Traditional imaging modalities often lack the molecular specificity and spatial resolution required for real-time tumor visualization

particularly in complex surgical settings. This narrative review examines the emerging class of human NAD(P)H:quinone oxidoreductase 1 (hNQO1)-activatable fluorescent probes operating within the near-infrared-II (NIR-II

1

000–1

700 nm) and near-infrared-III (NIR-III

1

700–2

500 nm) windows

which offer transformative potential for tumor imaging and surgical guidance. hNQO1

overexpressed in various malignancies

including pancreatic

lung

and breast cancers

while minimally present in normal tissues

serves as an ideal biomarker for selective probe activation. However

its residual expression in certain normal tissues

such as the kidney and vascular endothelium

necessitates careful probe design to minimize off-target activation. This review systematically explores the design principles underlying hNQO1-activatable probes

emphasizing structural features that enable enzyme-specific fluorescence activation with minimal background interference. Chemical synthesis strategies

including quinone propionate-based self-immolative linkers and advanced bioconjugation techniques

are discussed alongside optimization approaches to enhance probe stability

sensitivity

and biocompatibility. The review highlights clinical applications in real-time tumor visualization

accurate margin delineation

early cancer detection

and therapeutic monitoring. Significantly

the integration of artificial intelligence (AI) and deep learning frameworks for automated image analysis

tumor segmentation

and radiomics-based feature extraction represents a critical advancement

transforming qualitative fluorescence signals into quantitative

clinically actionable data. Crucially

AI-derived quantitative biomarkers extracted from hNQO1-activated fluorescence

such as spatial heterogeneity indices

activation kinetics

and texture features

can be linked to clinical endpoints including treatment response prediction

recurrence risk stratification

and overall survival

providing a data-driven foundation for personalized oncology decision-making. Translational challenges including regulatory pathways

pharmacokinetics

safety considerations

model validation requirements

and real-world clinical deployment frameworks are addressed

alongside future perspectives on theranostic applications and multimodal imaging integration. Together

hNQO1-activatable NIR-II probes combined with intelligent computational systems represent a paradigm shift toward data-driven

high-precision cancer care.