From Cancer Treatment Strategy to Cancer Survivorship Care: Advances in Multi-Omics and AI Integration

Abstract: Cancer encompasses a variety of diseases marked by unregulated growth and spread of abnormal cells, driven by genetic factors (inherited/acquired mutations), environmental exposures (chemicals, toxins, infections, tobacco, radiation), lifestyle choices (diet, obesity, alcohol, physical inactivity), and hormonal influences (estrogen, testosterone). This review highlights promising ad-vances in individual and combined anti-cancer therapies, including immunotherapy (e.g., immune checkpoint inhibitors targeting PD-1/PD-L1/CTLA-4, adoptive cell transfer such as CAR-T/TCR-T therapies) and gene therapy (e.g., AAV-mediated delivery of therapeutic genes), often integrated with chemotherapy, radiotherapy, or anti-angiogenic treatments to improve survival rates and reduce re-currence while optimizing healthcare delivery systems. For cancer survivors, transitioning from ac-tive treatment to long-term care is critical, requiring structured frameworks for prevention (e.g., sur-veillance for secondary cancers), surveillance (e.g., monitoring treatment-related complications), in-tervention (e.g., psychosocial support, symptom management), and coordination (e.g., collaboration between primary care providers and specialists) to address challenges such as physical well-being (pain, fatigue), psychological distress (anxiety, depression), and systemic barriers like fragmented healthcare and financial toxicity; innovative models like shared-care approaches (collaboration with primary care physicians) and nurse-led follow-up programs under physician supervision have emerged to enhance accessibility and continuity. The rapid advancement of Artificial Intelligence (AI), categorized into machine learning (e.g., supervised/unsupervised learning) and deep learning (e.g., DeepAffinity, DeepDTAF), further revolutionizes survivorship care by enabling precise diag-nostics through multimodal data analysis (imaging, genomics, electronic health records) for early recurrence detection and personalized risk stratification, facilitating tailored treatment planning based on genetic profiles and lifestyle to predict therapy responses and side effects, and streamlining healthcare operations via chatbots for symptom monitoring, virtual assistants for appointment sched-uling, and predictive analytics for resource allocation, ultimately powering Cancer Survivorship Care Databases to facilitate evidence-based decision-making for family physicians, reduce treatment in-tensity where appropriate, and foster global collaboration through shared-care models, offering a transformative pathway to elevate survivorship care standards, reduce healthcare costs, and improve outcomes for millions of cancer survivors worldwide.