Association between insurance status at diagnosis and survival among patients with de novo metastatic breast cancer: a population-based study

The Breast ◽  
2019 ◽  
Vol 44 ◽  
pp. S96
Author(s):  
P. Ji ◽  
Y. Gong ◽  
X. Hu ◽  
G.-H. Di ◽  
Z.-M. Shao
2017 ◽  
Vol 22 (4) ◽  
pp. 386-393 ◽  
Author(s):  
Ines Vaz‐Luis ◽  
Nancy U. Lin ◽  
Nancy L. Keating ◽  
William T. Barry ◽  
Eric P. Winer ◽  
...  

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 1125-1125 ◽  
Author(s):  
L. Cortesi ◽  
C. Cirilli ◽  
I. Rashid ◽  
E. Artioli ◽  
M. Federico

1125 Background: A significant improvement in overall survival was observed in the last two decades in patients with breast cancer due to early diagnosis and more effective therapies. However, a significant improvement in metastatic setting has been questioned. Our population based study was aimed to investigate the outcome of metastatic breast cancer from 1988 to 2005. Methods: Women with stage IV de novo or relapsed breast cancer diagnosed between 1988 and 2005 were identified by the Modena Cancer Registry (MCR). For all patients overall survival (OS) was measured from the date of first distant metastases to the date of death from any cause or last follow-up and compared across groups for four periods of similar duration time: 1988–1993 (A), 1994–1997(B), 1998–2001(C), 2002–2005(D). Results: Among 8,654 patients with breast cancer identified by the MCR, 409 had an initial metastatic disease (4.8%) and 693 (8.4%) had a distant recurrence. Median age at onset was 66 versus 59 years in de novo vs relapsed disease (p = 0.001). Significant differences for postmenopausal age (80% vs 71%) and for positive estrogen receptors (72% vs 63%) were registered in de novo and relapsed disease, respectively (p = 0.001). After a 27 months median follow-up for initial metastatic disease, the five-year OS was 12%, 14%, 9%, and 13% in the A, B, C, and D periods, respectively, (p = 0.5). Conversely, in relapsed breast cancer, after a 29 months median follow-up, a significant survival improvement was observed between the first and the other three periods, being the 5 year-survival rate after recurrence 10%, 22%, 30%, and 25%, respectively (p = 0.001). A survival improvement was seen in the last ten years for relapsed breast cancer using aromatase inhibitors (p < 0.0001) while for initial metastatic disease the same treatment provided a better outcome only in the last 4 years (p < 0.0001). Conclusions: Data from our study show that the outcome of initial metastatic breast cancer is still discouraging, despite the availability of several new drugs in recent years. A limited improvement was observed in relapsed breast cancer with the aromatase inhibitors introduction. In any case, the finish line is still far away, and robust investments in basic and translational research are still absolutely necessary. No significant financial relationships to disclose.


2018 ◽  
Vol Volume 10 ◽  
pp. 287-295 ◽  
Author(s):  
Zhenchong Xiong ◽  
Guangzheng Deng ◽  
Xinjian Huang ◽  
Xing Li ◽  
Xinhua Xie ◽  
...  

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 528-537 ◽  
Author(s):  
Albee Y Ling ◽  
Allison W Kurian ◽  
Jennifer L Caswell-Jin ◽  
George W Sledge ◽  
Nigam H Shah ◽  
...  

Abstract Objectives Most population-based cancer databases lack information on metastatic recurrence. Electronic medical records (EMR) and cancer registries contain complementary information on cancer diagnosis, treatment and outcome, yet are rarely used synergistically. To construct a cohort of metastatic breast cancer (MBC) patients, we applied natural language processing techniques within a semisupervised machine learning framework to linked EMR-California Cancer Registry (CCR) data. Materials and Methods We studied all female patients treated at Stanford Health Care with an incident breast cancer diagnosis from 2000 to 2014. Our database consisted of structured fields and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results Program (SEER). We identified de novo MBC patients from CCR and extracted information on distant recurrences from patient notes in EMR. Furthermore, we trained a regularized logistic regression model for recurrent MBC classification and evaluated its performance on a gold standard set of 146 patients. Results There were 11 459 breast cancer patients in total and the median follow-up time was 96.3 months. We identified 1886 MBC patients, 512 (27.1%) of whom were de novo MBC patients and 1374 (72.9%) were recurrent MBC patients. Our final MBC classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.917, with sensitivity 0.861, specificity 0.878, and accuracy 0.870. Discussion and Conclusion To enable population-based research on MBC, we developed a framework for retrospective case detection combining EMR and CCR data. Our classifier achieved good AUC, sensitivity, and specificity without expert-labeled examples.


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