Treatment patterns and survival in metastatic breast cancer patients by tumor characteristics

Hao, Yanbin; Meyer, Nicole; Song, Xue; Shi, Nianwen; Johnson, William M.; Juneau, Paul; Yardley, Denise A.; Rogerio, Jaqueline Willemann

doi:10.1185/03007995.2014.982245

Cited by 6 publications

(2 citation statements)

References 17 publications

(29 reference statements)

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“…There are quite a number of studies exploring risks involved in long term survival of metastatic breast cancer patients ( 7 , 8 ), while few focuses on occurrence of de novo metastatic breast cancer. Therefore, here we attempted to construct population-based models in order to clarify factors concerning occurrence of de novo metastatic breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models

et al. 2019

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Background De novo metastasis of breast cancer is a complex clinical issue to be identified. This study was the first to construct artificial neural networks (ANN) and logistic regression (LR) models with comparison to find out important factors associated with occurrence of de novo metastasis in invasive breast cancer. Methods A total of 40,899 patients diagnosed with de novo metastatic breast cancer in 2010 from Surveillance, Epidemiology and End Results (SEER) Cancer database were enrolled. ANN models and LR models were constructed based on thirteen relevant factors by 10-fold cross-validation approach respectively. Evaluation indexes as well as processing time were compared. Results Overall area under ROC curve (AUC) value of ANN models was significantly higher than that of LR models (0.917±0.01 vs. 0.844±0.011, P<0.001). In ANN models, number of positive ipsilateral axillary lymph nodes, tumor size, lymph node ratio (LNR) and regional lymph nodes status were important associated factors. While under the same experiment environment, ANN models obviously took much more processing time than LR models did (14,400 vs. 15 minutes for 10-fold cross-validation). Conclusions ANN models outperformed traditional LR models in identifying de novo metastasis of breast cancer. On the other hand, the much longer processing time of ANN models should also be considered.

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Section: Introductionmentioning

confidence: 99%

Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models

et al. 2019

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“…In a retrospective observational study, Hao et al [35] analyzed the treatment patterns and clinical outcomes among 18,059 US women with MBC between 2008 and 2011. Of the eligible population, 15% were treated with anti-HER2 agents, 71% were treated with other anticancer drugs, and 14% remained untreated.…”

Section: Loss Of Cancer Patients Per Treatment Linementioning

confidence: 99%

Late Administration of Trastuzumab Emtansine Might Lead to Loss of Chance for Better Outcome in Patients with HER2-Positive Metastatic Breast Cancer

et al. 2018

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The optimal sequence of anti-human epidermal growth factor receptor 2 (HER2) therapies in metastatic breast cancer (MBC) is still undetermined. Physicians must therefore make decisions based on clinical trials and their own experience for the best treatment sequence in these patients. The objective of this review is to summarize the efficacy and safety data for trastuzumab emtansine (T-DM1) in patients with MBC. Additionally, the concept of ‘loss of chance for a better outcome' is investigated. It applies to patients who are not receiving the best possible treatment for their disease. Physicians should strive to offer the best possible care, although getting optimal results in each individual patient is not guaranteed. Lastly, the number of patients with MBC lost per treatment line is evaluated. We conclude that both concepts reinforce the importance of giving the most active treatments as soon as possible in the course of disease to secure the longest possible survival for HER2-positive MBC patients.

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Treatment patterns, survival and economic outcomes in Medicare-enrolled, older patients with HR+/HER2− metastatic breast cancer

Carter

Nagar

et al. 2019

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Treatment patterns and survival in metastatic breast cancer patients by tumor characteristics

Cited by 6 publications

References 17 publications

Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models

Factors associated with de novo metastatic disease in invasive breast cancer: comparison of artificial neural network and logistic regression models

Late Administration of Trastuzumab Emtansine Might Lead to Loss of Chance for Better Outcome in Patients with HER2-Positive Metastatic Breast Cancer

Treatment patterns, survival and economic outcomes in Medicare-enrolled, older patients with HR+/HER2− metastatic breast cancer

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