THE EVOLVING ROLE OF IMAGING IN ONCOLOGY RESPONSE ASSESSMENT: BEYOND RECIST

Abstract

Background: The Response Evaluation Criteria in Solid Tumors (RECIST) framework has served as the cornerstone of oncologic response assessment for over two decades. However, the emergence of novel systemic therapies including immune checkpoint inhibitors, targeted molecular agents, and antibodydrug conjugates has exposed fundamental limitations of purely morphologic, sizebased evaluation. This review critically appraises the evolution of imagingbased response assessment in oncology and explores the expanding role of functional imaging, multiparametric magnetic resonance imaging, positron emission tomography/computed tomography beyond fluorodeoxyglucose, radiomics, and artificial intelligence in capturing the biological complexity of tumortreatment interactions. Methods: A comprehensive search of PubMed, MEDLINE, Embase, and Cochrane Library databases was performed (1979–2024) using MeSH terms: 'tumor response assessment,' 'RECIST,' 'iRECIST,' 'PERCIST,' 'radiomics,' 'pseudoprogression,' and 'oncologic imaging.' Randomized controlled trials, phase II/III clinical trials, systematic reviews, metaanalyses, and landmark observational studies were included. Results: RECIST 1.1 retains validity for conventional cytotoxic chemotherapy but inadequately captures response patterns under immunotherapy (pseudoprogression, hyperprogression), targeted therapies (cytostatic responses, tumor cavitation), and locoregional interventions. Disease and modalityspecific criteria (iRECIST, PERCIST, mRECIST, Choi, RANO) provide complementary frameworks. Radiomics and AIbased methods demonstrate promising discriminative accuracy (AUC 0.72–0.88) for response prediction and prognostication, pending external validation. Novel PET tracers, liquid biopsy integration, and theranostic imaging represent frontier technologies redefining the assessment paradigm. Conclusions: No single imaging framework captures the full spectrum of oncologic response in contemporary practice. A precision approach integrating morphologic, metabolic, functional, and molecular imaging with computational analytics is necessary to optimize treatment decisions and clinical trial endpoints. Standardization, prospective validation, and multidisciplinary consensus are prerequisites for clinical implementation.