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Radiomics systematic review in cervical cancer: gynecological oncologists’ perspective

医学 无线电技术 宫颈癌 放射科 透视图(图形) 妇科 医学物理学 梅德林 癌症 内科学 人工智能 政治学 计算机科学 法学
作者
Nicolò Bizzarri,Luca Russo,Miriam Dolciami,Konstantinos Zormpas‐Petridis,Luca Boldrini,Denis Querleu,Gabriella Ferrandina,Luigi Pedone Anchora,Benedetta Gui,Evis Sala,Giovanni Scambia
出处
期刊:International Journal of Gynecological Cancer [BMJ]
卷期号:33 (10): 1522-1541 被引量:5
标识
DOI:10.1136/ijgc-2023-004589
摘要

Objective Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. Methods A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. Results A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. Conclusion Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.
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