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

The Breast ◽  
2022 ◽  
Vol 61 ◽  
pp. 11-21
Author(s):  
Huan Wang ◽  
Peter Donnan ◽  
E. Jane Macaskill ◽  
Lee Jordan ◽  
Alastair Thompson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Prognostic Model ◽  
Specific Mortality

scholarly journals Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study (Preprint)

2020 ◽  
Author(s):  
Xiaorong Zhong ◽  
Ting Luo ◽  
Ling Deng ◽  
Pei Liu ◽  
Kejia Hu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Disease Progression ◽  
Invasive Breast Cancer ◽  
Prognostic Model ◽  
The United States ◽  
Biomedical Ethics ◽  
Extreme Gradient Boosting ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Cancer Specific Mortality

BACKGROUND Current online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. OBJECTIVE We aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. METHODS This study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. RESULTS The training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values. CONCLUSIONS Our prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.

scholarly journals Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

10.2196/19069 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e19069
Author(s):  
Xiaorong Zhong ◽  
Ting Luo ◽  
Ling Deng ◽  
Pei Liu ◽  
Kejia Hu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Disease Progression ◽  
Invasive Breast Cancer ◽  
Prognostic Model ◽  
The United States ◽  
Biomedical Ethics ◽  
Extreme Gradient Boosting ◽  
All Cause Mortality ◽  
Specific Mortality ◽  
Cancer Specific Mortality

Background Current online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions. Objective We aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China. Methods This study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT. Results The training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values. Conclusions Our prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.

scholarly journals PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer

2010 ◽  
Vol 12 (1) ◽  
Author(s):  
Gordon C Wishart ◽  
Elizabeth M Azzato ◽  
David C Greenberg ◽  
Jem Rashbass ◽  
Olive Kearins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Prognostic Model

scholarly journals Erratum to: PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer

2010 ◽  
Vol 12 (2) ◽  
Author(s):  
Gordon C Wishart ◽  
Elizabeth M Azzato ◽  
David C Greenberg ◽  
Jem Rashbass ◽  
Olive Kearins ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Prognostic Model

The performance of the 21-gene assay standard cutpoints of 18 and 31 in HR+, HER2- invasive breast cancer (BC), while waiting for TAILORx mid-range recurrence score results.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 537-537 ◽  
Author(s):  
Dave P. Miller ◽  
Valentina I. Petkov ◽  
Steven Shak
Keyword(s):  
Breast Cancer ◽  
Clinical Practice ◽  
Hormonal Therapy ◽  
Invasive Breast Cancer ◽  
Recurrence Score ◽  
Population Based ◽  
Additional Benefit ◽  
Gene Assay ◽  
Specific Mortality ◽  
Prior Malignancy

537 Background: The Recurrence Score (RS) was shown in NSABP B-20 to predict chemotherapy (CT) benefit for RS ≥31 and no CT benefit for RS <18. The TAILORx results for RS <11 (NEJM 2015) reported excellent outcomes with no opportunity for CT to add additional benefit. As we await TAILORx results for RS 11-25, we characterized BC specific mortality (BCSM) for RS groups (cutoffs of 11, 18, 25, and 31) in the population-based SEER study of pts treated based on RS. Methods: RS results were provided to SEER registries per their methods (npj Breast Cancer 2016). Pts diagnosed (Jan 2004 - Dec 2012) with N0 HR+ HER2- negative BC, and no prior malignancy were eligible. BCSM estimates by CT use were computed using standard cutpoints of 18 and 31 and TAILORx cutpoints of 11 and 25. Results: Among 49,681 with a RS, 9,486 (19%) had RS <11, 17,988 (36%) had RS 11-17, 14,541 (29%) had RS 18-25, 3,805 (8%) had RS 26-30, and 3,861 (8%) had RS ≥31. Reported CT use and 5-y BCSM increased with increasing RS. For pts with both RS <11 and RS 11-17, CT use was uncommon and 5-y BCSM was low regardless of CT use. For pts with RS 18-25, CT use was more common and the 5-y BCSM was about 1% regardless of CT use. For pts with RS of 26-30 or ≥31, CT was common, and lower 5-y BCSM was observed with CT reported yes than with CT reported no or unknown. Conclusions: Pts in real-world clinical practice with RS <11, consistent with TAILORx, and pts with RS 11-17 have low 5-y BCSM with limited CT use, supporting hormonal therapy alone for pts with RS <18. The high end of the TAILORx mid-range (18-25) also showed good 5-y BCSM both with and without CT, highlighting the importance of the randomized results of TAILORx. [Table: see text]

Breast cancer specific mortality in patients with early-stage hormone receptor–positive invasive breast cancer and oncotype DX recurrence score results in the SEER database.

2016 ◽  
Vol 34 (7_suppl) ◽  
pp. 176-176 ◽  
Author(s):  
Steven Shak ◽  
Valentina Petkov ◽  
Dave P Miller ◽  
Nadia Howlader ◽  
Nathan Gliner ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Hormone Receptor ◽  
Early Stage ◽  
Recurrence Score ◽  
Oncotype Dx ◽  
Large Sample Size ◽  
Breast Cancer Specific Mortality ◽  
Specific Mortality ◽  
Cancer Specific Mortality

176 Background: NCI’s SEER Program provides cancer incidence and survival statistics for ~28% of the US. New research models are needed to characterize the use and impact of genomic tests on patient outcomes. Genomic Health and SEER collaborated to electronically supplement SEER registries with Recurrence Score (RS) results, and have evaluated breast cancer specific mortality (BCSM) in early stage hormone receptor (HR)+ HER2- invasive breast cancer. Methods: Pts were eligible for pre-specified node negative (N-) disease analysis if HR+, HER2- (by RT-PCR), no prior malignancy, 40-85 years of age, and diagnosed between Jan 2004 (Oncotype DX available Jan 2004) and Dec 2011 (SEER survival analysis complete through 2012). BCSM was defined as previously described (Howlader et al, JNCI 2010). Additional analyses of BCSM were performed for pts with N+ disease. Results: Of 169,158 eligible N- pts, 38,568 (23%) had a RS, increasing from 2% in 2004 to 35% in 2011. Pts with RS had median age of 57yr, were 99.4% female, 84% white, 29% grade 1 & 54% grade 2, 25% < 1cm & 53% 1-2cm. Median FU was 39mo. 8,239 pts had > 5yrs follow-up. Among RS < 18 (N = 21,023), RS 18-30 (N = 14,494) and RS ≥ 31 (N = 3,051) pts, chemotherapy use was reported in 7%, 34%, & 69%, respectively, and 5yr N- BCSM was 0.4% (95% CI, 0.3-0.6), 1.4% (95% CI, 1.1-1.7) and 4.4% (95% CI,3.4-5.6), respectively. Multivariate showed that RS was significantly associated with BCSM after adjusting for age, grade, and tumor size (p < 0.001), and when stratified by treatment (p < 0.001). BCSM results in additional N- subgroups (e.g., socioeconomic), and in > 60,000 N+ pts will be presented. Conclusions: 5yr survival outcomes are excellent in the over 21,000 N- pts with RS < 18 disease. RS ≥ 31 disease is associated with greater 5yr mortality despite addition of chemotherapy. The large sample size of this population-based observational study provides important information on prospective outcomes in subsets of pts that are often underrepresented in randomized clinical trials.

Prior DCIS and overall mortality in women with stage I breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12593-e12593
Author(s):  
Rebecca A. Nelson ◽  
Lily L. Lai ◽  
Joanne E. Mortimer ◽  
Enrique Soto Perez De Celis ◽  
Rowan T. Chlebowski ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Invasive Breast Cancer ◽  
Tumor Grade ◽  
Breast Cancer Diagnosis ◽  
Stage I ◽  
Factors Associated ◽  
Breast Cancer Specific Mortality ◽  
Specific Mortality ◽  
Cancer Specific Mortality ◽  
Overall Mortality

e12593 Background: Whether a prior diagnosis of ductal carcinoma in situ (DCIS) impacts women later diagnosed with invasive breast cancer is unclear. If localized breast cancer following DCIS is more aggressive than localized breast cancer alone, this could inform therapy decisions. To our knowledge, no study has examined the impact of prior DCIS on overall mortality in women with stage I invasive breast cancer. The study objective was to determine if overall mortality for women with stage I breast cancer with prior DCIS is different from those with stage I disease without prior DCIS. Our hypothesis was that women with prior DCIS would have higher mortality compared to those without prior DCIS. Methods: 302,484 patients with stage I cancer diagnosed from 1998 to 2016 were ascertained from SEER. Of these, 5,011 (1.7%) had prior DCIS. Patients with DCIS were matched 1:2 to women with no prior DCIS based on age, year of diagnosis, race/ethnicity, marital status, and invasive breast cancer characteristics including histology, tumor grade, tumor size, T stage, N stage, ER/PR status, surgery type, radiation, and chemotherapy status. The primary study outcome was overall mortality. Cox proportional hazards models were used to compute hazard ratios (HR) and 95% confidence intervals (CI). Results: Cases and controls had similar demographics. Compared to women with stage I breast cancer without prior DCIS, overall mortality was statistically significantly lower in women with stage I breast cancer with prior DCIS (hazard ratio [HR] 0.89 95% confidence interval [CI]0.80-0.98). Other factors associated with overall mortality were bilateral mastectomy (adjusted HR: 0.62; 95% CI: 0.49-0.78), radiation therapy (adjusted HR: 0.64; 95% CI: 0.56-0.75) and chemotherapy (adjusted HR: 0.85; 95% CI: 0.72-0.99). Factors associated with higher overall mortality included age (trend p < 0.001), tumor grade (trend p = 0.003), and negative PR receptor status (adjusted HR: 1.29; 95% CI: 1.13-1.45). Breast cancer specific mortality, however, was statistically significantly higher in women with prior DCIS to their breast cancer diagnosis compared to women without prior DCIS to their breast cancer diagnosis (HR 1.24 95% CI 1.01-1.52). Conclusions: Contrary to our hypothesis, women with prior DCIS and subsequent stage I breast cancer have lower overall mortality compared to matched controls with stage I breast cancer without prior DCIS. In contrast, those with prior DCIS have higher breast cancer specific mortality than those without prior DCIS. Reasons for this discrepancy are unknown, but since DCIS is most commonly diagnosed on mammogram, differences may be related to sociodemographic characteristics that are associated with both higher screening adherence and higher overall survival, such as higher income, higher education achievement , and higher access to health care.

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