Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

BACKGROUND: In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence). METHODS: An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model. RESULTS: For recurrence  5 years, stage 2 cancer (OR 1.67, 95% CI = 1.31-2.14) and radiotherapy+mastectomy (OR 2.45, 95% CI = 1.81-3.32) were significant predictors; furthermore, relative to mastectomy without radiotherapy (as reference for comparison), quadranectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence > 5 years (OR 7.62, 95% CI = 1.52-38.15 vs. OR 1.75, 95% CI = 1.32-2.32). Accuracy, sensitivity, and specificity of the model were 71%, 78.8%, and 55.8%, respectively. CONCLUSION: For the first time, we constructed models for estimating recurrence based on timing of recurrence which are among the most applicable models with excellent accuracy (> 80%)

Introducing novel and comprehensive models for predicting recurrence in breast cancer using the group LASSO approach: are estimates of early and late recurrence different?

Abstract Background In here, we constructed personalized models for predicting breast cancer (BC) recurrence according to timing of recurrence (as early and late recurrence). Methods An efficient algorithm called group LASSO was used for simultaneous variable selection and risk factor prediction in a logistic regression model. Results For recurrence  5 years, stage 2 cancer (OR 1.67, 95% CI = 1.31–2.14) and radiotherapy+mastectomy (OR 2.45, 95% CI = 1.81–3.32) were significant predictors; furthermore, relative to mastectomy without radiotherapy (as reference for comparison), quadranectomy without radiotherapy had a noticeably higher odds ratio compared to quadranectomy with radiotherapy for recurrence > 5 years (OR 7.62, 95% CI = 1.52–38.15 vs. OR 1.75, 95% CI = 1.32–2.32). Accuracy, sensitivity, and specificity of the model were 71%, 78.8%, and 55.8%, respectively. Conclusion For the first time, we constructed models for estimating recurrence based on timing of recurrence which are among the most applicable models with excellent accuracy (> 80%)