The pursuit of advancements in oncology has been significantly bolstered by the integration of computational methods and data science. This integration has ushered in a new era of precision medicine, characterized by highly individualized treatment strategies. Computational oncology encompasses not just the application of artificial intelligence for image analysis and genomics but also extends to sophisticated data analytics for identifying biomarkers, optimizing treatment plans, and predicting clinical outcomes. Oligometastatic diseases are increasing pursued with local treatment as systematic therapies have been significantly improved over the last 2 decades with emerging targeted therapies and immunotherapies. The data supporting the use of local therapy for oligometastatic disease has mixed results. We believe that using computation oncology and harnessing the most recent advances in AI and machine learning, may help us to further understand the biology of oligometastatic disease and help us decide how best to address the disease.

The primary aim of this special edition in Frontiers of Oncology is to serve as a platform for exploring the role of computational methods and data science in clinical oncology, especially in the area of refining management for the oligometastatic disease. This edition recognizes the critical importance of leveraging computational approaches and data science innovations for individualized treatment strategies in patients with oligometastatic lesions.

We intend to highlight and disseminate cutting-edge research, breakthrough innovations, and practical applications that demonstrate the synergy between computational oncology and data science, how to integrate this information for patient selection of who may benefit from local treatment for those oligometastatic lesions.

Ultimately, this article collection seeks to equip the oncology community with comprehensive insights into the potential of computational and data science methodologies, fostering hope for improved patient outcomes who has overall good disease control with systematic treatment but suffer from oligoprogressive or oligopersistant disease.

This Research Topic aims to provide a comprehensive exploration of the latest developments, challenges, and various facets of computational methods and data science applications within clinical oncology. We welcome manuscripts focusing on, but not limited to, the following areas:

• Computational Approaches in Oncology Genomics: Investigate state-of-the-art computational strategies for genomic data analysis for patient selection.
• Advancements in Medical Analysis: Discuss the latest developments in computational image processing in treating oligometastatic lesions.
• Personalized Treatment Strategies: Explore computational techniques, including AI, in developing tailored treatment plans, considering genetic profiles and clinical data in selecting patients who may benefit from local therapy.
• Predictive Analytics in Oncology: Address the role of computational methods in identifying and validating predictive biomarkers for diagnosis, prognosis, and treatment efficacy.
• Clinical Decision Support and Ethics: Examine ethics and insurance issues related to using local therapies for metastatic disease.
• Clinical Trials Optimization: Discussions, insights, perspectives and reviewers on how AI can help streamline patient recruitment, trial design, and data analysis in oncology clinical trials related oligometastatic diseases.

Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.

Dr. James Sohn is the CEO of Oncosoft and receives financial compensation. Shuhua (Steve) Zheng, DO, PhD is a shareholder and cofounder of KANCURE PTE. LTD., and KANCURE RESEARCH PTE. LTD. The other Topic Editors declare no potential competing interests with regards to this topic theme.

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