scholarly journals Evaluation of breast cancer using intravoxel incoherent motion (IVIM) histogram analysis: comparison with malignant status, histological subtype, and molecular prognostic factors

2015 ◽  
Vol 26 (8) ◽  
pp. 2547-2558 ◽  
Author(s):  
Gene Young Cho ◽  
Linda Moy ◽  
Sungheon G. Kim ◽  
Steven H. Baete ◽  
Melanie Moccaldi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Histogram Analysis ◽  
Intravoxel Incoherent Motion ◽  
Histological Subtype

scholarly journals Breast Tumor Detection and Classification Using Intravoxel Incoherent Motion Hyperspectral Imaging Techniques

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Si-Wa Chan ◽  
Yung-Chieh Chang ◽  
Po-Wen Huang ◽  
Yen-Chieh Ouyang ◽  
Yu-Tzu Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Malignant Tumors ◽  
Chronic Renal Disease ◽  
Imaging Techniques ◽  
Image Resolution ◽  
Histogram Analysis ◽  
Intravoxel Incoherent Motion ◽  
Analysis Approach ◽  
Generation Process

Breast cancer is a main cause of disease and death for women globally. Because of the limitations of traditional mammography and ultrasonography, magnetic resonance imaging (MRI) has gradually become an important radiological method for breast cancer assessment over the past decades. MRI is free of the problems related to radiation exposure and provides excellent image resolution and contrast. However, a disadvantage is the injection of contrast agent, which is toxic for some patients (such as patients with chronic renal disease or pregnant and lactating women). Recent findings of gadolinium deposits in the brain are also a concern. To address these issues, this paper develops an intravoxel incoherent motion- (IVIM-) MRI-based histogram analysis approach, which takes advantage of several hyperspectral techniques, such as the band expansion process (BEP), to expand a multispectral image to hyperspectral images and create an automatic target generation process (ATGP). After automatically finding suspected targets, further detection was attained by using kernel constrained energy minimization (KCEM). A decision tree and histogram analysis were applied to classify breast tissue via quantitative analysis for detected lesions, which were used to distinguish between three categories of breast tissue: malignant tumors (i.e., central and peripheral zone), cysts, and normal breast tissues. The experimental results demonstrated that the proposed IVIM-MRI-based histogram analysis approach can effectively differentiate between these three breast tissue types.

Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer

Breast Cancer ◽  
2018 ◽  
Vol 26 (1) ◽  
pp. 113-124 ◽  
Author(s):  
Ken Nagasaka ◽  
Hiroko Satake ◽  
Satoko Ishigaki ◽  
Hisashi Kawai ◽  
Shinji Naganawa
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Molecular Subtypes ◽  
Histogram Analysis ◽  
Pharmacokinetic Parameters ◽  
Dce Mri

Prognostic Factors in Breast Cancer

1989 ◽  
Vol 3 (4) ◽  
pp. 641-652 ◽  
Author(s):  
Douglas E. Merkel ◽  
C. Kent Osborne
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors

Prognostic Factors in Breast Cancer.

1995 ◽  
Author(s):  
Jeffery Seidman
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors

Prognostic Factors in Breast Cancer

1997 ◽  
Author(s):  
Timothy J. O'Leary ◽  
Jeffrey D. Seidman
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors

scholarly journals Surgery for primary tumor benefits survival for breast cancer patients with bone metastases: a large cohort retrospective study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhangheng Huang ◽  
Xin Zhou ◽  
Yuexin Tong ◽  
Lujian Zhu ◽  
Ruhan Zhao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Bone Metastases ◽  
Cancer Patients ◽  
Primary Tumor ◽  
Predictive Accuracy ◽  
Rank Test ◽  
Breast Cancer Patients ◽  
Risk Of Death ◽  
The Impact

Abstract Background The role of surgery for the primary tumor in breast cancer patients with bone metastases (BM) remains unclear. The purpose of this study was to determine the impact of surgery for the primary tumor in breast cancer patients with BM and to develop prognostic nomograms to predict the overall survival (OS) of breast cancer patients with BM. Methods A total of 3956 breast cancer patients with BM from the Surveillance, Epidemiology, and End Results database between 2010 and 2016 were included. Propensity score matching (PSM) was used to eliminate the bias between the surgery and non-surgery groups. The Kaplan-Meier analysis and the log-rank test were performed to compare the OS between two groups. Cox proportional risk regression models were used to identify independent prognostic factors. Two nomograms were constructed for predicting the OS of patients in the surgery and non-surgery groups, respectively. In addition, calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the performance of nomograms. Result The survival analysis showed that the surgery of the primary tumor significantly improved the OS for breast cancer patients with BM. Based on independent prognostic factors, separate nomograms were constructed for the surgery and non-surgery groups. The calibration and ROC curves of these nomograms indicated that both two models have high predictive accuracy, with the area under the curve values ≥0.700 on both the training and validation cohorts. Moreover, DCA showed that nomograms have strong clinical utility. Based on the results of the X-tile analysis, all patients were classified in the low-risk-of-death subgroup had a better prognosis. Conclusion The surgery of the primary tumor may provide survival benefits for breast cancer patients with BM. Furthermore, these prognostic nomograms we constructed may be used as a tool to accurately assess the long-term prognosis of patients and help clinicians to develop individualized treatment strategies.

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