Pathology in Precision and Personalized Oncology: Integrating AI, Digital Biomarkers, and Computational Biophysics and Experimental Studies. Volume II

Building on the success of Pathology in Personalised Oncology: Biomarkers, Predictive Models, and Therapeutic Targets, we are pleased to launch Volume II.Modern oncology is being transformed by the rapid convergence of artificial intelligence, digital pathology, and computational biophysics. Today, AI-driven image analysis is revolutionizing how we identify prognostic and predictive biomarkers directly from tissue slides, extracting critical data that extends far beyond human visual limits. Simultaneously, advanced computational tools such as Molecular Dynamics (MD) simulations and MM/GBSA calculations provide insights into protein stability, drug-target interactions, and mutation-driven resistance mechanisms. While laboratory models and in vitro assays remain highly valuable for confirming these in silico observations, the immense predictive power of modern AI and computational algorithms is increasingly robust enough to guide therapeutic strategies on its own. This rapidly evolving landscape allows researchers to influence massive digital and structural datasets, seamlessly connecting the gap between molecular mechanisms, histopathological features, and highly personalized patient care.The primary goal is to highlight cutting-edge advances at the intersection of AI, computational modeling, and precision oncology. We aim to create a multidisciplinary platform that brings together digital pathologists, biophysicists, computational biologists, and experimental oncologists to translate complex algorithmic data into actionable clinical insights. This volume seeks to champion the discovery of novel digital biomarkers and structural insights driven by advanced machine learning and predictive biophysics. While we strongly encourage studies that pair in silico pipelines with empirical laboratory validation, we equally welcome robust, standalone computational and AI-based research that pushes the boundaries of current analytical capabilities. Ultimately, our objective is to showcase how intelligent algorithms, structural modeling, and targeted experimental insights can synergize to accelerate biomarker discovery, improve patient stratification, and refine tailored cancer therapies.We invite Original Research, Reviews, Methods, and Perspectives that explore computational, pathological, and experimental methodologies in oncology. Key areas of interest include the integration of AI and digital pathology to discover novel predictive and diagnostic biomarkers, supporting enhanced patient stratification. We welcome rigorous computational studies utilizing MD simulations, structural modeling, and MM/GBSA calculations to decode oncogenic mutations, drug interactions, and resistance pathways. Overall, we seek submissions demonstrating how digital innovations, structural biophysics, and molecular pathology either independently or alongside laboratory models can be successfully translated into practical clinical applications for advanced cancer management. Topic editors: Mohammad Zeeshan Najm, Mohd Ashif Khan Source: https://www.frontiersin.org/research-topics/84835/pathology-in-precision-and-personalized-oncology-integrating-ai-digital-biomarkers-and-computational-biophysics-and-experimental-studies-volume-ii