Prediction of one-year recurrence among breast cancer patients undergone surgery using artificial intelligence-based algorithms: a retrospective study on prognostic factors

Abstract

Background and aim: Breast cancer is highly prevalent, with an increasing trend in women globally. Although the survival of breast cancer is relatively high, the recurrence rate is also high, demanding effective predictive solutions to breast cancer prognosis among post-operative patients. So far, Artificial intelligence algorithms integrated with various clinical data have demonstrated potential predictive capability regarding breast cancer recurrence.

Objective: This study aims to specifically conduct a predictive analysis of one-year recurrence of breast cancer by comparing and analyzing different machine learning and deep learning algorithms trained by structural prognostic data.

Materials and methods: This retrospective study was carried out using one database, including 1156 post-operative breast cancer data from 30 January 2020 to 30 December 2022, in three clinical centers in Tehran City. The inclusion criteria were patients who had undergone at least one surgery, had at least one year of medical records, and did not have other conditions. The patients who were diagnosed with malignant BC and had undergone adjuvant therapies without surgery were excluded from the study. Twenty-three prognostic factors were utilized to train algorithms to establish prediction models for the one-year recurrence of breast cancer. The data were analyzed using univariate and adjusted correlation-based methods and chosen machine learning and deep learning algorithms. The discrimination, calibration, and clinical utility were leveraged to assess the algorithms' performance efficiency. The SHapley Additive exPlanations plot was generated to identify the prominent prognostic factors affecting the one-year recurrence of breast cancer.

Results: Totally, 445 relapsed and 711 non-relapsed cases were utilized in this study. Our empirical study showed that the random forest with a positive predictive value of 0.96, negative predictive value of 0.92, sensitivity of 0.92, specificity of 0.96, accuracy of 0.94, F-score of 0.94, area under the receiver operator characteristics curve of 0.919 was the best-performing model for predicting the breast cancer recurrence. As the analysis of SHapley Additive exPlanations indicated, the tumor grade, HER-2, and the number of lymph nodes involved were more significant predictors.

Conclusion: The current study demonstrated the potential predictive power of the random forest for early predicting tumors among breast cancer patients who have undergone surgery and its utility in enhancing decision-making in clinical environments. It is crucial in promoting the prognosis, more effectively choosing therapies, augmenting post-operative breast cancer patients' survival, and controlling the limited healthcare resources.

Clinical trial number: Not applicable.

Keywords: Artificial intelligence; Breast cancer; Clinical utility; Prognosis; Tumor recurrence.

Fig. 1

The flowchart of the sample selection process

Fig. 2

The ROC curve plots for ML models

Fig. 3

The ROC curve plots for DL models

Fig. 4

Calibration plots for ML models

Fig. 5

Calibration plots for DL models

Fig. 6

The DCA of AI-based models. The NB curves for the chosen prognostic factors are indicated. The X and Y axes indicate the risk thresholds for predicting the one-year BC recurrence and NB, respectively

Fig. 7

The SHAP summary plot for one-year BC recurrence. It put together feature importance and feature influence. Each point on the summary plot illustrates the Shapley value associated with one instance. The positions on the y and x axes represent the feature and Shapley values, respectively. The blue and red colors show the value of the individual features from low to high, respectively