Deep Learning in Cancer Diagnosis, Prognosis, and Therapeutics

Cancer is a leading global cause of mortality, with various underlying conditions and molecular pathways contributing to its onset and progression. Recent strides in computational biology have yielded diverse data sources such as highresolution scan imaging, genomic profiles, transcriptomic data, and histopathological information linked to cancer. Analysing this data comprehensively can unveil the disease’s complex biology, aiding in early, precise diagnosis and targeted drug development. Deep Learning, an advanced branch of Artificial Intelligence (AI), leverages multi-layered neural networks to mimic intricate decision-making like the human brain. Its robust learning capabilities enable efficient analysis of vast biological datasets. While early findings are promising, the dynamic nature of this field continually yields new insights into both deep learning and cancer biology. The intersection of these domains holds promise for revolutionising cancer diagnosis, prognosis, and therapeutic strategies. This chapter outlines emerging deep learning techniques applied in oncology, specifically focusing on MRI image analysis, genomic profiling, histopathology, and transcriptomic data analysis. Examples highlighting how deep learning can enhance cancer diagnosis, prognosis, and management are discussed. The chapter concludes with a discussion of current limitations and challenges in applying deep learning in oncology and proposes strategies to overcome these hurdles and maximise its efficient utilisation.