Background/Objectives:
Multiplex immunofluorescence (mIF) is an emerging spatial biology technology enabling high‑resolution characterization of the tumor microenvironment and revealing spatial interactions that extend beyond cell‑count metrics. Reproducibility is limited by tumor heterogeneity, device variability and challenges in interpreting AI‑assisted cell profiling measurements. This study delineates a pathologist‑guided, error‑focused analytical framework intended to mitigate major sources of analytical variability across visualization, segmentation, and classification workflows.

Methods:

A framework was developed based on a literature review and pathologists' annotations for selecting regions of interest across intratumoral, peri‑/extratumoral, and tumor–stroma interface compartments in primary and recurrent bioptic, surgical, and cytological NSCLC samples analyzed entirely by mIF. Quality‑control procedures included morphological verification, adaptive thresholds, and exclusion or resampling of low‑cellularity or artifact‑rich tissue. Prespecified cellularity cutoffs ensured stability of spatial metrics, and several positional indices were evaluated. A validated mIF panel (CD3, CD4, CD8, CD20, MPO, CD21, CD23, CD206, CD45, CD56, CD11b, CD66b, CD68, CD31, SMA, PNAd, CK, PD‑1, PD‑L1, CTLA‑4, LAG‑3, TIM‑3, TCF1, GranzymeB, and FOXP3) enabled comprehensive profiling of lymphoid and myeloid lineages, including TAMs, as well as stromal, vascular, and immune‑checkpoint components. Immunophenotyping used pretrained deep‑learning and forest‑plot-based models with pathologist supervision to reduce classification and segmentation errors. Spatial analysis incorporated surrogate metrics of T‑cell activity across immune compartments, and internal positive controls were documented to monitor staining performance.

Results:
The workflow ensured consistent multicompartment acquisition of spatial and immunophenotypic data, reducing analytical vulnerabilities. Morphological validation improved the accuracy of AI‑assisted cell classification, and whole‑slide-based ROI selection addressed limitations typical of tissue microarrays.

Conclusions:

A structured, pathologist-guided mIF workflow integrating standardized sampling, rigorous quality control, and spatial analytics enhances analytical robustness and interpretability of errors in NSCLC TME profiling, offering a practical template for reproducible spatial immuno‑oncology studies and supporting clinical translation of mIF‑based TME analysis.