Personalized oncology is rapidly advancing, integrating cutting-edge imaging, novel molecular assays, and sophisticated analytical methods. This inherently multidisciplinary development is redefining cancer diagnostics and therapy, demanding close collaboration across physics, chemistry, biology, biomedical engineering, computer science, and clinical medicine. Modern imaging systems, from time-resolved fluorescence to multi-parametric platforms, offer unprecedented insights into tumor biology, visualizing molecular interactions and tumor microenvironment dynamics in real time. Simultaneously, new probes, contrast agents, and chemical sensing mechanisms are pushing the boundaries of sensitivity and specificity in both preclinical and clinical settings.

The goal of this Research Topic is to address the complex challenges in cancer diagnostics and therapy by leveraging recent advances across diverse scientific and engineering disciplines. We aim to explore how computational and physical models, including physics-informed inverse models and data-driven methods, enable accurate image reconstruction and quantitative analysis. Furthermore, we seek to highlight the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in personalized oncology, particularly deep learning approaches for tasks like tumor segmentation and longitudinal monitoring. We also want to showcase how biological methods like multi-omics (genomics, transcriptomics, proteomics, and metabolomics), protein-ligand interaction profiling, and next-generation sequencing (NGS) are stratifying patients and identifying actionable targets. The convergence of these fields offers deeper insights into tumor resistance, progression, and precision treatment design.

This Research Topic invites Original Research and Reviews showcasing breakthroughs in imaging science, molecular diagnostics, and computational methods for cancer detection, classification, and treatment monitoring. We are particularly interested in studies demonstrating translational potential and clinical relevance. This includes, but is not limited to, intraoperative guidance systems, biopsy-free molecular diagnostics, and dynamic monitoring of treatment response. We encourage submissions that integrate explainable AI, attention mechanisms, and hybrid physics-data models to enhance interpretability and clinical trust, and those that present these tools as embedded components of end-to-end imaging and analysis pipelines.

Scope of Interest includes (but not limited to):

• Advanced fluorescence, fluorescence lifetime (FLI), and Förster resonance energy transfer (FRET) imaging
• Spectroscopic, hyperspectral, photoacoustic, optoacoustic, and label-free imaging methods (e.g., Raman, SHG, CARS, OCT)
• Novel contrast agents, fluorophores, chemical probes, and micro/nanoscale assays for in vivo and in vitro cancer profiling
• Computational imaging, AI-enhanced methods, model-based reconstruction, and multimodal image registration across scales
• Intraoperative imaging, real-time decision support tools, and integration of imaging biomarkers with clinical decision-making in surgical oncology and image-guided therapy
• Artificial tissue engineering and 3D culture systems for personalized oncology research
• Organoid platforms and ex vivo tumor models for drug testing and biomarker validation
• NGS-based diagnostics and computational pipelines for mutation profiling and personalized therapy
• Protein-ligand binding studies and molecular docking for therapeutic target identification
• Multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics) for patient stratification and tumor characterization

This Research Topic offers a platform for interdisciplinary work that advances the scientific and translational landscape of personalized oncology. Submissions from both fundamental research and applied clinical studies are encouraged, particularly those that demonstrate how technical innovation leads to improved cancer care.

Are you interested in contributing a manuscript to this Research Topic?

Next-Generation Preclinical Imaging and Analytical Technologies for Personalized Oncology