image: Step 1, data collection: immunogenomics (genomics/transcriptomics), radiomics, and digital pathology data from the real world are appropriately collected and stored. Step 2, data processing: data from various sources undergo several processing steps, including data cleaning to remove inconsistencies, data normalization to standardize values, data augmentation to enhance dataset diversity, and data splitting to create training and testing sets, thus ensuring quality and consistency for analysis and model development. Step 3, feature extraction and analysis: deep learning and machine learning algorithms are used to identify, quantify, and analyze relevant patterns, characteristics, and relationships within datasets for predictive modeling. Step 4, integration and application: extracted features are combined with clinical data to build predictive models and comprehensive systems that enhance diagnosis, treatment planning, and personalized patient care through advanced analysis.
Credit: Xi Wei, Tianjin Medical University Cancer Institute & Hospital
A team of researchers from the Department of Diagnostic and Therapeutic Ultrasonography at the Tianjin Medical University Cancer Institute & Hospital, have published a review (DOI: 10.20892/j.issn.2095-3941.2024.0376) in Cancer Biology & Medicine. The paper underscores the potential of AI to decode complex biological data with unprecedented speed and accuracy. By integrating genomics, medical imaging, and pathology at scale, AI is paving the way for data-driven strategies that bring precision medicine from theory into real-world clinical practice.
In the realm of immunogenomics, AI excels at processing vast quantities of genomic and multi-omic data, identifying patterns and predictive biomarkers linked to immunotherapy responsiveness and prognosis. These insights empower clinicians to design more personalized treatment plans based on a patient’s unique molecular signature.
In radiomics, AI-driven algorithms can extract and interpret high-dimensional quantitative features from imaging modalities such as CT, MRI, and PET/CT. These features capture the spatial and temporal heterogeneity of tumors, offering a non-invasive means to monitor disease progression and treatment response in real time. The ability to stratify patients based on imaging phenotypes holds immense promise for tailoring therapies with greater precision.
Pathomics, the AI-based analysis of digital pathology images, provides yet another layer of innovation. AI can detect subtle variations in cellular morphology and tissue architecture that may elude the human eyes. These micro-level insights into the tumor microenvironment are keys to understanding immune interactions and developing novel biomarkers for therapy selection.
Despite remarkable advances, the authors acknowledge ongoing challenges, including data heterogeneity, model interpretability, and multi-modal integration. Nevertheless, the convergence of AI, bioinformatics, and clinical oncology—fueled by interdisciplinary collaboration—is expected to overcome these barriers. The review envisions a future where AI not only augments diagnostic and prognostic accuracy but also catalyzes the development of novel therapeutic targets.
Dr. Xi Wei, the corresponding author, remarks: “Artificial intelligence is not just a tool—it’s a transformative force accelerating the shift from empirical treatment to true precision medicine. By bridging immunogenomics, radiomics, and pathomics, we can unlock a new dimension of personalized cancer care.”
This work signals a pivotal moment in cancer research, where data integration and algorithmic intelligence unite to advance the frontiers of immunotherapy. As AI continues to evolve, its application in biomarker discovery and treatment optimization promises to enhance patient outcomes, ushering in a new paradigm of individualized medicine.
Journal
Cancer Biology & Medicine
Article Title
Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization
Article Publication Date
15-Apr-2025