Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome

Abstract

To evaluate the repeatability of MRI and CT derived texture features and to investigate the feasibility of use in predictive single and multi-modality models for radiotherapy of non-small cell lung cancer, 59 texture features were extracted from unfiltered and wavelet filtered images. Repeatability of test-retest features from helical 4D CT scans, true fast MRI with steady state precession (TRUFISP), and volumetric interpolation breath-hold examination (VIBE) was determined by the concordance correlation coefficient (CCC). A workflow was developed to predict overall survival at 12, 18, and 24 months and tumour response at end of treatment for tumour features, and normal muscle tissue features as a control. Texture features were reduced to repeatable and stable features before clustering. Cluster representative feature selection was performed by univariate or medoid analysis before model selection. P-values were corrected for false discovery rate. Repeatable (CCC ⩾ 0.9) features were found for both tumour and normal muscle tissue: CT: 54.4% for tumour and 78.5% for normal tissue, TRUFISP: 64.4% for tumour and 67.8% for normal tissue, and VIBE: 52.6% for tumour and 72.9% for normal muscle tissue. Muscle tissue control analysis found seven significant models with six of seven models utilizing the univariate representative feature selection technique. Tumour analysis revealed 12 significant models for overall survival and none for tumour response at end of treatment. The accuracy of significant single modality was about the same for MR and CT. Multi-modality tumour models had comparable performance to single modality models. MR derived texture features may add value to predictive models and should be investigated in a larger cohort. Control analysis demonstrated that the medoid representative feature selection method may result in more robust models.

Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer

“…Therefore, it is very crucial to accurately confirm the histological subtype of the NSCLC prior to the treatment decisions [6]. Clinically, the histopathological analysis of the tumor tissues by biopsy is the first-line reference in identifying the NSCLC subtypes [4][5][6][7][8]. It is an invasive diagnostic process and full of risk in actual practices [6].…”

Image Contrast, Image Pre-Processing, and T1 Mapping Affect MRI Radiomic Feature Repeatability in Patients with Colorectal Cancer Liver Metastases

“…By assessing MR radiomic feature repeatability using two different metrics in a relatively large clinical cohort, investigating the effects of MR sequence, image normalisation, and assumptions about feature distributions, this work contributes to the technical validation of radiomic features. By focussing on liver metastases, and using quantitative T maps and post-contrast T W images, this work complements existing repeatability studies using other MR sequences in other tumour types [ 12 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

“…In terms of technical validation, measuring repeatability in single centres and reproducibility across multiple centres is crucial, and provides an important step in developing metrology standards for quantitative imaging biomarkers in general [ 9 ], including radiomic features [ 10 ]. While a number of studies have assessed the repeatability and/or reproducibility of CT-derived cancer radiomic features [ 11 ], there are fewer studies investigating the repeatability of MR-derived radiomic features [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. These studies are limited by small patient numbers (≤17, with the exception of Kickingereder et al, [ 13 ] and Merisaari et al [ 20 ], with 55 and 112 patients, respectively), and the use of only one or two MR sequences (except in [ 13 ] where three were used).…”

The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges

“…A recent study compared the repeatability of CT and MR (using volumetric interpolation breath-hold examination (VIBE) and true fast MRI with steady state precession (TRUFISP) texture features of non-small cell lung cancer and showed 12 significant models that accurately predict overall survival but not tumor response. CT and MRI had a fairly similar predictive accuracy; 54.4% of CT texture features, 64.4% of TRUFISP and 52.6% of VIBE texture features were reproducible with a concordance correlation coefficient of ≥0.9 [23]. However as mentioned previously, simulation of the groundtruth textural composition of tissues of MR images can be more difficult, since the image signal intensities of tissues are strongly influenced by the MR acquisition parameters; moreover, images are more prone to artifacts that affect the quantitative analysis of texture features (especially the Gibbs ringing) compared to CT [14].…”