Radiomics: a quantitative imaging biomarker in precision oncology

被引:15
作者
Jha, Ashish Kumar [1 ,2 ,3 ]
Mithun, Sneha [1 ,2 ,3 ]
Purandare, Nilendu C. [2 ,3 ]
Kumar, Rakesh [4 ]
Rangarajan, Venkatesh [2 ,3 ]
Wee, Leonard [1 ]
Dekker, Andre [1 ]
机构
[1] Maastricht Univ, Med Ctr, GROW Sch Oncol, Dept Radiat Oncol Maastro, NL-6229 ET Maastricht, Netherlands
[2] Tata Mem Hosp, Dept Nucl Med & Mol Imaging, Mumbai, Maharashtra, India
[3] Homi Bhabha Natl Inst HBNI Univ, Mumbai, Maharashtra, India
[4] All India Inst Med Sci, Dept Nucl Med, New Delhi, India
关键词
artificial intelligence; imaging biomarker; precision oncology; radiomics; PATHOLOGICAL COMPLETE RESPONSE; LYMPH-NODE METASTASIS; ARTIFICIAL-INTELLIGENCE; RADIATION-THERAPY; TEXTURE ANALYSIS; GASTRIC-CANCER; F-18-FDG PET; PREDICTION; SIGNATURE; FEATURES;
D O I
10.1097/MNM.0000000000001543
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
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.
引用
收藏
页码:483 / 493
页数:11
相关论文
共 102 条
[1]   Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC [J].
Aerts, Hugo J. W. L. ;
Grossmann, Patrick ;
Tan, Yongqiang ;
Oxnard, Geoffrey G. ;
Rizvi, Naiyer ;
Schwartz, Lawrence H. ;
Zhao, Binsheng .
SCIENTIFIC REPORTS, 2016, 6
[2]   From Handcrafted to Deep-Learning-Based Cancer Radiomics Challenges and opportunities [J].
Afshar, Parnian ;
Mohammadi, Arash ;
Plataniotis, Konstantinos N. ;
Oikonomou, Anastasia ;
Benali, Habib .
IEEE SIGNAL PROCESSING MAGAZINE, 2019, 36 (04) :132-160
[3]   Radiomic Features on MRI Enable Risk Categorization of Prostate Cancer Patients on Active Surveillance: Preliminary Findings [J].
Algohary, Ahmad ;
Viswanath, Satish ;
Shiradkar, Rakesh ;
Ghose, Soumya ;
Pahwa, Shivani ;
Moses, Daniel ;
Jambor, Ivan ;
Shnier, Ronald ;
Bohm, Maret ;
Haynes, Anne-Maree ;
Brenner, Phillip ;
Delprado, Warick ;
Thompson, James ;
Pulbrock, Marley ;
Purysko, Andrei S. ;
Verma, Sadhna ;
Ponsky, Lee ;
Stricker, Phillip ;
Madabhushi, Anant .
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2018, 48 (03) :818-828
[4]   Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes [J].
Altazi, Baderaldeen A. ;
Fernandez, Daniel C. ;
Zhang, Geoffrey G. ;
Hawkins, Samuel ;
Naqvi, Syeda M. ;
Kim, Youngchul ;
Hunt, Dylan ;
Latifi, Kujtim ;
Biagioli, Matthew ;
Venkata, Puja ;
Moros, Eduardo G. .
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2018, 46 :180-188
[5]  
[Anonymous], QUANT IM BIOM ALL QI
[6]   Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework [J].
Atkinson, AJ ;
Colburn, WA ;
DeGruttola, VG ;
DeMets, DL ;
Downing, GJ ;
Hoth, DF ;
Oates, JA ;
Peck, CC ;
Schooley, RT ;
Spilker, BA ;
Woodcock, J ;
Zeger, SL .
CLINICAL PHARMACOLOGY & THERAPEUTICS, 2001, 69 (03) :89-95
[7]   Radiomics and deep learning in lung cancer [J].
Avanzo, Michele ;
Stancanello, Joseph ;
Pirrone, Giovanni ;
Sartor, Giovanna .
STRAHLENTHERAPIE UND ONKOLOGIE, 2020, 196 (10) :879-887
[8]   Texture-based classification of different gastric tumors at contrast-enhanced CT [J].
Ba-Ssalamah, Ahmed ;
Muin, Dina ;
Schernthaner, Ruediger ;
Kulinna-Cosentini, Christiana ;
Bastati, Nina ;
Stift, Judith ;
Gore, Richard ;
Mayerhoefer, Marius E. .
EUROPEAN JOURNAL OF RADIOLOGY, 2013, 82 (10) :E537-E543
[9]   Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging [J].
Bai, Yan ;
Lin, Yusong ;
Tian, Jie ;
Shi, Dapeng ;
Cheng, Jingliang ;
Haacke, E. Mark ;
Hong, Xiaohua ;
Ma, Bo ;
Zhou, Jinyuan ;
Wang, Meiyun .
RADIOLOGY, 2016, 278 (02) :496-504
[10]   Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: A pilot study [J].
Bakr, Shaimaa ;
Echegaray, Sebastian ;
Shah, Rajesh ;
Kamaya, Aya ;
Louie, John ;
Napel, Sandy ;
Kothary, Nishita ;
Gevaert, Olivier .
Journal of Medical Imaging, 2017, 4 (04)