The diagnostic armamentarium for solid tumors is evolving rapidly, redefining how we detect, stage, stratify, monitor, and ultimately treat cancer. This Research Topic invites original research, reviews, mini-reviews, brief reports, technology reports, and perspectives that advance clinically actionable diagnostics across the continuum of care. We aim to spotlight innovations that translate molecular and spatial insights into decisions that improve outcomes, streamline pathways, and personalize therapy while addressing health-system realities such as cost, access, and workflow integration.

We welcome contributions spanning multi-omics assays (genomics, transcriptomics, epigenomics, proteomics, metabolomics), radiomics and pathomics, advanced imaging, liquid biopsy (ctDNA, cfRNA, CTCs, EVs), and AI-enabled decision support. Studies should emphasize analytical validity, clinical validity, and clinical utility—ideally in real-world or prospective settings—and clarify how new tests complement or outperform standard histopathology and tissue-based sequencing. Equally important are works that tackle pre-analytical variables, standardization, quality assurance, and regulatory readiness, including multicenter reproducibility and reporting frameworks.

Priority subthemes include:

o Early detection and minimal residual disease (MRD): sensitivity/specificity benchmarks, longitudinal surveillance, and implications for adjuvant therapy.

o Spatial biology and digital pathology: multiplex imaging, spatial transcriptomics, and AI/pathomics for tumor–immune microenvironment profiling and response prediction.

o Radiomics and imaging biomarkers: harmonization across scanners, multimodal fusion with molecular data, and predictive models for treatment selection.

o Liquid biopsy in hard-to-biopsy or heterogeneous tumors: resistance evolution, tumor dynamics, and actionable alterations in real time.

o Companion diagnostics and theranostics: targeted, immuno-, cellular, and radiopharmaceutical therapies; adaptive trial designs and biomarker-driven endpoints.

o Equity, implementation science, and cost-effectiveness: pragmatic pathways for community oncology, LMIC settings, and value-based care.

o Data standards and interpretability: transparent AI, federated learning, and clinically meaningful reporting that supports tumor boards and shared decision-making.

Submissions that include rigorous methodology, transparent datasets and code, and clear clinical endpoints are strongly encouraged. We particularly seek cross-disciplinary efforts linking oncologists, pathologists, radiologists, molecular scientists, data scientists, and health economists to close the gap between diagnostic innovation and improved patient survival and quality of life. By curating evidence across these fronts, this Research Topic aims to chart practical, scalable routes for integrating next-generation diagnostics into routine management of solid tumors.

Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.

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New Horizons in the Landscape of Solid Tumor Diagnostics for Improved Cancer Management