Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT
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NAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis.
Methods
This article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC.
Results
Pathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response.
Conclusion
It was concluded that deep learning methods can predict breast cancer treatment.
Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. He completed his PhD in Computer Engineering at Kırıkkale University between 2017-2021 and wrote his thesis on "Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms".
His research interests focus on artificial intelligence, machine learning, image processing and deep neural networks. He has carried out important academic studies using artificial intelligence applications in different fields such as medicine, agriculture, food technologies and environmental analysis.
Since 2021, he has been continuing his administrative duties as the Head of the Department of Information Processing and Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses such as Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, Artificial Intelligence Applications.
His academic publications include more than 75 articles scanned by SCI-Expanded, SSCI and Scopus, and international conference proceedings. His work covers various topics such as brain tumor analysis with artificial intelligence, artificial intelligence applications in agriculture, environmental data analysis and object detection.
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