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

Purpose

This 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.

Methods

This 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.

Results

A 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.

Conclusions

The 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.

Underlying Breast Cancer Risk and Menopausal Hormone Therapy

The recent Collaborative Group on Hormonal Factors in Breast Cancer (CGHFBC) publication calculated the attributable risk of breast cancer from use of estrogen alone and estrogen plus a synthetic progestogen for less than 5 to 15 or more years of use. This CGHFB report calculated attributable risk based on their findings of relative risk from pooled data from 58 studies. Notably, neither the CGHFBC nor other previous studies have examined the effect of underlying risk of breast cancer on attributable risk. This omission prompted us to determine the magnitude of the effect of underlying risk on attributable risk in this perspective. Meaningful communication of the potential risk of menopausal hormonal therapy requires providing women with the estimated risk above their existing underlying risk (ie, attributable risk). Therefore, we have estimated attributable risks from the data published by the CGHFBC, taking into account varying degrees of underlying risk. Based on the Endocrine Society Guideline on Menopausal Hormone Therapy (MHT), we divided groups into 3 categories of risk: low (1.5%), intermediate (3.0%), and high (6.0%) underlying risk of breast cancer over 5 years. In women taking estrogen plus a synthetic progestogen for 5 to 9 years, the attributable risks of MHT increased from 12, to 42, to 85 additional women per 1000 in the low-, intermediate-, and high-risk groups, respectively. The attributable risks for estrogen alone were lower but also increased based on underlying risk. Notably, the attributable risks were amplified with duration of MHT use, which increased both relative risk and breast cancer incidence.

Functional IGF1R variant predicts breast cancer risk in women with preeclampsia in California Teachers Study

Purpose

Hypertension in pregnancy has been associated with decreased future risk of breast cancer in many but not all studies. In the Marin Women’s Study, pregnancy-induced hypertension was shown to interact with the T allele of a functional IGF1R gene variant, rs2016347, to result in lower breast density, as well as decreased breast cancer risk. Our objective was to explore these findings in a larger sample of women from the California Teachers Study (CTS).

Methods

The CTS cohort consists of over 130,000 female educators. DNA was available from a nested case–control study, which included 2,030 non-Hispanic white women who developed breast cancer and 1,552 controls. The current study included all participants from the case–control group with a self-reported history of preeclampsia (80 cases/57 controls).

Results

Comparing TT to GG genotypes revealed adjusted odds ratios of 0.38 (CI 0.13, 1.14) for all invasive breast cancers, 0.26 (CI 0.07, 0.89) for hormone receptor-positive (HR+) breast cancers, 0.15 (CI 0.04, 0.56) for those with age at first birth (AFB) < 30, and 0.10 (CI 0.02, 0.49) for those with AFB < 30 and HR+ breast cancers. Trend analysis yielded p values of 0.09, 0.03, 0.005, and 0.004 respectively, suggesting a biological effect for each T allele.

Conclusion

Study findings indicate that the T allele of IGF1R variant rs2016347 is associated with a significant reduction in breast cancer risk in women with a history of preeclampsia, most marked for HR+ breast cancer and in women with AFB < 30.