Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising

Lee, Jina; Jeon, Jiyeon; Hong, Yong Woo; Jeong, Dawun; Jang, Yeonggul; Jeon, Bo-Young; Baek, Hye Jin; Cho, Eun; Shim, Hackjoon; Chang, Hyuk Jae

doi:10.1016/j.compbiomed.2023.106931

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…For these reasons, whether it is necessary to compare radiomics data deriving from different scanners or from multiple institutions, it is of utmost importance to provide a post-processing step, in order to reduce potential bias. Different methods were proposed, including denoising [15], N4 bias field correction [16], voxel size resampling and interpolation, discretization [17], and ComBat harmonization [18]. Technical information regarding these aspects are out of the scope of this present review.…”

Section: Application Of Radiomics In the Female Pelvis: From Segmenta...mentioning

confidence: 99%

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

Franco,

Vernuccio,

Maino

et al. 2023

Applied Sciences

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Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. In recent years, a number of studies have been published regarding different possible applications of radiomics in gynaecological imaging. Many fields have been explored, such as tumour diagnosis and staging, differentiation of histological subtypes, assessment of distant metastases, prediction of response to therapy, recurrence, and patients’ outcome. However, several studies are not robust, do not include validation cohorts, or lack reproducibility. On these bases, the purpose of this narrative review is to provide an overview of the most relevant studies in the literature on radiomics in gynaecological imaging. We focused on gynaecological malignancies, particularly endometrial, cervical, mesenchymal, and ovarian malignant pathologies.

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