Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype

被引:81
作者
Fornacon-Wood, Isabella [1 ]
Faivre-Finn, Corinne [1 ,2 ]
O'Connor, James P. B. [1 ,3 ]
Price, Gareth J. [1 ]
机构
[1] Univ Manchester, Div Canc Sci, Wilmslow Rd, Manchester M20 4BX, Lancs, England
[2] Christie Hosp NHS Fdn Trust, Dept Radiat Oncol, Manchester, Lancs, England
[3] Christie Hosp NHS Fdn Trust, Dept Radiol, Manchester, Lancs, England
关键词
Radiomics; Imaging biomarkers; Lung cancer; Personalized medicine; INTEROBSERVER DELINEATION VARIABILITY; LYMPH-NODE METASTASIS; FEATURE STABILITY; PATHOLOGICAL RESPONSE; HISTOLOGIC SUBTYPE; RADIATION-THERAPY; CT IMAGES; STAGE I; FEATURES; PREDICTION;
D O I
10.1016/j.lungcan.2020.05.028
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.
引用
收藏
页码:197 / 208
页数:12
相关论文
共 129 条
[31]   Radiomics-based features for pattern recognition of lung cancer histopathology and metastases [J].
Ferreira Junior, Jose Raniery ;
Koenigkam-Santos, Marcel ;
Garcia Cipriano, Federico Enrique ;
Fabro, Alexandre Todorovic ;
de Azevedo-Marques, Paulo Mazzoncini .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2018, 159 :23-30
[32]   CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms [J].
Ferreira-Junior, Jose Raniery ;
Koenigkam-Santos, Marcel ;
Magalhaes Tenorio, Ariane Priscilla ;
Faleiros, Matheus Calil ;
Garcia Cipriano, Federico Enrique ;
Fabro, Alexandre Todorovic ;
Nappi, Janne ;
Yoshida, Hiroyuki ;
De Azevedo-Marques, Paulo Mazzoncini .
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2020, 15 (01) :163-172
[33]   Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform [J].
Fornacon-Wood, Isabella ;
Mistry, Hitesh ;
Ackermann, Christoph J. ;
Blackhall, Fiona ;
McPartlin, Andrew ;
Faivre-Finn, Corinne ;
Price, Gareth J. ;
O'Connor, James P. B. .
EUROPEAN RADIOLOGY, 2020, 30 (11) :6241-6250
[34]   Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis [J].
Foy, Joseph J. ;
Armato, Samuel G., III ;
Al-Hallaq, Hania A. .
JOURNAL OF MEDICAL IMAGING, 2020, 7 (01)
[35]   A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer. [J].
Franceschini, Davide ;
Cozzi, Luca ;
De Rose, Fiorenza ;
Navarria, Pierina ;
Fogliata, Antonella ;
Franzese, Ciro ;
Pezzulla, Donato ;
Tomatis, Stefano ;
Reggiori, Giacomo ;
Scorsetti, Marta .
STRAHLENTHERAPIE UND ONKOLOGIE, 2020, 196 (10) :922-931
[36]   Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies [J].
Ger, Rachel B. ;
Zhou, Shouhao ;
Chi, Pai-Chun Melinda ;
Lee, Hannah J. ;
Layman, Rick R. ;
Jones, A. Kyle ;
Goff, David L. ;
Fuller, Clifton D. ;
Howell, Rebecca M. ;
Li, Heng ;
Stafford, Jason ;
Court, Laurence E. ;
Mackin, Dennis S. .
SCIENTIFIC REPORTS, 2018, 8
[37]   The biology underlying molecular imaging in oncology: from genome to anatome and back again [J].
Gillies, R. J. ;
Anderson, A. R. ;
Gatenby, R. A. ;
Morse, D. L. .
CLINICAL RADIOLOGY, 2010, 65 (07) :517-521
[38]   Radiomics: Images Are More than Pictures, They Are Data [J].
Gillies, Robert J. ;
Kinahan, Paul E. ;
Hricak, Hedvig .
RADIOLOGY, 2016, 278 (02) :563-577
[39]   Defining the biological basis of radiomic phenotypes in lung cancer [J].
Grossmann, Patrick ;
Stringfield, Olya ;
El-Hachem, Nehme ;
Bui, Marilyn M. ;
Velazquez, Emmanuel Rios ;
Parmar, Chintan ;
Leijenaar, Ralph T. H. ;
Haibe-Kains, Benjamin ;
Lambin, Philippe ;
Gilles, Robert J. ;
Aerts, Hugo J. W. L. .
ELIFE, 2017, 6
[40]   Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer [J].
Gu, Qianbiao ;
Feng, Zhichao ;
Liang, Qi ;
Li, Meijiao ;
Deng, Jiao ;
Ma, Mengtian ;
Wang, Wei ;
Liu, Jianbin ;
Liu, Peng ;
Rong, Pengfei .
EUROPEAN JOURNAL OF RADIOLOGY, 2019, 118 :32-37