Palliative Care in the Elderly Breast Cancer Patient

15 Background: Nodal evaluation of the elderly breast cancer patient remains controversial, and some have suggested that selected older women with breast cancer may not require sentinel lymph node biopsy (SLNB). Methods: An IRB-approved database was queried for patients undergoing SLNB for invasive breast cancer from 2000-2006. We compared 8 cohorts: age <40 years, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, and >70 years. Logistic regression and chi-square test were used. Results: Procedure success rate was above 95% for all groups in a total sample size of 1268 patients. Patients >70 years had lower grade tumors than patients <40 years (Grade 1: 25% vs. 7%; Grade 2: 53% vs. 47%; Grade 3: 17% vs. 40%, p<0.0001) and higher ER expression (ER+: 83% vs. 59%, p<0.0005). Patients <40 years also had a higher proportion of multifocal disease (21% vs. 9%, p<0.002), lymphovascular invasion (20% vs. 10%, p<0.007), and number of positive sentinel lymph nodes (PSLN) removed (mean: 3.7 vs. 2.7, p<0.028). Upon multivariate analysis, the odds of a PSLN decrease 9% for every 5-year increase in age (OR 0.91, p<0.003), but increase significantly with certain tumor characteristics (ER+ vs. ER-: OR 1.7, p=0.002), larger size (0.5 cm increase: OR 1.26, p<0.0001), and higher grade (Grades 2-3: OR 1.99, p<0.0007). The predicted probability of a PSLN for patients age 35, 55, and 70 years is 27%, 22%, and 16%, assuming each had a ER+, low grade, 2 cm tumor. Conclusions: Older breast cancer patients have more favorable pathology, and the chance of a PSLN decreases as age increases. However, the odds of a PSLN are significantly higher in patients with certain tumor characteristics, which are known prior to definitive surgery. Given recent reports that older patients are less likely to receive standard treatment for breast cancer and prognosis may worsen as a result, tumor size and characteristics rather than age should dictate the decision to perform SLNB, and we should continue appropriate, aggressive staging of the older breast cancer patient.

Download Full-text

THE RELATION OF COPPING STRATEGY BY LOOKING SOCIAL SUPPORT WITH STRESS LEVEL OF BREAST CANCER PATIENT WHO GONE TROUGH CHEMOTHERAPY

Elevate The International Journal of Nursing Education Practice and Research ◽

10.25077/elevate.1.1.51-54.2018 ◽

2018 ◽

Vol 1 (1) ◽

pp. 51-54

Author(s):

Sri Burhani Putri

Keyword(s):

Breast Cancer ◽

Social Support ◽

Cancer Patient ◽

Breast Cancer Patient ◽

Cross Sectional Study ◽

P Value ◽

Spiritual Support ◽

Cross Sectional ◽

Breast Cancer Chemotherapy ◽

Patient Stress

Breast cancer is one of the most common illness that killed woman. One of the therapy to cure breast cancer is chemotherapy. Chemotherapy has side effect either physical and psychology, that caused people who’s in chemo therapy, prone to stress. Stress effected by many factors, such as characteristic and chopping strategy that patient has been using. The aim of this research is to get a perspective about the relation of characteristic and chopping strategy with breast cancer patient stress, whose in chemo therapy. This research using cross sectional study and taking sample by using accidental sampling method. The data analyzed by using bavariat and multivariat with variable result shows that breast cancer patient stress who has chemo therapy realted to age characteristic (p value = 0.00) the time since they diagnosed with cancer (pvalue = 0.03), how long they have chemo therapy (pvalue = 0.00) and chopping strategyby looking social support (pvalue = 0.00) looking for spiritual (pvalue = 0.00) with dominan variable which related to stress is chopping strategy to looking spiritual support (coeffecients B = -1.139). Key words : Breast cancer, chemotherapy, stress

Download Full-text

Metastatic Breast Cancer: Patient Journey, Patient Needs, and Expectations: Results of the RÉALITÉS National Survey

ONCOLOGIE ◽

10.3166/onco-2018-0013 ◽

2018 ◽

Vol 20 (3-4) ◽

pp. 73-83 ◽

Cited By ~ 3

Author(s):

M. Espié ◽

I. Moley-Massol ◽

N. Zernik ◽

D. Debiais ◽

V. Diéras

Keyword(s):

Breast Cancer ◽

Metastatic Breast Cancer ◽

Cancer Patient ◽

Breast Cancer Patient ◽

National Survey ◽

Metastatic Breast ◽

Metastatic Breast Cancer Patient ◽

Patient Needs ◽

Patient Journey

Download Full-text

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

Scientific Reports ◽

10.1038/s41598-021-94862-6 ◽

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.

Download Full-text