<p>Histochemical staining is indispensable for histopathological diagnosis, enabling interpretation of cellular and tissue architecture and lesion patterns. Deep learning–based virtual staining has rapidly expanded across label-free and stain-to-stain settings by learning data-driven transformations to synthesize stain-like contrast or additional stain representations, with the potential to reduce turnaround time and the need for additional tissue sections and repeated staining. Yet standardization remains limited across studies. Inconsistent acquisition, data curation, and preprocessing, model configuration, and evaluation reduce reproducibility, hinder fair cross-study comparison, and weaken clinical translation. In this review, we present a standardization blueprint that organizes the field of modality-specific data construction and model design to domain shift, evaluation, safety, and clinical translation. We propose a minimum reporting checklist to support consistent disclosure of datasets, protocols, and evaluation settings for reproducible benchmarking. Finally, we discuss clinical translation and regulatory considerations by situating virtual staining within real pathology workflows, clarifying why full replacement is not yet routine in practice, and highlighting key evidence gaps that must be addressed for safe, credible deployment. Collectively, these contributions advance standardization in deep learning–based virtual staining by enabling more reproducible reporting, more comparable benchmarking, and more clinically grounded assessment.</p>