The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer

Abstract

PurposeThis study aimed to develop a machine learning model to retrospectively study and predict the recurrence risk of breast cancer patients after surgery by extracting the clinicopathological features of tumors from unstructured clinical electronic health record (EHR) data.MethodsThis retrospective cohort included 1,841 breast cancer patients who underwent surgical treatment. To extract the principal features associated with recurrence risk, the clinical notes and histopathology reports of patients were collected and feature engineering was used. Predictive models were next conducted based on this important information. All algorithms were implemented using Python software. The accuracy of prediction models was further verified in the test cohort. The area under the curve (AUC), precision, recall, and F1 score were adopted to evaluate the performance of each model.ResultsA training cohort with 1,289 patients and a test cohort with 552 patients were recruited. From 2011 to 2019, a total of 1,841 textual reports were included. For the prediction of recurrence risk, both LSTM, XGBoost, and SVM had favorable accuracies of 0.89, 0.86, and 0.78. The AUC values of the micro-average ROC curve corresponding to LSTM, XGBoost, and SVM were 0.98 ± 0.01, 0.97 ± 0.03, and 0.92 ± 0.06. Especially the LSTM model achieved superior execution than other models. The accuracy, F1 score, macro-avg F1 score (0.87), and weighted-avg F1 score (0.89) of the LSTM model produced higher values. All P values were statistically significant. Patients in the high-risk group predicted by our model performed more resistant to DNA damage and microtubule targeting drugs than those in the intermediate-risk group. The predicted low-risk patients were not statistically significant compared with intermediate- or high-risk patients due to the small sample size (188 low-risk patients were predicted via our model, and only two of them were administered chemotherapy alone after surgery). The prognosis of patients predicted by our model was consistent with the actual follow-up records.ConclusionsThe constructed model accurately predicted the recurrence risk of breast cancer patients from EHR data and certainly evaluated the chemoresistance and prognosis of patients. Therefore, our model can help clinicians to formulate the individualized management of breast cancer patients.

Optimized Deep Convolutional Neural Network for the Prediction of Breast Cancer Recurrence

“…The proposed breast cancer recurrence prediction model shows exceptional performance when compared to existing methods, like that of (Gianni et al, 2022), demonstrating substantial advantages in accuracy, precision, recall, and F1-score metrics. In comparison to the result of (Zeng et al, 2023), the infusion of additional dense layers into DCNN emerges as a pivotal element contributing to the observed superiority. A meticulous comparison with other schemes (as outlined in Table 3) shows that the DCNN method excels across various performance measures, affirming its efficacy in classification and prediction tasks.…”

A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

“…Others, such as Osareh and Shadgar [21], Yue et al [23], Ming et al [26], Chaurasia and Pal [28], Naji et al [29], Rabiei et al [30], Nindrea et al [34], Acevedo et al [36], Santiago-Montero et al [67], have used machine learning algorithms to predict breast cancer diagnosis. Addittionally, in the same line, Ahmad et al [22], Zeng et al [31] use machine-learning strategies to predict breast cancer recurrence. Other authors, such as Yerukala Sathipati and Ho [68], had intended to predict the disease's different stages and proposed treatment strategies.…”

“…Regarding the early detection of breast cancer, some authors in the world have been working with some sophisticated techniques and methodologies, such as the machine learning approach and others (see., e.g., Osareh and Shadgar [21], Ahmad et al [22], Yue et al [23], Ganggayah et al [24], Rajendran et al [25], Ming et al [26], Rajendran et al [27], Chaurasia and Pal [28], Naji et al [29], Rabiei et al [30], Zeng et al [31]), which has allowed them to help predict and classify different types of diagnoses, estimate the survival rate, and provide clinical follow-up to patients with breast cancer that facilitate decision making by the health team in the aid of patients. Some applications include the XAI algorithm, favoring health team knowledge and their decision making (see Idrees and Sohail [32], Rodriguez-Sampaio et al [33]).…”

Unleashing the Power of OpenAI in Shaping the Future of Cancer Research

“…Machine learning algorithms can identify patterns and correlations in large datasets of patient data to help predict how a patient's cancer will progress and how they will respond to different treatments [33][34][35][36] . This can help clinicians make more informed decisions about treatment options, potentially improving outcomes and saving lives 37,38 . OpenAI can also make it easier to identify patients who are at a higher risk of developing cancer or who may benefit more from certain preventive measures 16,39 .…”