scholarly journals Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Valérie Bourdès ◽  
Stéphane Bonnevay ◽  
Paolo Lisboa ◽  
Rémy Defrance ◽  
David Pérol ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Disease Free Survival ◽  
Roc Curves ◽  
Disease Recurrence ◽  
Breast Cancer Patients ◽  
Operating Characteristics ◽  
Free Survival ◽  
Breast Cancer Cohort ◽  
Disease Free

The aim of this study was to compare multilayer perceptron neural networks (NNs) with standard logistic regression (LR) to identify key covariates impacting on mortality from cancer causes, disease-free survival (DFS), and disease recurrence using Area Under Receiver-Operating Characteristics (AUROC) in breast cancer patients. From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the study at a single French centre, where they received standard treatment. For specific mortality as well as DFS analysis, the ROC curves were greater with the NN models compared to LR model with better sensitivity and specificity. Four predictive factors were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance of the use of NN models in predictive analysis in oncology, which appeared to be more accurate in prediction in this French breast cancer cohort.

Neutrophil-lymphocyte index as prognostic factor for overall survival and disease-free survival in breast cancer patients

2020 ◽  
Vol 33 (4) ◽  
pp. 137-144
Author(s):  
Guillermo Peralta-Castillo ◽  
Antonio Maffuz-Aziz ◽  
Mariana Sierra-Murguía ◽  
Sergio Rodriguez-Cuevas
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factor ◽  
Cancer Patients ◽  
Disease Free Survival ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Disease Free

scholarly journals Use of Germline Genetic Variability for Prediction of Chemoresistance and Prognosis of Breast Cancer Patients

Cancers ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 511 ◽  
Author(s):  
Viktor Hlavac ◽  
Maria Kovacova ◽  
Katerina Elsnerova ◽  
Veronika Brynychova ◽  
Renata Kozevnikovova ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Disease Free Survival ◽  
Breast Cancer Patients ◽  
Cytotoxic Therapy ◽  
Free Survival ◽  
Testing Phase ◽  
Major Allele Frequency ◽  
Set Up ◽  
Disease Free

The aim of our study was to set up a panel for targeted sequencing of chemoresistance genes and the main transcription factors driving their expression and to evaluate their predictive and prognostic value in breast cancer patients. Coding and regulatory regions of 509 genes, selected from PharmGKB and Phenopedia, were sequenced using massive parallel sequencing in blood DNA from 105 breast cancer patients in the testing phase. In total, 18,245 variants were identified of which 2565 were novel variants (without rs number in dbSNP build 150) in the testing phase. Variants with major allele frequency over 0.05 were further prioritized for validation phase based on a newly developed decision tree. Using emerging in silico tools and pharmacogenomic databases for functional predictions and associations with response to cytotoxic therapy or disease-free survival of patients, 55 putative variants were identified and used for validation in 805 patients with clinical follow up using KASPTM technology. In conclusion, associations of rs2227291, rs2293194, and rs4376673 (located in ATP7A, KCNAB1, and DFFB genes, respectively) with response to neoadjuvant cytotoxic therapy and rs1801160 in DPYD with disease-free survival of patients treated with cytotoxic drugs were validated and should be further functionally characterized.

scholarly journals PIWI-Like 1 and PIWI-Like 2 Expression in Breast Cancer

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2742
Author(s):  
Ramona Erber ◽  
Julia Meyer ◽  
Helge Taubert ◽  
Peter A. Fasching ◽  
Sven Wach ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Disease Free Survival ◽  
Breast Cancer Subtypes ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Cancer Subtypes ◽  
Prognostic Effect ◽  
Enhanced Expression ◽  
Disease Free

PIWI-like 1 and PIWI-like 2 play a role in stem cell self-renewal, and enhanced expression has been reported for several tumor entities. However, few studies have investigated PIWI-like 1 and PIWI-like 2 expressions in breast cancer subtypes regarding prognosis. Therefore, we examined protein expression in a large consecutive cohort of breast cancer patients and correlated it to breast cancer subtypes and survival outcome. PIWI-like 1 and PIWI-like 2 expressions were evaluated using immunohistochemistry in a cohort of 894 breast cancer patients, of whom 363 were eligible for further analysis. Percentage and intensity of stained tumor cells were analyzed and an immunoreactive score (IRS) was calculated. The interaction of PIWI-like 1 and PIWI-like 2 showed a prognostic effect on survival. For the combination of high PIWI-like 1 and low PIWI-like 2 expressions, adjusted hazard ratios (HRs) were significantly higher with regard to overall survival (OS) (HR 2.92; 95% confidence interval (CI) 1.24, 6.90), disease-free survival (DFS) (HR 3.27; 95% CI 1.48, 7.20), and distant disease-free survival (DDFS) (HR 7.64; 95% CI 2.35, 24.82). Both proteins were significantly associated with molecular-like and PAM50 subgroups. Combining high PIWI-like 1 and low PIWI-like 2 expressions predicted poorer prognosis and both markers were associated with aggressive molecular subtypes.

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