Abstract P5-01-16: Combining HyperVOX with pattern recognition neural networks: A new method for analyzing flow cytometry-based immunophenotyping data for increased early detection of stage I/II breast cancer (BCa)

2020 ◽  
Author(s):  
George A Dominguez ◽  
John Roop ◽  
Alexander Polo ◽  
Anthony Campisi ◽  
Dmitry I Gabrilovich ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Flow Cytometry ◽  
Pattern Recognition ◽  
Early Detection ◽  
Stage I ◽  
New Method

Staging Breast Cancer - Screening for Occult Metastases

1985 ◽  
Vol 71 (4) ◽  
pp. 339-344 ◽  
Author(s):  
Stefano Ciatto ◽  
Paolo Pacini ◽  
Patrizia Bravetti ◽  
Luigi Cataliotti ◽  
Gaetano Cardona ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Detection Rate ◽  
Clinical Stage ◽  
Preoperative Staging ◽  
Stage I ◽  
Stage Iii ◽  
Prognostic Impact ◽  
Detection Rates ◽  
Occult Metastases

The authors report on 1,017 consecutive breast cancer cases without symptomatic metastases staged by means of chest X-ray (CXR), skeletal survey (BXR) and bone scintigraphy (BS). Occult metastases (DM) detection rate was 0.88 %: 0.29 % for lung and 0.59 % for bone DM. The detection rate was correlated with clinical stage: 0.36 % for stage I, 0.20 % for stage II, 0.26 % for stages I and II, and 2.77 % for stage III cases. The sensitivity based on DM cases prevalent or surfacing within 6 months of follow-up was 0.30 for CXR, 0.22 for BXR and 0.55 for BS; specificity was 0.99, 0.98 and 0.90, respectively. The study confirms the possibility of early detection of DM with preoperative staging, but the extremely low detection rates in stage I and II cancers do not advise such a routine procedure. The higher detection rate of DM may suggest adoption of the routine staging procedure in stage III cancers. In these cases, although no evidence is available of a favorable prognostic impact of early detection and treatment of DM, an unnecessary mastectomy could be avoided in about 3 % of cases in the presence of DM detected by the staging procedure.

scholarly journals Interpretable multi-stream ensemble learning for radiographic pattern recognition

2021 ◽  
Author(s):  
Jose Raniery Ferreira ◽  
Diego Armando Cardona Cardenas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Early Detection ◽  
Ensemble Learning ◽  
Chest Radiography ◽  
Lung Diseases ◽  
Model Output ◽  
Critical Factors ◽  
Ensemble Strategy ◽  
Computer Based

Chest radiography (CXR) remains an essential component to evaluate lung diseases. However, it is crucial nowadays to include computer-based tools to aid physicians in the early detection of chest abnormalities. Therefore, this work proposed deep ensemble models to improve the CXR evaluation, interpretability, and reproducibility. Five convolutional neural networks and six different processed image inputs yielded an AUC of 0.982. Furthermore, ensemble learning could produce more reliable outcomes as it did not consider the information of only one method. Moreover, the ensemble strategy balanced the most critical factors from each model to perform a more consistent classification. Finally, class activation and gradient propagation maps allowed locally visualizing CXR regions that most activate neurons from the trained models and explaining practically which areas of the CXR correlated to the model output.

scholarly journals Breast Cancer by Age at Diagnosis in the Gharbiah, Egypt, Population-Based Registry Compared to the United States Surveillance, Epidemiology, and End Results Program, 2004–2008

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jennifer A. Schlichting ◽  
Amr S. Soliman ◽  
Catherine Schairer ◽  
Joe B. Harford ◽  
Ahmed Hablas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
United States ◽  
Early Detection ◽  
Stage Iv ◽  
Tumor Grade ◽  
The United States ◽  
Stage I ◽  
Future Research ◽  
Breast Cancers ◽  
End Results

Objective. Although breast cancers (BCs) in young women often display more aggressive features, younger women are generally not screened for early detection. It is important to understand the characteristics of young onset breast cancer to increase awareness in this population. This analysis includes all ages, with emphasis placed on younger onset BC in Egypt as compared to the United States.Methods. BC cases in the Gharbiah cancer registry (GCR), Egypt, were compared to those in the Surveillance, Epidemiology, and End Results (SEER) database. This analysis included 3,819 cases from the GCR and 273,019 from SEER diagnosed 2004–2008.Results. GCR cases were diagnosed at later stages, with <5% diagnosed at Stage I and 12% diagnosed at Stage IV. 48% of all SEER cases were diagnosed at Stage I, dropping to 30% among those ≤40. Significant differences in age, tumor grade, hormone receptor status, histology, and stage exist between GCR and SEER BCs. After adjustment, GCR cases were nearly 45 times more likely to be diagnosed at stage III and 16 times more likely to be diagnosed at stage IV than SEER cases.Conclusions. Future research should examine ways to increase literacy about early detection and prompt therapy in young cases.

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