Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Abstract

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

Figure 1. Extracting radiomics data from images.

(a) Tumours are different. Example computed tomography (CT) images of lung cancer patients. CT images with tumour contours left, three-dimensional visualizations right. Please note strong phenotypic differences that can be captured with routine CT imaging, such as intratumour heterogeneity and tumour shape. (b) Strategy for extracting radiomics data from images. (I) Experienced physicians contour the tumour areas on all CT slices. (II) Features are extracted from within the defined tumour contours on the CT images, quantifying tumour intensity, shape, texture and wavelet texture. (III) For the analysis the radiomics features are compared with clinical data and gene-expression data.

Figure 2. Analysis workflow.

The defined radiomic features algorithms were applied to seven different data sets. Two data sets were used to calculate the feature stability ranks, RIDER test/retest and multiple delineation respectively (both orange). The Lung1 data set, containing data of 422 non-small cell lung cancer (NSCLC) patients, was used as training data set. Lung2 (n=225), H&N1 (n=136) and H&N2 (n=95) were used as validation data sets. The Lung3 data set (n=89) was used for association of the radiomic signature with gene expression profiles. For the multivariate analysis, only one fixed four-feature radiomic signature was tested in the validation data sets.

Figure 3. Radiomics heat map.

(a) Unsupervised clustering of lung cancer patients (Lung1 set, n=422) on the y axis and radiomic feature expression (n=440) on the x axis, revealed clusters of patients with similar radiomic expression patterns. (b) Clinical patient parameters for showing significant association of the radiomic expression patterns with primary tumour stage (T-stage; P<1 × 10⁻²⁰, χ² test), overall stage (P=3.4 × 10⁻³, χ² test) and histology (P=0.019, χ² test). (c) Correspondence of radiomic feature groups with the clustered expression patterns.

Figure 4. Prognostic performance and gene-expression association of the radiomics signature.

(a) Radiomic signature performance. Kaplan–Meier curves demonstrating performance of the radiomic signature on the lung cancer data sets (left) and the head-and-neck cancer data sets (right). The signature was built on the Lung1 data (n=422). The signature had a good performance in the Lung2 (CI=0.65, P=2.91 × 10⁻⁰⁹, Wilcoxon test, n=225), and a high performance in H&N1 (CI=0.69, P=7.99 × 10⁻⁰⁷, Wilcoxon test, n=136) and H&N2 (CI=0.69, P=3.53 × 10⁻⁰⁶, Wilcoxon test, n=95) validation data sets. (b) Association of radiomic signature features and gene expression using gene-set enrichment analysis (GSEA) in the Lung3 data set (n=89). Gene sets that have been significantly enriched (FDR=20%) for at least one of the four radiomic features are indicated with an asterisk. The corresponding normalized enrichment scores (NES), GSEA’s primary statistic, for all radiomic signature features is displayed in a heat map, where light blue means low and dark blue means high NES.