无线电技术
叙述的
叙述性评论
乳腺癌
肺癌
电流(流体)
医学
医学物理学
重症监护医学
癌症
肿瘤科
内科学
放射科
哲学
语言学
工程类
电气工程
作者
Alessandra Ferro,Michele Bottosso,Maria Vittoria Dieci,Elena Scagliori,Federica Miglietta,Vittoria Aldegheri,Stefano Indraccolo,Francesca Caumo,Valentina Guarneri,Gaia Griguolo,Giulia Pasello
标识
DOI:10.1016/j.critrevonc.2024.104479
摘要
Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients' history. However, the incorporation of radiomics in clinical decision-making process is still limited by low data reproducibility and study variability. Moreover, the need for prospective validations and standardizations is emerging. In this narrative review, we summarize current evidence regarding radiomic applications in high-incidence cancers (breast and lung) for screening, diagnosis, staging, treatment choice, response, and clinical outcome evaluation. We also discuss pro and cons of the radiomic approach, suggesting possible solutions to critical issues which might invalidate radiomics studies and propose future perspectives.
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