scholarly journals Predictive Factors of Survival Time of Breast Cancer in Kurdistan Province of Iran between 2006-2014: A Cox Regression Approach

2014 ◽  
Vol 15 (19) ◽  
pp. 8483-8488 ◽  
Author(s):  
Asrin Karimi ◽  
Ali Delpisheh ◽  
Kourosh Sayehmiri ◽  
Hojjatollah Saboori ◽  
Ezzatollah Rahimi
Keyword(s):  
Breast Cancer ◽  
Survival Time ◽  
Predictive Factors ◽  
Cox Regression ◽  
Regression Approach

scholarly journals Survival Analysis: A Hospital Based Retrospective Life Span Study of Breast Cancer Patients after First Recurrence

2020 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Madiha Liaqat ◽  
◽  
Waqas Fazil ◽  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Life Span ◽  
Survival Time ◽  
Cancer Patients ◽  
Cox Regression ◽  
Molecular Subtype ◽  
Published Data ◽  
Lower Grade ◽  
Breast Cancer Patients

Background Overall survival of breast cancer patients has been calculated many times but there is no precise research available regarding the survival time of breast cancer patients after recurrence. We investigated the effects factors on mortality due to breast cancer. Methods All Factors were analyzed using statistical tools and techniques to find out rate of mortality after recurrence. Descriptive statistics, cox proportional hazard models were used to find statistical significant variables. In the present study recurrence is considered as an important event which may play a role in study of breast cancer progression. In this study, we evaluated breast cancer risk factors in relation to mortality due to this disease among 1028 women with breast cancer in Lahore, Pakistan. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between risk factors and mortality due to breast cancer were estimated in subtype-specific Cox regression models. Results Survival of breast cancer patients depends upon many factors. A total of 581 alive and 447 deaths due to breast cancer occurred during a median follow-up period of 1977 days. Median survival time after recurrence was 3 years. Significant factors were included post- menopausal women who diagnosed and had recurrence at the age < 45 of molecular subtype estrogen receptor positive, progesterone receptor negative, Her2.neu positive with tumor size ≥ 3 & involved lymph nodes >5. Radiotherapy has increased life span of patients even after recurrence. Conclusion Younger women had higher risk of mortality after recurrence even gone through chemotherapy while lower grade tumor had good prognosis. Radiotherapy played a major role in increasing life time of breast cancer women after recurrence. Our findings are consistent with those from previously published data.

scholarly journals Clinicopathological Characteristics and Treatment Strategies of Triple-Negative Breast Cancer Patients With a Survival Longer than 5 Years

2021 ◽  
Vol 10 ◽  
Author(s):  
Ning Xie ◽  
Ying Xu ◽  
Ying Zhong ◽  
Junwei Li ◽  
Herui Yao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Survival Time ◽  
Triple Negative Breast Cancer ◽  
Tumor Size ◽  
Triple Negative ◽  
Cox Regression ◽  
Propensity Score Analysis ◽  
Term Survival ◽  
Cox Regression Analysis

PurposeTriple-negative breast cancer (TNBC) is characterized by high malignancy and a poor prognosis. Patients with TNBC who survive longer than 5 years represent a unique portion of the population. This study aimed to analyze the clinicopathological features, explore prognostic factors, and evaluate treatment options for these patients.MethodsA total of 24,943 TNBC patients were enrolled from the national Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2016. The patients were divided into three groups: group 1, survival time &lt;3 years; group 2, 3–5 years; and group 3, survival time ≥5 years. The overall survival (OS) and breast cancer cause-specific survival (BCSS) were primarily assessed in this study. A propensity score analysis was used to avoid bias caused by the data selection criteria. We used a Cox hazard ratio analysis to determine prognostic factors, which were selected as nomogram parameters to develop a model for predicting patient survival.ResultsPatients who survived longer than 5 years were more likely to be younger than 55 years, Caucasian, and exhibit a lower AJCC stage, N stage, distant metastasis, lymph node (LN) involvement, and tumor size than those with a shorter survival time (p &lt; 0.05). The multivariable Cox regression analysis showed that age, race, tumor size, LN status, and chemotherapy were independent prognostic factors. Subgroup analyses for patients with tumors ≤20 mm displayed a superior OS and BCSS for breast-conserving surgery (BCS) not treated with a mastectomy. BCS provided at least an equivalent prognosis to a mastectomy in patients with tumors larger than 20 mm. A nomogram with a C-index of 0.776 (95% confidence interval: 0.767–0.785) was developed to predict the 3- and 5-year survival probability for the patients with TNBC.ConclusionA localized surgical approach may represent a superior choice for TNBC patients with a survival time longer than 5 years. Our study indicated that age, race, tumor size, LN status, and chemotherapy were independent prognostic factors. A prognostic nomogram directly quantified patient risk and was better able to predict long-term survival in TNBC patients.

Prognostic facctor for early versus late relapse in hormonodependant non metastatic breast cancers treated by tamoxifen: PROFARE study results from GETNA group

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 10585-10585
Author(s):  
D. Azria ◽  
M. Tubiana-Hulin ◽  
M. Spielmann ◽  
B. Coudert ◽  
A. Monnier ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Survival Time ◽  
Cox Regression ◽  
Late Relapse ◽  
Lower Quartile ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Early Relapse

10585 Background: The purpose of this study was to identify potential prognostic factors of early relapse among postmenopausal women with ER+ breast cancer treated at least by adjuvant tamoxifen therapy. Methods: This was a multi-center, retrospective study. Data was collected from 1588 subjects on demographic and subject characteristics and the subjects’ course of the disease using information from patient files. It was planned to have data to cover a follow-up period of ten years. Frequency distributions of the pre-specified potential prognostic factors were presented for subjects with relapse or death (RoD) below 3 years (early), RoD after at least 3 years (late) and without RoD recorded. Results: There were 1550 subjects included in the analysis population. The median age was 60 years [52–67]. There were 633 patients (40.8%) with an event for RoD. Median relapse-free survival time (RFS) was 165 months (no confidence interval calculation possible due to low event rate) and lower quartile relapse-free survival time was 68 months [95% CI, 59–76]. Concerning RFS 212 (13.7%) subjects had an early relapse and 421 (31.5%) had a late relapse. Out of 1550 subjects analyzed for overall survival, there were 344 (22.2%) with an event. Median survival time was not reached; lower quartile survival time was 143 months [131–176 months]. The Cox regression for RFS showed that prognostic factors for RFS overall and for early RFS seems to have similar patterns. Prognostic factors for late RFS differed from those for RFS overall and early RFS. The Cox regression for overall survival showed similar results. SBR grade 1 or 2 vs. 3 (p < 0.0001), <4 vs. ≥4 lymph nodes involved (p < 0.0001), and tumor size pT0–1 vs pT2–4 (p = 0.0001) are selected in both models as prognostic factors. These factors are also of prognostic value for RFS. Conclusions: Women with hormonodependant breast cancer who are at high risk of early relapse while on tamoxifen can potentially be identified. These data could maybe be of importance concerning the choice of the upfront hormonal regimen in menopaused women treated for hormonodependant breast cancer. No significant financial relationships to disclose.

A novel three-long noncoding RNA risk score system for the prognostic prediction of triple-negative breast cancer

2021 ◽  
Vol 15 (1) ◽  
pp. 43-55
Author(s):  
Chao Yuan ◽  
Hongjun Yuan ◽  
Li Chen ◽  
Miaomiao Sheng ◽  
Wenru Tang
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Triple Negative Breast Cancer ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Triple Negative ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Gene Expression Omnibus ◽  
Prognostic Prediction

Background: Triple-negative breast cancer (TNBC) is characterized by fast tumor increase, rapid recurrence and natural metastasis. We aimed to identify a genetic signature for predicting the prognosis of TNBC. Materials & methods: We conducted a weighted correlation network analysis of datasets from the Gene Expression Omnibus. Multivariate Cox regression was used to construct a risk score model. Results: The multi-factor risk scoring model was meaningfully associated with the prognosis of patients with TBNC. The predictive power of the model was demonstrated by the time-dependent receiver operating characteristic curve and Kaplan–Meier curve, and verified using a validation set. Conclusion: We established a long noncoding RNA-based model for the prognostic prediction of TNBC.

scholarly journals Tumor collagen framework from bright-field histology images predicts overall survival of breast carcinoma patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mindaugas Morkunas ◽  
Dovile Zilenaite ◽  
Aida Laurinaviciene ◽  
Povilas Treigys ◽  
Arvydas Laurinavicius
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Cancer Patient ◽  
Breast Cancer Patient ◽  
Cox Regression ◽  
Regional Lymph Node ◽  
Histological Grade ◽  
Biological Information ◽  
Lymph Node Status ◽  
Bright Field

AbstractWithin the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value < 0.05) superior to conventional clinical parameters (size of the primary tumor (T), regional lymph node status (N), histological grade (G), and patient age). Additionally, we noted statistically significant differences of collagen features between tumor grade groups, and the factor analysis revealed features resembling the TACS concept. Our proposed method offers collagen framework segmentation from bright-field histology images and provides novel image-based features for better breast cancer patient prognostication.

scholarly journals Survival after surgery among patients with cholangiocarcinoma in Northeast Thailand according to anatomical and morphological classification

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chaiwat Tawarungruang ◽  
Narong Khuntikeo ◽  
Nittaya Chamadol ◽  
Vallop Laopaiboon ◽  
Jaruwan Thuanman ◽  
...  
Keyword(s):  
Survival Rate ◽  
Survival Time ◽  
Median Survival ◽  
Median Survival Time ◽  
Cox Regression ◽  
Survival Rates ◽  
Care Program ◽  
Tumor Location ◽  
Northeast Thailand ◽  
Curative Surgery

Abstract Background Cholangiocarcinoma (CCA) has been categorized based on tumor location as intrahepatic (ICCA), perihilar (PCCA) or distal (DCCA), and based on the morphology of the tumor of the bile duct as mass forming (MF), periductal infiltrating (PI) or intraductal (ID). To date, there is limited evidence available regarding the survival of CCA among these different anatomical and morphological classifications. This study aimed to evaluate the survival rate and median survival time after curative surgery among CCA patients according to their anatomical and morphological classifications, and to determine the association between these classifications and survival. Methods This study included CCA patients who underwent curative surgery from the Cholangiocarcinoma Screening and Care Program (CASCAP), Northeast Thailand. The anatomical and morphological classifications were based on pathological findings after surgery. Survival rates of CCA and median survival time since the date of CCA surgery and 95% confidence intervals (CI) were calculated. Multiple cox regression was performed to evaluate factors associated with survival which were quantified by hazard ratios (HR) and their 95% CIs. Results Of the 746 CCA patients, 514 had died at the completion of the study which constituted 15,643.6 person-months of data recordings. The incidence rate was 3.3 per 100 patients per month (95% CI: 3.0–3.6), with median survival time of 17.8 months (95% CI: 15.4–20.2), and 5-year survival rate of 24.6% (95% CI: 20.7–28.6). The longest median survival time was 21.8 months (95% CI: 16.3–27.3) while the highest 5-year survival rate of 34.8% (95% CI: 23.8–46.0) occurred in the DCCA group. A combination of anatomical and morphological classifications, PCCA+ID, was associated with the longest median survival time of 40.5 months (95% CI: 17.9–63.0) and the highest 5-year survival rate of 42.6% (95% CI: 25.4–58.9). The ICCA+MF combination was associated with survival (adjusted HR: 1.45; 95% CI: 1.01–2.09; P = 0.013) compared to ICCA+ID patients. Conclusions Among patients receiving surgical treatment, those with PCCA+ID had the highest 5-year survival rate, which was higher than in groups classified by only anatomical characteristics. Additionally, the patients with ICCA+MF tended to have unfavorable surgical outcomes. Showed the highest survival association. Therefore, further investigations into CCA imaging should focus on patients with a combination of anatomical and morphological classifications.

scholarly journals Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Feng Jiang ◽  
Chuyan Wu ◽  
Ming Wang ◽  
Ke Wei ◽  
Jimei Wang
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Single Gene ◽  
Prediction Method ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Models ◽  
Normal Tissues ◽  
Predictive Risk

AbstractOne of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.

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