Introduction: Why Deep Learning Matters in Cancer Care

The global cancer burden continues to rise sharply, posing immense challenges to healthcare systems worldwide. In 2022 alone, nearly 20 million new cancer cases and close to 10 million cancer-related deaths were recorded, with projections indicating an alarming 77% increase in incidence by 2050—surpassing 35 million new cases annually. Early detection remains one of the most critical obstacles; many cancers are still diagnosed at advanced stages, where treatment options are limited and survival rates diminished. Beyond early detection, accurate tumor classification, response prediction, and efficient drug development remain pressing challenges in oncology.1,2

Deep learning (DL)—a powerful branch of artificial intelligence (AI)—has emerged as a transformative tool in this space. With its ability to detect complex, nonlinear patterns in large datasets such as histopathology slides, genomic sequences, and radiological images, DL offers unprecedented capabilities across the cancer care continuum. From improving screening accuracy to guiding personalized therapy and accelerating drug discovery, DL is reshaping the landscape of oncology and offering hope for improved patient outcomes and more equitable global cancer care.3

Understanding Deep Learning in Oncology

Deep learning utilizes artificial neural networks to model complex relationships in data, often outperforming traditional machine learning algorithms. In oncology, convolutional neural networks (CNNs) dominate image-based analysis tasks—such as detecting lesions on radiological scans or classifying tumors in histopathology slides. Recurrent neural networks (RNNs) and transformer-based models excel at analyzing sequential and longitudinal data, such as patient histories and genomic profiles, enabling dynamic disease modeling and response forecasting.

Unlike conventional algorithms that rely on manua

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