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Article Abstract
Objectives: The present study aimed to develop and validate nomograms to predict the survival of patients with breast invasive micropapillary carcinoma (IMPC) to aid objective decision-making.
Design: Prognostic factors were identified using Cox proportional hazards regression analyses and used to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) at 3 and 5 years. Kaplan-Meier analysis, calibration curves, the area under the curve (AUC) and the concordance index (C-index) evaluated the nomograms' performance. Decision curve analysis (DCA), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system.
Setting: Patient data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. This database holds data related to the incidence of cancer acquired from 18 population-based cancer registries in the US.
Participants: We ruled out 1893 patients and allowed the incorporation of 1340 patients into the present study.
Results: The C-index of the AJCC8 stage was lower than that of the OS nomogram (0.670 vs 0.766) and the OS nomograms had higher AUCs than the AJCC8 stage (3 years: 0.839 vs 0.735, 5 years: 0.787 vs 0.658). On calibration plots, the predicted and actual outcomes agreed well, and DCA revealed that the nomograms had better clinical utility compared with the conventional prognosis tool. In the training cohort, the NRI for OS was 0.227, and for BCSS was 0.182, while the IDI for OS was 0.070, and for BCSS was 0.078 (both p<0.001), confirming its accuracy. The Kaplan-Meier curves for nomogram-based risk stratification showed significant differences (p<0.001).
Conclusions: The nomograms showed excellent discrimination and clinical utility to predict OS and BCSS at 3 and 5 years, and could identify high-risk patients, thus providing IMPC patients with personalised treatment strategies.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944677 | PMC |
| http://dx.doi.org/10.1136/bmjopen-2022-065312 | DOI Listing |
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