Zhang et al. investigated using histological staining methods to assess subtypes and risks of small cell lung cancer (SCLC) to improve patient stratification and prognosis given the cancer’s heterogeneity. Using hematoxylin and eosin (H&E)–stained whole-slide images, they applied a deep learning model to identify histomorphological phenotypes (HIPOs) and characterize the diversity of tumor ecosystems, which allowed them to define subtypes based on patient HIPO features. CD46 immunohistochemistry, proteomics, and pathomics were then used to correlate these subtypes with biological and microenvironmental factors through single-sample gene set enrichment analysis (ssGSEA). In their cohort, the researchers identified 15 distinct HIPOs with consistent features such as normal bronchial cartilage (HIPO1), mucosal glands and fibrous tissue (HIPO3), and adjacent lung tissue (HIPOs 6 and 8), with variation in tumor purity, fibrosis, and necrosis. Each patient was profiled with a unique HIPO composition, and two broader subtypes, HIPOS-I and HIPOS-II, were distinguished by significant differences in their HIPO makeup. These subtypes also carried prognostic value, with HIPOS-I patients showing better overall (OS) and disease-free survival (DFS) compared to HIPOS-II patients. Unlike molecular classifications such as neuroendocrine and transcription factor–based subtypes, which showed no survival differences, HIPO-based subtypes provided meaningful stratification. The classifier also revealed stage-specific predictive patterns in tumor purity, necrosis, stromal bands, and lymphocyte infiltration within each subtype. Microenvironmental differences were further supported by analyses of xCell, immune activity, angiogenesis, fibroblast signatures, epithelial–mesenchymal transition, proliferation, and markers like CD45. These findings establish a framework that can subtype SCLC through image-based features and link morphology with microenvironment, allowing pathologists to identify meaningful histological patterns to improve prognosis and patient risk stratification.