Emerging prospects of radiomics in the management of ovarian cancer:from diagnosis to treatment
Radiomics is a burgeoning discipline that centers on the extraction of quantitative imaging attributes from medical images,with the aim of augmenting the diagnosis,prognosis,and treatment of cancer.This comprehensive analysis underscores the various applications of radiomics in the management of ovarian cancer.Through the extraction of numerous shape,intensity,and texture attributes,radiomics facilitates a data-centric approach to image analysis.When employed alongside machine learning,radiomics has demonstrated the ability to effectively categorize ovarian tumors,forecast outcomes,evaluate heterogeneity,and anticipate gene expression patterns via radiogenomics.Various investigations have employed computed tomography(CT),magnetic resonance imaging(MRI),and ultrasound methodologies to establish radiomic biomarkers capable of distinguishing between benign,borderline,and malignant ovarian tumors with remarkable precision.Deep learning techniques have been utilized to augment radiomic analyses,facilitating automated feature acquisition.Nonetheless,the field of radiomics is still nascent and necessitates thorough validation of its clinical efficacy prior to its widespread implementation and integration into current workflows.In essence,radiomics presents a promising avenue in the era of precision medicine for ovarian cancer;however,larger-scale multicenter trials are imperative to fully harness its potential in enhancing patient care.
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