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Review
. 2024 Dec 14;11(1):77.
doi: 10.1186/s40779-024-00580-1.

Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer

Song Zeng  1 , Xin-Lu Wang  1 , Hua Yang  2
Affiliations
Review

Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer

Song Zeng et al. Mil Med Res. .

Abstract

Ovarian cancer (OC) remains one of the most lethal gynecological malignancies globally. Despite the implementation of various medical imaging approaches for OC screening, achieving accurate differential diagnosis of ovarian tumors continues to pose significant challenges due to variability in image performance, resulting in a lack of objectivity that relies heavily on the expertise of medical professionals. This challenge can be addressed through the emergence and advancement of radiomics, which enables high-throughput extraction of valuable information from conventional medical images. Furthermore, radiomics can integrate with genomics, a novel approach termed radiogenomics, which allows for a more comprehensive, precise, and personalized assessment of tumor biological features. In this review, we present an extensive overview of the application of radiomics and radiogenomics in diagnosing and predicting ovarian tumors. The findings indicate that artificial intelligence methods based on imaging can accurately differentiate between benign and malignant ovarian tumors, as well as classify their subtypes. Moreover, these methods are effective in forecasting survival rates, treatment outcomes, metastasis risk, and recurrence for patients with OC. It is anticipated that these advancements will function as decision-support tools for managing OC while contributing to the advancement of precision medicine.

Keywords: Deep learning; Machine learning; Ovarian cancer; Radiogenomics; Radiomics.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare that they have no competing interests. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable.

Figures

Fig. 1
Fig. 1
The development and workflow of radiomics and radiogenomics
Fig. 2
Fig. 2
General framework of radiomics. The process for radiomic research applying the machine learning (ML) method includes image acquisition, regions of interest (ROI) segmentation, feature extraction, feature selection, model training, and validation, whereas the deep learning (DL) method includes data preprocessing, convolution, activation, pooling, and full connection
Fig. 3
Fig. 3
The application of radiomics and radiogenomics in ovarian cancer (OC). The application of radiomics involves the diagnosis and prediction of OC, while the application of radiogenomics involves identifying abnormal genetic changes and predicting clinical outcomes in OC patients. CA125 cancer antigen 125, CT computed tomography, HE4 human epididymis protein 4, PET/CT positron emission tomography/CT, US ultrasound
See this image and copyright information in PMC

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