preparation. LP: study design, data acquisition and analysis, manuscript revision and guarantor. YZ: data analysis and manuscript revision. GT: data acquisition. ZZ: data acquisition. YH: study design and manuscript revision. CS, ML, and LL contributed equally to this study. All authors contributed to the article and approved the submitted version.