SAFARI: A Self-supervised Tumor-Agnostic Framework for Radiomics Representation Learning in Oncology Practice

Conventional radiomics is hindered by manual feature engineering and poor cross-tumor generalization. We introduce SAFARI (Self-supervised Tumor-Agnostic Framework for Radiomics Representation Learning), a novel, self-supervised model for radiomics that learns disease agnostic tumor characteristics. Built on a transformer architecture with a dual-objective self-supervised strategy, combining masked feature reconstruction and contrastive learning across tumor subvolumes, SAFARI was trained on 1,218 radiomic features from 27,144 tumor subvolumes across 1,086 patients with six major cancer types (lung, liver, colorectal, kidney, pancreatic, and colorectal liver metastases). In transfer learning experiments on independent datasets comprising 762 patients, fixed SAFARI embeddings outperformed our matched conventional radiomics baselines across multiple downstream tasks. Performance gains were substantial for predicting HER2 status in gallbladder cancer (AUC: 0.758 vs. 0.610), NSCLC histopathology (accuracy: 71.3% vs. 62.1%), and EGFR mutation status (AUC: 0.805 vs. 0.690). By demonstrating that a single pretrained model outperforms matched task-specific baselines on the majority of endpoints, SAFARI establishes a new, more robust paradigm for radiomic analysis in oncology.