Radiomics: a quantitative imaging biomarker in precision oncology. Issue 5 (7th February 2022)

• Record Type: Journal Article
• Authors: Jha, Ashish Kumar; Mithun, Sneha; Purandare, Nilendu C.; Kumar, Rakesh; Rangarajan, Venkatesh; Wee, Leonard; Dekker, Andre
• Is Part Of: Nuclear medicine communications. Volume 43:Issue 5(2022)
• Page Start: 483
• Page End: 493
• Publication Date: 2022-02-07
• Subjects: artificial intelligence -- imaging biomarker -- precision oncology -- radiomics; Nuclear medicine -- Periodicals; 616.07575
• DOI: 10.1097/MNM.0000000000001543
• Languages: English
• ISSNs: 0143-3636

Abstract

Abstract : 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.