Radiomics-Guided Prediction of Histopathological Aggressiveness and Margin Status in Solid Organ Malignancies: A Comprehensive Systematic Review Study
Keywords:
Radiomics, Neoplasms, Diagnostic Imaging, Artificial Intelligence, Machine Learning, Neoplasm GradingAbstract
Background: Radiomics-guided imaging has become a promising non-invasive strategy to assess tumor biology characteristics in solid organ malignancies. Microvascular invasion, tumor grade, Ki-67 expression, Gleason Grade Group, ISUP grade, extracapsular extension, and margin status are all significant features that can be used to determine the aggressiveness of the condition, but are only confirmed after biopsy or surgery. This review aimed to summarize the evidence of the use of radiomics for prediction of the histopathological aggressiveness and margin-related outcome in solid organ cancers. Methods: This systematic review followed PRISMA 2020 guidelines. PubMed, Scopus, Web of Science, and Google Scholar databases were used to search for publications from January 2019 until February 2026. Studies that used full-text were included, and they were original studies using either CT, MRI, PET/CT, or ultrasound-based radiomics or machine-learning models. Eligible studies provided prediction of pathological aggressiveness, margin status or survival-related outcomes. Only articles reporting the results of a study with relevant outcomes were included, in addition to reviews, abstracts of conference presentations, non-English publications and non-solid tumor studies. The certainty of evidence was estimated with GRADE and risk of bias with QUADAS-2 and PROBAST. Results: 16 studies were included in the analysis from 512 records identified. The studies included in this were HCC, prostate cancer, renal cell cancer, PDA, and PNET. Predictive value of radiomics models was promising for the prediction of microvascular invasion, histological grade, ISUP/Fuhrman grade, Gleason Grade Group, Ki-67 expression, EXT, PSM, RS, and survival outcomes. The level of certainty was low to moderate, and the scores of most studies were medium. Conclusion: Radiomics-guided models could facilitate non-invasive prediction of tumor aggressiveness and margin-related outcomes but need to be validated in a prospective multicentre study and standardized radiomics workflows prior to their routine use in clinical practice.
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