CURRENT ISSUE

ABSTRACT: Spatial tumour heterogeneity, which denotes the changes in cellular and molecular attributes across distinct locations within a tumour, significantly influences cancer diagnosis and treatment resistance. The heterogeneity of tumour cells inside a singular mass facilitates tumour development, metastasis, and the ineffectiveness of standard therapy. Comprehending the geographical distribution of tumour cells is crucial for formulating more efficient treatment regimens. Diverse methodologies are employed to investigate spatial heterogeneity, encompassing modern imaging techniques such as MRI, PET, and multiplexed imaging, alongside omics approaches including genomes, transcriptomics, and proteomics. These instruments offer insights into the tumour microenvironment and facilitate the identification of resistant subpopulations. The amalgamation of imaging and genomic data via radiogenomics has emerged as a viable methodology, providing an extensive perspective on the spatial and molecular intricacies of tumours. Principal findings reveal that spatial heterogeneity fosters medication resistance by establishing microenvironments characterised by varying oxygen levels, immunological infiltration, and genetic alterations, hence complicating the efficacy of monotherapy strategies. Hypoxic environments and immunological evasion significantly contribute to treatment resistance. Addressing geographical heterogeneity has the potential to enhance cancer treatments. By analysing the molecular and geographical characteristics of tumours, physicians can customise therapies more efficiently, minimising resistance and improving therapeutic results. This methodology signifies a vital advancement in precision medicine, providing more individualised and efficacious cancer therapies in the future.

ABSTRACT: Recent advances in artificial intelligence—particularly Vision–Language Models (VLMs)—offer promising avenues for enhancing microscopic diagnostics. This review synthesizes the current landscape of VLM applications across microbiology, hematology, cytology, and histopathology, spanning tasks such as Gram stain classification, cell-type recognition, feature localization, captioning, and report drafting. We outline how VLMs integrate visual features with domain-specific prompts to support triage, decision support, and quality control, while highlighting opportunities for few-shot and zero-shot generalization to rare findings. In parallel, we compare conventional convolutional pipelines with VLM-enhanced workflows, emphasizing gains in scalability, reproducibility, and explainability through multimodal rationales and grounded visual evidence. Key challenges include data curation and harmonization across laboratories, domain shift from variable staining and optics, bias and safety risks, limited task-relevant benchmarks, and the need for rigorous human-in-the-loop evaluation in clinical contexts. We propose a practical roadmap for deployment—covering dataset governance, prompt and template standardization, uncertainty reporting, and audit trails—alongside research priorities in robust evaluation, privacy-preserving learning, and alignment with clinical guidelines. Overall, VLMs are poised to complement expert microscopy by accelerating routine workflows and improving documentation, provided their adoption is guided by transparent validation and fit-for-purpose governance.