scholarly journals Use of peripheral lymphocytes and support vector machine for survival prediction in breast cancer patients

2018 ◽  
Vol 7 (4) ◽  
pp. 978-987
Author(s):  
Fang Bai ◽  
Chuanchao Wei ◽  
Peng Zhang ◽  
Dexi Bi ◽  
Meixin Ge ◽  
...  
2021 ◽  
Vol 11 ◽  
Author(s):  
Mozhi Wang ◽  
Zhiyuan Pang ◽  
Yusong Wang ◽  
Mingke Cui ◽  
Litong Yao ◽  
...  

Tumor microenvironment has been increasingly proved to be crucial during the development of breast cancer. The theory about the conversion of cold and hot tumor attracted the attention to the influences of traditional therapeutic strategies on immune system. Various genetic models have been constructed, although the relation between immune system and local microenvironment still remains unclear. In this study, we tested and collected the immune index of 262 breast cancer patients before and after neoadjuvant chemotherapy. Five indexes were selected and analyzed to form the prediction model, including the ratio values between after and before neoadjuvant chemotherapy of CD4+/CD8+ T cell ratio; lymphosum of T, B, and natural killer (NK) cells; CD3+CD8+ cytotoxic T cell percent; CD16+CD56+ NK cell absolute value; and CD3+CD4+ helper T cell percent. Interestingly, these characters are both the ratio value of immune status after neoadjuvant chemotherapy to the baseline. Then the prediction model was constructed by support vector machine (accuracy rate = 75.71%, area under curve = 0.793). Beyond the prognostic effect and prediction significance, the study instead emphasized the importance of immune status in traditional systemic therapies. The result provided new evidence that the dynamic change of immune status during neoadjuvant chemotherapy should be paid more attention.


2020 ◽  
Vol 8 (21) ◽  
pp. 1446-1446
Author(s):  
Jianli Zhao ◽  
Yaping Yang ◽  
Danmei Pang ◽  
Yunfang Yu ◽  
Xiao Lin ◽  
...  

2004 ◽  
Vol 35 (6) ◽  
pp. 480-483 ◽  
Author(s):  
Patricia Sánchez ◽  
Rubicelia Peñarroja ◽  
Francisco Gallegos ◽  
José Luis Bravo ◽  
Emilio Rojas ◽  
...  

2021 ◽  
Vol 12 (4) ◽  
pp. 117-137
Author(s):  
Mazen Mobtasem El-Lamey ◽  
Mohab Mohammed Eid ◽  
Muhammad Gamal ◽  
Nour-Elhoda Mohamed Bishady ◽  
Ali Wagdy Mohamed

There are many cancer patients, especially breast cancer patients as it is the most common type of cancer. Due to the huge number of breast cancer patients, many breast cancer-focused hospitals aren't able to process the huge number of patients and might expose some women to late stages of cancer. Thus, the automation of the process can help these hospitals in speeding up the process of cancer detection. In this paper, the authors test several machine learning models such as k-nearest neighbours (KNN), support vector machine (SVM), and artificial neural network (ANN). They then compare their accuracies and losses with themselves and other models that have been developed by other researchers to see whether their approach is efficient or not and to decide what machine learning algorithm is best to use.


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