影像组学在卵巢癌管理中的应用:从诊断到治疗的新兴前景

Emerging prospects of radiomics in the management of ovarian cancer:from diagnosis to treatment

影像组学(radiomics)是一个新兴领域,专注于从医学图像中提取定量成像特征,以增强癌症的诊断、预后和治疗。本综述突出了影像组学在卵巢癌管理中的应用。通过提取大量形状、强度和纹理特征,影像组学实现了基于数据的图像分析。当与各类数据、算法结合时,影像组学能够准确地对卵巢癌进行诊断、预测和评估异质性,并通过影像基因组学预测基因表达模式。已有研究利用计算机断层扫描(computed tomography, CT)、磁共振成像(magnetic resonance imaging, MRI)和超声技术来开发影像组学标志物,以高准确度区分良性、边缘性和恶性卵巢癌。当深度学习技术被用来进一步增强影像组学分析,则实现自动化的特征学习。然而,影像组学仍然处于早期阶段,在广泛采用之前需要对其临床效用进行广泛验证,并将其整合到现有工作流程中。总体而言,影像组学代表了卵巢癌精准医学中一种有前途的方法;然而,更大规模的多中心试验对于充分发挥其在改善患者护理方面的潜力至关重要。

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.