无线电技术
放射基因组学
医学
个性化医疗
成像生物标志物
特征选择
精密医学
生物标志物
医学影像学
人工智能
肿瘤科
医学物理学
内科学
计算机科学
放射科
生物信息学
磁共振成像
病理
生物
生物化学
作者
Ashish Kumar Jha,Sneha Mithun,Nilendu Purandare,Rakesh Kumar,Venkatesh Rangarajan,Leonard Wee,André Dekker
出处
期刊:Nuclear Medicine Communications
[Ovid Technologies (Wolters Kluwer)]
日期:2022-02-07
卷期号:43 (5): 483-493
被引量:8
标识
DOI:10.1097/mnm.0000000000001543
摘要
Cancer treatment is heading towards precision medicine driven by genetic and biochemical markers. Various genetic and biochemical markers are utilized to render personalized treatment in cancer. In the last decade, noninvasive imaging biomarkers have also been developed to assist personalized decision support systems in oncology. The imaging biomarkers i.e., radiomics is being researched to develop specific digital phenotype of tumor in cancer. Radiomics is a process to extract high throughput data from medical images by using advanced mathematical and statistical algorithms. The radiomics process involves various steps i.e., image generation, segmentation of region of interest (e.g. a tumor), image preprocessing, radiomic feature extraction, feature analysis and selection and finally prediction model development. Radiomics process explores the heterogeneity, irregularity and size parameters of the tumor to calculate thousands of advanced features. Our study investigates the role of radiomics in precision oncology. Radiomics research has witnessed a rapid growth in the last decade with several studies published that show the potential of radiomics in diagnosis and treatment outcome prediction in oncology. Several radiomics based prediction models have been developed and reported in the literature to predict various prediction endpoints i.e., overall survival, progression-free survival and recurrence in various cancer i.e., brain tumor, head and neck cancer, lung cancer and several other cancer types. Radiomics based digital phenotypes have shown promising results in diagnosis and treatment outcome prediction in oncology. In the coming years, radiomics is going to play a significant role in precision oncology.
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