scholarly journals Deep Vision for Breast Cancer Classification and Segmentation

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5384
Author(s):  
Lawrence Fulton ◽  
Alex McLeod ◽  
Diane Dolezel ◽  
Nathaniel Bastian ◽  
Christopher P. Fulton
Keyword(s):  
Breast Cancer ◽  
False Negative ◽  
Image Representation ◽  
Region Of Interest ◽  
Training Model ◽  
Female Breast Cancer ◽  
Training Images ◽  
Female Breast ◽  
Classification Probability ◽  
Initial Classification

(1) Background: Female breast cancer diagnoses odds have increased from 11:1 in 1975 to 8:1 today. Mammography false positive rates (FPR) are associated with overdiagnoses and overtreatment, while false negative rates (FNR) increase morbidity and mortality. (2) Methods: Deep vision supervised learning classifies 299 × 299 pixel de-noised mammography images as negative or non-negative using models built on 55,890 pre-processed training images and applied to 15,364 unseen test images. A small image representation from the fitted training model is returned to evaluate the portion of the loss function gradient with respect to the image that maximizes the classification probability. This gradient is then re-mapped back to the original images, highlighting the areas of the original image that are most influential for classification (perhaps masses or boundary areas). (3) Results: initial classification results were 97% accurate, 99% specific, and 83% sensitive. Gradient techniques for unsupervised region of interest mapping identified areas most associated with the classification results clearly on positive mammograms and might be used to support clinician analysis. (4) Conclusions: deep vision techniques hold promise for addressing the overdiagnoses and treatment, underdiagnoses, and automated region of interest identification on mammography.

Treatment disparities in female breast cancer patients according to race, ethnicity and socioeconomic status

2013 ◽  
Author(s):  
Christopher S. Bartlett ◽  
Tulay Koru-Sengul ◽  
Feng Miao ◽  
Stacey L. Tannenbaum ◽  
David J. Lee ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Socioeconomic Status ◽  
Cancer Patients ◽  
Female Breast Cancer ◽  
Breast Cancer Patients ◽  
Female Breast ◽  
Treatment Disparities ◽  
Race Ethnicity

scholarly journals 165P Comparison of male and female breast cancer in reference hospital in Mexico

2021 ◽  
Vol 32 ◽  
pp. S90-S91
Author(s):  
G. Sanchez ◽  
A. Gutierrez ◽  
J.C. Jímenez ◽  
R. Correa ◽  
J.A. Alegría Baños ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Female Breast Cancer ◽  
Male And Female ◽  
Female Breast ◽  
Reference Hospital

scholarly journals Socio-Economic Position, Cancer Incidence and Stage at Diagnosis: A Nationwide Cohort Study in Belgium

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 933
Author(s):  
Michael Rosskamp ◽  
Julie Verbeeck ◽  
Sylvie Gadeyne ◽  
Freija Verdoodt ◽  
Harlinde De Schutter
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Cohort Study ◽  
Cancer Incidence ◽  
Regression Models ◽  
Female Breast Cancer ◽  
Stage At Diagnosis ◽  
Female Breast ◽  
Economic Parameters ◽  
Economic Position

Background: Socio-economic position is associated with cancer incidence, but the direction and magnitude of this relationship differs across cancer types, geographical regions, and socio-economic parameters. In this nationwide cohort study, we evaluated the association between different individual-level socio-economic and -demographic factors, cancer incidence, and stage at diagnosis in Belgium. Methods: The 2001 census was linked to the nationwide Belgian Cancer Registry for cancer diagnoses between 2004 and 2013. Socio-economic parameters included education level, household composition, and housing conditions. Incidence rate ratios were assessed through Poisson regression models. Stage-specific analyses were conducted through logistic regression models. Results: Deprived groups showed higher risks for lung cancer and head and neck cancers, whereas an inverse relation was observed for malignant melanoma and female breast cancer. Typically, associations were more pronounced in men than in women. A lower socio-economic position was associated with reduced chances of being diagnosed with known or early stage at diagnosis; the strongest disparities were found for male lung cancer and female breast cancer. Conclusions: This study identified population groups at increased risk of cancer and unknown or advanced stage at diagnosis in Belgium. Further investigation is needed to build a comprehensive picture of socio-economic inequality in cancer incidence.

Age-Specific Spatio-Temporal Patterns of Female Breast Cancer Mortality in Spain (1975–2005)

2010 ◽  
Vol 20 (12) ◽  
pp. 906-916 ◽  
Author(s):  
María D. Ugarte ◽  
Tomás Goicoa ◽  
Jaione Etxeberria ◽  
Ana F. Militino ◽  
Marina Pollán
Keyword(s):  
Breast Cancer ◽  
Cancer Mortality ◽  
Breast Cancer Mortality ◽  
Temporal Patterns ◽  
Female Breast Cancer ◽  
Female Breast ◽  
Spatio Temporal

scholarly journals Study of Gene Expression of Programmed Cell Death Ligand 1 (PD-L1) in Breast Cancer Patients

2021 ◽  
Vol 107 (1_suppl) ◽  
pp. 2-2
Author(s):  
H Gadelrab ◽  
M Mokhtar ◽  
H Morsy ◽  
M Elnaggar
Keyword(s):  
Breast Cancer ◽  
Cell Death ◽  
Programmed Cell Death ◽  
Cancer Patients ◽  
Therapeutic Response ◽  
Female Breast Cancer ◽  
Clinicopathological Parameters ◽  
Breast Cancer Patients ◽  
Expression Levels ◽  
Female Breast

Introduction: Breast cancer is the most frequently occurring cancer among females and the second most common cancer overall. Programmed Cell Death Ligand 1 (PD-L1) plays an important role in blocking ‘cancer-immunity cycle’ and is considered as a major inhibitory pathway. The aim of the present study was to clarify the alterations of expression of PD-L1 in peripheral blood mononuclear cytes (PBMCs) of female breast cancer patients and analyze its association with clinico-pathological criteria as well as therapeutic response. Materials and Methods: The study was conducted on 45 female breast cancer patients and 45 female controls. Blood samples were collected followed by PBMCs isolation, total RNA extraction, reverse transcription and finally, quantitative polymerase chain reaction (qPCR) using SYBR Green DNA binding dye. Expression levels of PD-L1 were calculated and then compared with clinicopathological parameters of the patients in addition to initial therapeutic response. Results: A significant difference was detected for PD-L1 expression levels in breast cancer patients compared to controls. A significant association with age, metastatic breast cancer, estrogen receptor (ER) negative status as well as high concentrations of cancer antigen 15-3 (CA15-3) was detected. On the other hand, no significant association was recognized with tumor size, lymph nodal status, histopathological type, grade, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER-2) status, triple negative, among de novo and recurrent metastatic patients and for the number of metastatic sites as well as the therapeutic response. Conclusions: This study paves the way of the use of PD-L1 as a noninvasive prognostic and diagnostic biomarker for poor prognosis of breast cancer.

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