Early detection of breast cancer through liquid biopsy using deep learning technology
Publish place: 14th International Congress on Breast Cancer
Publish Year: 1397
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICBCMED14_007
تاریخ نمایه سازی: 21 مرداد 1398
Abstract:
Introduction & Aim: Breast cancer is one of the most prevalent and fatal types of cancer among women. The early diagnosis of breast cancer is a crucial step in reducing its morbidity and mortality. Deep learning approaches have been shown its great potential in various segments of medical researches. In the present study, we propose a new method for early detection of breast cancer using artificial intelligence and deep learning that assess the level of protein biomarkers and cell-free DNAs in blood samples to detect breast cancer. Methods: In this study, we used previously published dataset named CancerSeek. A total of 209 patients with non-metastatic breast cancer and 812 healthy patients included in the study. We used Tensorflow package in python environment to deploy our deep learning model. We trained the data set using 10 cross-folds method. Results: Under our experimental setting, our model reached the specificity and sensitivity of 97% and 81%, respectively. Meanwhile, the area under the curve was 97.13% which was higher than other similar studies that used this database. Conclusion: The results of the study demonstrated that using a blood sample analysis through deep learning can potentially lead to a new approach in the early diagnosis of cancer. Furthermore, deep learning technology can give us the ability to eliminate bias, interpret uncertainty, explain data and reduce diagnosis costs more efficiently.
Authors
Amir Daaee
School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran.
Mohammad Taheri
Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hossein Mohammad-Rahimi
Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hosein Kazazi
Master of Computer Applications, Iranian University, Tehran Iran