Enhancing Cancer Treatment with AI: Deep Learning for Predicting Neoadjuvant Therapy Response

Authors : Bita Kheibari, Şebnem Bora, Burçin Pehlivanoğlu, Anil Aysal, Özgül Sağol

Pages : 129-136

View : 30 | Download : 52

Publication Date : 2025-07-31

Article Type : Research Paper

Abstract :Research Problem/Questions— Stomach cancer is one of the most lethal malignancies worldwide, ranking as the sixth most common cancer and the third leading cause of cancer-related deaths. Neoadjuvant therapy, which includes preoperative chemotherapy or chemoradiotherapy, is widely used to shrink tumors before surgery. However, accurately assessing a patient’s response to this treatment remains a significant challenge due to the limitations of traditional histopathological evaluation methods, which are time-consuming and subject to interobserver variability. To address this issue, artificial intelligence (AI) and deep learning models offer a more efficient, accurate, and scalable approach for evaluating treatment responses. Short Literature Review – Deep learning models have been increasingly applied in medical imaging to enhance diagnostic accuracy and decision-making. YOLOv9, a real-time object detection model, has demonstrated high precision in identifying tumor regions in histopathology slides. EfficientDet, on the other hand, enables multi-scale feature extraction and classification, allowing for a more detailed analysis of tumor characteristics and progression . Previous studies have explored AI-based image segmentation and classification in oncology, highlighting the potential of machine learning models to standardize pathology assessments and reduce diagnostic errors . Methodology—This study integrates YOLOv9 and EfficientDet to analyze histopathological tissue samples and predict the effectiveness of neoadjuvant therapy. The research methodology includes data collection from medical institutions, preprocessing and augmentation of histopathological images, transfer learning for model optimization, and performance evaluation using precision, recall, and F1-score metrics. Additionally, AI-generated pathology reports provide automated and standardized assessments for clinicians, improving decision-making in cancer treatment planning . Results and Conclusions— Preliminary findings indicate that this AI-driven approach significantly improves the accuracy of neoadjuvant therapy response prediction, surpassing conventional manual assessments. The integration of deep learning models in histopathological analysis presents a promising future for personalized medicine, early cancer detection, and optimized treatment strategies. This study highlights the potential of AI to revolutionize oncology, paving the way for faster and more reliable cancer diagnostics.
Keywords : Yapay Zeka, Derin Öğrenme, Tümör Tespiti, YOLOv9, EfficientDet, Neoadjuvan Tedavi, Patoloji Analizi

ORIGINAL ARTICLE URL