A pre-operative prognostic model predicting all cause and cause specific mortality for women presenting with invasive breast cancer

Abstract

PurposeThe aim of this study is to develop a pre-operative prognostic model based on known pre-operative factors. MethodsA database of ultrasound (US) lesions undergoing biopsy documented US lesion size, stiffness, and patient source prospectively. Women with invasive cancer presenting between 2010 and 2015 were the study group. Breast and axillary core results and ER, PR and HER receptor status were collected prospectively. Assessment of US skin thickening, US distal enhancement and presence of chronic kidney disease (CKD) was performed retrospectively. Patient survival and cause of death were ascertained from computer records. Predictive models for (i) all-cause mortality (ACM) and (ii) breast cancer death (BCD) were built and then validated using bootstrap k-fold cross-validation. A comparison of predictive performance was made between a full cause-specific Cox model, a sub cause-specific Cox model, and a full Fine-Gray sub-distribution hazard model. Results1136 patients were included in the study. The median follow-up time was 6.2 years. 125 (11%) women died from breast cancer and 155 (14%) died from other causes. For the prediction of BCD, the cause-specific Cox sub-model performed the best. The time dependent AUC begins above 0.91 in year one to 3 reducing to 0.83 in year 6. The factors included in the Cox sub model were tumour size, skin thickening, source of detection, tumour grade, ER status, pre-operative nodal metastasis and CKD. ConclusionWe have shown that a model based on preoperative factors can predict BCD. Such prediction if externally validated and incorporating treatment data could be useful for treatment planning and patient counselling.