Artificial Intelligence for RECIST-Based Radiologic Treatment Response Assessment in Solid Tumors: A Systematic Review of Imaging- and Report-Derived Approaches

AI-based RECIST-oriented response assessment is feasible and potentially beneficial for standardization, efficiency, and scalability, but current evidence is limited and heterogeneous, requiring larger multi-center studies with rigorous external validation before clinical adoption. Key limitations include data source variability, reference standard inconsistencies, and lack of robust external validation.

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Publication Details

Journal
Cancers
Published
2026-03-02
DOI
https://doi.org/10.3390/cancers18050808
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
31.92
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article

Artificial Intelligence for RECIST-Based Radiologic Treatment Response Assessment in Solid Tumors: A Systematic Review of Imaging- and Report-Derived Approaches

Michał Seweryn, Paweł Potocki, A Leszczynska, Rafał Obuchowicz et al.
1 citations
Cancers
Radiomics and Machine Learning in Medical Imaging
31.92
article

Artificial Intelligence for RECIST-Based Radiologic Treatment Response Assessment in Solid Tumors: A Systematic Review of Imaging- and Report-Derived Approaches

Michał Seweryn, Paweł Potocki, A Leszczynska, Rafał Obuchowicz, Adam Piórkowski, Michał Strzelecki
article en
1 citations

Abstract

AI-based RECIST-oriented response assessment is feasible and potentially beneficial for standardization, efficiency, and scalability, but current evidence is limited and heterogeneous, requiring larger multi-center studies with rigorous external validation before clinical adoption. Key limitations include data source variability, reference standard inconsistencies, and lack of robust external validation.

CancersVol. 18(5)
Jagiellonian University (PL), Lodz University of Technology (PL), EC Systems (Poland) (PL), Andrzej Frycz Modrzewski Krakow University (PL), Lux Research (United States) (US), AGH University of Krakow (PL)
Quality Education
Openalex Percentile: Top 1%
Radiomics and Machine Learning in Medical Imaging
31.92
DOIOpen Access PDFOpenAlex
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Artificial Intelligence for RECIST-Based Radiologic Treatment Response Assessment in Solid Tumors: A Systematic Review of Imaging- and Report-Derived Approaches — Michał Seweryn, Paweł Potocki, et al. · Cancers (2026) | TGRS Research Map | TGRS