Radiomic Phenotype-Informed Patient-Level Aggregation of Multiple Lesions: Application to PET Prognostic Modeling in NET and DLBCL

Abstract Purpose: To develop and validate a multilesion hierarchical radiomic phenotyping and aggregation framework for positron emission tomography (PET) imaging that captures intertumor heterogeneity and improves prognostic modeling in metastatic cancers.

Methods

This retrospective study included 81 neuroendocrine tumor (NET) patients imaged with 68Ga-DOTATATE or 68Ga-DOTATOC PET/CT and 76 diffuse large B-cell lymphoma (DLBCL) patients imaged with 18F-FDG PET/CT. Lesion-level radiomic features were extracted using PyRadiomics v3.0.1 and harmonized using ComBat. Unsupervised consensus clustering identified radiomic phenotypes, which were aggregated at four levels: body-level (B), phenotype-specific body-level (BP), organ-level (O), and phenotype-specific organ-level (OP). Nested cross-validation models predicted overall survival (OS), time to progression (TTP), and binary progression, comparing phenotype-agnostic and phenotype-informed aggregation, with and without clinical features, against single-lesion approaches and clinical baselines.