Radiomics and liquid biopsy in oncology: the holons of systems medicine

Insights into Imaging (2018) 9:915–924 https://doi.org/10.1007/s13244-018-0657-7 REVIEW Radiomics and liquid biopsy in oncology: the holons of systems medicine Emanuele Neri 1 & Marzia Del Re 2 & Fabiola Paiar 3 & Paola Erba 4 & Paola Cocuzza 3 & Daniele Regge 5 & Romano Danesi 2 Received: 14 May 2018 / Revised: 10 August 2018 / Accepted: 28 August 2018 / Published online: 14 November 2018 # The Author(s) 2018 Abstract Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. Liquid biopsy is a test done on a sample of blood to look for cancer cells or for pieces of tumourigenic DNA circulating in the blood. Radiomics and liquid biopsy have great potential in oncology, since both are minimally invasive, easy to perform, and can be repeated in patient follow-up visits, enabling the extraction of valuable information regarding tumour type, aggressiveness, progression, and response to treatment. Both methods are in their infancy, with major evidence of application in lung and gastrointestinal cancer, while still undergoing evaluation in other cancer types. In this paper, the main oncologic applications of radiomics and liquid biopsy are reviewed, and a synergistic approach incorporating both tests for cancer diagnosis and follow-up is discussed within the context of systems medicine. Teaching Points • Radiomics is a process of extraction and analysis of quantitative features from diagnostic images. • Most clinical applications of radiomics are in the field of oncologic imaging. • Radiomics applies to all imaging modalities. • A cluster of radiomic features is a Bradiomic signature^. • Machine learning may improve the efficacy of radiomics analysis. Keywords Imaging biomarkers . Imaging biobanks . Radiomics . Liquid biopsy . Personalised medicine Introduction In the last few years, the term Bradiomics^ has emerged in the imaging community as a novel field of research, defined by Lambin et al. as a Bhigh-throughput extraction of image features from radiographic images^ [1]. Radiomics is the discipline that deals with the extraction and analysis of quantitative features from diagnostic images [2]. The basis of radiomics is that such extracted features are * Emanuele Neri 1 Diagnostic and Interventional Radiology, Department of Translational Research, University of Pisa, Pisa, Italy 2 Clinical Pharmacology and Pharmacogenetics Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy the phenotype, the image quantitative expression of pathophysiological processes that can also be expressed by other Bomics^ including genomics, transcriptomics, metabolomics, and proteomics [3, 4]. Examples of features that can be extracted by radiomics analysis include shape/size-based, histogram-based, filteringbased, and texture analysis [5]. Texture analysis represents a highly promising feature extraction method that is largely based on the so called Haralick 3 Radiation Oncology Unit, Department of Translational Research, University of Pisa, Pisa, Italy 4 Nuclear Medicine Unit, Department of Translational Research, University of Pisa, Pisa, Italy 5 Radiology Unit, Candiolo Cancer Institute - FPO, IRCCS, Candiolo, Turin, Italy 916 Insights Imaging (2018) 9:915–924 method [6]. An example of texture analysis in MRI of locally advanced rectal cancer is shown in Fig. 1. Radiomics is therefore a process of extracting features from diagnostic images, as in other Bomics^ fields, but where the final product is a quantitative feature/parameter, measurable and minable. The concept of quantitative features is combined with that of Bimaging biomarkers^, defined in the white paper from the European Society of Radiology as Bcharacteristics that are objectively measured as indicators of normal biological processes, pathological changes, or pharmaceutical responses to a therapeutic intervention^ [7]. Thus, through a conceptual combination of the two definitions, which can be subject to interpretation, radiomics is a process that enables the extraction of imaging biomarkers from medical images. Radiomics are features that can be extracted only by computer algorithms, and cannot be derived by human visual assessment. This is the main Badvantage^ of quantitative analysis. However, extensive development and clinical validation of radiomic features is needed, and to date, the singular validated method of interpretation in clinical practice, with all the limitations and advantages of the human brain, is still the visual assessment. The high inter-reader agreement among radiologists in image interpretation supports the reliability of qualitative assessment, and may therefore represent a standard of reference for the development and validation of quantitative analysis integrating other Bomics^ and clinical data [8]. Numerous scientific advances have been made in the field of radiomics, and a literature review of the term Bradiomic^ (at the time of this review preparation) shows that over the 6-year period from 2012 to 2018, the number of publications including such a term has grown exponentially (Fig. 2). Radiomics applications in oncology To date, the vast majority of papers published about radiomics refer to oncologic applications. Aerts et al. performed CT radiomics analysis of tumour phenotypes in 1019 patients with lung and head and neck cancers, and found 440 features (among image intensity, shape, and texture) with a potential prognostic value that may have an impact in clinical practice [3]. One important group of features that can be extracted by the radiomic process is tumour heterogeneity, quantifiable by texture analysis. In a study by Leijnaar et al., radiomics analysis of positron emission tomography-computed tomography (PET/CT) data in patients with lung cancer who underwent repeated scans enabled the extraction of multiple texture features that showed high test–retest (71%) and inter-observer 3) Texture analysis report 1) Segmentaon 2) Features extracon Autocorrelaon 960 Maximum probability 0.01 Cluster prominence 311282.70 Sum average 60.01 Cluster Shade 1012.60 Sum Entropy 4.08 Contrast 51.24 Sum os Square Variance 82.21 Correlaon 0.70 Sum Variance 293.27 Difference Entropy 2.66 Kurtosis 3.76 Difference Variance 22.01 Skweness 0.16 Dissimilarity 5.34 Pixel Intensity Means 147.06 Energy 0.00 Pixel Intensity Std. 30.82 Entropy 5.63 Pixel Intensity Median 145 Homogeneity 0.18 Pixel Intensity P25 129.75 Informaon measure of Correlaon 1 -0.40 Pixel Intensity P75 164 Informaon measure of Correlaon 1 0.97 D2D 1.42 Fig. 1 Example of texture analysis in MRI of rectal cancer performed with QUIBIM software (QUIBIM S.L., Valencia, Spain). The region of interest for the texture is defined by manual segmentation (1). The texture model is extracted by the software through a grey-level co-occurrence matrix analysis (2) that enables the extraction of a set o (...truncated)