scholarly journals Mothering Death: A Psychosocial Interpretation of Breast Cancer Biography

2021 ◽  
Vol 6 (1) ◽  
pp. 38-67
Author(s):  
Birgitta Haga Gripsrud
Keyword(s):  
Breast Cancer ◽  
Expressive Writing ◽  
Single Case ◽  
Breast Cancer Diagnosis ◽  
Data Interpretation ◽  
Data Set ◽  
Interpretive Study ◽  
Psychosocial Studies ◽  
Field Notes ◽  
Point Of Departure

In this article I take as my point of departure a puzzle presented by a woman who had an apparently ‘bizarre’ reaction to a breast cancer diagnosis. In the clinic, she had exclaimed: “I would rather die than lose the breast!”. My aim is to unpack layers in this woman’s embodied and enculturated experience, with a view towards developing a psychosocial interpretation of breast cancer biography. The single case on which the present study is based, was extracted from a larger longitudinal data set which allowed me to follow ‘Ella’s’ transition from diagnosis to survivorship. I relied on five sources of data to unfold the case: two participant-generated texts (expressive writing and a Breast Biography), two interviews, and my own field notes. The two texts that Ella wrote provided a participant-led frame for depth-hermeneutic group interpretation sessions, the first of which, synergistically, produced a scenic voicing of latent content in the sub-text of Ella’s expressive writing: the fantasy of mothering death. This subsequently became a lead for my further interpretation of the case, and for methodological reflections on the value of shared thinking in qualitative data interpretation. Crucially, and with some bearing on the current healthcare context, this interpretive study sheds light on what goes on beneath the surface of an apparently ‘irrational’ and ‘recalcitrant’ patient, evidenced by Ella’s entry into what I call a ‘vortex of suffering’. Findings point towards her suffering as an expression of a psychosocial reality, against the backdrop of hope and ideals contained within a psychosocial imaginary that revolves around biomedical cure and reparation.   Keywords: breast cancer biography; the breast; psychosocial studies; depth-hermeneutics; vortex of suffering; psychosocial reality

scholarly journals Ekspressiv skriving som egenterapeutisk verktøy ett år etter brystkreftdiagnosen - resultater fra en norsk pilotstudie

2015 ◽  
Vol 10 (2) ◽  
pp. 45
Author(s):  
Birgitta Haga Gripsrud ◽  
Håvard Søiland ◽  
Kirsten Lode
Keyword(s):  
Breast Cancer ◽  
Pilot Study ◽  
Expressive Writing ◽  
Breast Cancer Diagnosis ◽  
Design Data ◽  
Illness Experiences ◽  
Electronic Writing ◽  
Scientific Context ◽  
One Year ◽  
The Mind

<p>Expressive writing as a self-help tool one year after the breast cancer diagnosis – results from a Norwegian pilot study</p><p>The article presents findings from a pilot study on expressive writing, a therapeutic method undescribed in a Norwegian scientific context. Objective: 1. Gain qualitative data on breast cancer women’s experiences with expressive writing. 2. Evaluate the intervention’s feasibility, based on participants’ experiences of the instruction, procedure, and circumstances for writing. Method &amp; design: The study has an exploratory descriptive design. Data collection was achieved through in-depth interviews, followed by experiential thematic analysis of transcripts. Results: Two women enrolled, participating in writing/interviews. Analysis revealed three themes: "The experience of the writing process", "Writing as working through and work to clear the mind", "Strength and vulnerability in relation to others". Conclusion: Findings reveal that expressive writing was experienced as achievable for two breast cancer women, one year after diagnosis. Writing provided an opportunity to work through, and sort out, feelings and thoughts connected to participants’ lives and illness experiences. The instruction was evaluated as easy to understand and inspiring. The women became absorbed in electronic writing in their own homes. They both recommended expressive writing for other women with breast cancer, especially in the period after initial diagnosis.</p>

scholarly journals Using Support Vector Machine Detection of Breast Cancer in Early stage

2020 ◽  
pp. 213-216
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Sensitivity Specificity

The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors

scholarly journals Causes of Death and Relative Survival of Older Women After a Breast Cancer Diagnosis

2011 ◽  
Vol 29 (12) ◽  
pp. 1570-1577 ◽  
Author(s):  
Mara A. Schonberg ◽  
Edward R. Marcantonio ◽  
Long Ngo ◽  
Donglin Li ◽  
Rebecca A. Silliman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Older Women ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Stage I ◽  
Stage Ii ◽  
Mortality Data ◽  
Data Set ◽  
The Impact

Purpose To understand the impact of breast cancer on older women's survival, we compared survival of older women diagnosed with breast cancer with matched controls. Methods Using the linked 1992 to 2003 Surveillance, Epidemiology, and End Results (SEER) -Medicare data set, we identified women age 67 years or older who were newly diagnosed with ductal carcinoma in situ (DCIS) or breast cancer. We identified women not diagnosed with breast cancer from the 5% random sample of Medicare beneficiaries residing in SEER areas. We matched patient cases to controls by birth year and registry (99% or 66,039 patient cases matched successfully). We assigned the start of follow-up for controls as the patient cases' date of diagnosis. Mortality data were available through 2006. We compared survival of women with breast cancer by stage with survival of controls using multivariable proportional hazards models adjusting for age at diagnosis, comorbidity, prior mammography use, and sociodemographics. We repeated these analyses stratifying by age. Results Median follow-up time was 7.7 years. Differences between patient cases and controls in sociodemographics and comorbidities were small (< 4%). Women diagnosed with DCIS (adjusted hazard ratio [aHR], 0.7; 95% CI, 0.7 to 0.7) or stage I disease (aHR, 0.8; 95% CI, 0.8 to 0.8) had slightly lower mortality than controls. Women diagnosed with stage II disease or higher had greater mortality than controls (stage II disease: aHR, 1.2; 95% CI, 1.2 to 1.2). The association of a breast cancer diagnosis with mortality declined with age among women with advanced disease. Conclusion Compared with matched controls, a diagnosis of DCIS or stage I breast cancer in older women is associated with better survival, whereas a diagnosis of stage II or higher breast cancer is associated with worse survival.

An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization Scheme for reliable breast cancer diagnosis

2020 ◽  
Vol 13 ◽  
Author(s):  
S. Punitha ◽  
A. Amuthan ◽  
K. Suresh Joseph
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Cancer Diagnosis ◽  
Artificial Bee Colony ◽  
Breast Cancer Diagnosis ◽  
Bacterial Foraging Optimization ◽  
Data Set ◽  
Cancer Data ◽  
Bacterial Foraging ◽  
Bee Colony

: Breast cancer is essential to be detected in primitive localized stage for enhancing the possibility of survival since it is considered as the major malediction to the women society around the globe. Most of the intelligent approaches devised for breast cancer necessitates expertise that results in reliable identification of patterns that conclude the presence of oncology cells and determine the possible treatment to the breast cancer patients in order to enhance their survival feasibility. Moreover, the majority of the existing scheme of the literature incurs intensive labor and time, which induces predominant impact over the diagnosis time utilized for detecting breast cancer cells. An Intelligent Artificial Bee Colony and Adaptive Bacterial Foraging Optimization (IABC-ABFO) scheme is proposed for facilitating better rate of local and global searching ability in selecting the optimal features subsets and optimal parameters of ANN considered for breast cancer diagnosis. In the proposed IABC-ABFO approach, the traditional ABC algorithm used for cancer detection is improved by integrating an adaptive bacterial foraging process in the onlooker bee and the employee bee phase that results in an optimal exploitation and exploration. The results investigation of the proposed IABC-ABFO approach facilitated using Wisconsin breast cancer data set confirmed an enhanced mean classification accuracy of 99.52% on par with the existing baseline cancer detection schemes.

Identifying Diagnostic Errors with Induced Decision Trees

2001 ◽  
Vol 21 (5) ◽  
pp. 368-375 ◽  
Author(s):  
Catherine K. Murphy
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Decision Trees ◽  
Cancer Diagnosis ◽  
Decision Rules ◽  
Diagnostic Errors ◽  
Breast Cancer Diagnosis ◽  
Data Set ◽  
Minority Class ◽  
New Methods

Objective. The purpose of this article is to compare the diagnostic accuracy of induced decision trees with that of pruned neural networks and to improve the accuracy and interpretation of breast cancer diagnosis from readings of thin-needle aspirate by identifying cases likely to be misclassified by induced decision rules. Method. Using an online database consisting of 699 cases of suspected breast cancer and their corresponding readings of fine-needle aspirate, decision trees were induced from half of the cases, randomly selected. Accuracy was determined for the remaining cases in successive partitions. The pattern of errors in the multiple decision trees was examined. A smaller data set was created with 2 classes: (1) correctly classified and (2) misclassified by a decision tree, rather than the original benign and malignant classes. From this data set, decision trees that describe the misclassified cases were induced. Results. Larger, less severely pruned decision trees were more accurate in breast cancer diagnosis for both training and test data. The accuracy of the induced decision trees exceeded that reported for the smaller pruned neural networks. Combining classifications from 2 trees was effective in identifying malignancies missed by a single tree. Induced decision trees were able to identify patterns associated with misclassified cases, but the identification of errors inductively did not improve the overall error rate. Conclusion. In this application, a model that is too compact identifies fewer cases of the minority class, malignancy. New methods that combine models and examine classification errors can improve diagnosis by identifying more malignancies and by describing ambiguous cases.

Physical Activity and Survival Subsequent to Metastatic Breast Cancer Diagnosis

2010 ◽  
Author(s):  
Susan Sharp ◽  
Ashleigh Golden ◽  
Cheryl Koopman ◽  
Eric Neri ◽  
David Spiegel
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Metastatic Breast Cancer ◽  
Cancer Diagnosis ◽  
Metastatic Breast ◽  
Breast Cancer Diagnosis
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