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Breast Cancer Diagnostics by the Intelligent Analysis of White Blood Cells’ Interaction With Target Cancer Cells Using Convolutional Neural Networks Publisher



Ali Khayamian M1, 2, 3 ; Salemizadeh Parizi M1 ; Vanaei S1 ; Ghaderinia M1 ; Abadijoo H1 ; Shalileh S1 ; Saghafi M1 ; Simaee H4 ; Abbasvandi F5 ; Akbari N1 ; Karimi A1 ; Sanati H1 ; Sarramiforooshani R5 ; Abdolahad M1, 6, 7
Authors
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Authors Affiliations
  1. 1. Nano Electronic Center of Excellence, Nano Bio Electronic Devices Lab, School of Electrical and Computer Engineering, University of Tehran, Tehran, P.O. BOX 14395/515, Iran
  2. 2. Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 1417614335, Iran
  3. 3. Integrated Biophysics and Bioengineering Lab (iBL), Institute of Biochemistry and Biophysics University of Tehran, Tehran, 1417614335, Iran
  4. 4. Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, P.O. BOX 15179/64311, Iran
  6. 6. UT and TUMS Cancer Electronics Research Center, Tehran University of Medical Sciences, Tehran, Iran
  7. 7. Cancer Institute, Imam-Khomeini Hospital, Tehran University of Medical Sciences, Tehran, P.O. BOX 13145-158, Iran

Source: Microchemical Journal Published:2024


Abstract

Interaction of immune system and cancer cells plays a crucial role in defining the physical and chemical characteristics of the tumor microenvironment. Consequently, monitoring and analyzing these interactions may prove essential for cancer diagnosis and prognosis. While standard techniques assessing cellular interaction are technically complicated and usually expensive, engineering solutions can be employed to introduce novel methods and effective techniques. In this paper, we presented a new blood-based breast cancer hallmark for people suspected of breast tumor disease (BTD) by time-lapse microscopy imaging from the interaction between the patient's blood and MDA-MB-231 breast cancer cell lines. The detection protocol is based on the quantifying invasion of the WBCs to the breast cancer cell line. Many cytological, molecular, and immunofluorescent assays were carried out to approve the hypothesis. Blood immune cells showed meaningful invasion patterns to breast cancer cell lines in reverse correlation by the cancerous stage of the patients. Hence, we believe that the immune system is cognizant of the neoplastic nature of breast tumor disease. To eliminate all the human-related limitations, a convolutional neural network (CNN) architecture was used for invasion recognition. The proposed CNN architecture showed an accuracy of approximately 86%, making it a reliable, fast, and easy way for intelligent detection of invasion patterns to decide on the tumor stage. Results made us present the hypothesis that people with more aggressive breast cancer tumors have less strong immune cells to invade cancer cells which could be a start in the clinical use of the cellular-based immune system for cancer investigation. As WBCs were isolated from the blood with no pre-processing, this method would shed new light as a simple complementary method for better clarification of tumor nature. © 2024 Elsevier B.V.
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