Potential Complementary Value of Noncontrast and Contrast Enhanced CT Radiomics in Colorectal Cancers.

RATIONALE AND OBJECTIVES The aim of our study was to assess the relationships between textural features extracted from contrast enhanced (CE) and noncontrast enhanced (NCE) computed tomography (CT) images of primary colorectal cancer, in order to identify radiomics features more likely to provide potential complementary information regarding outcome. MATERIALS AND METHODS Sixty-one patients with primary colorectal cancer underwent both CE-CT and NCE-CT scans within the same acquisition. First-order and textural features (with three different methods for grey-level discretization) were extracted from the tumor volume in both modalities and their correlation was assessed with Spearman's rank correlation (rs). Significance was assessed at p < 0.05 with correction for multiple comparisons. Kaplan-Meier estimation and log-rank tests were used to identify features associated with long term patient survival. RESULTS Moderate positive correlations were observed between CE-CT and NCE-CT histogram-derived entropy (EntropyHist) and area under the curve (CHAUC) (rs = 0.49, p < 0.001 and rs= 0.45, p < 0.001, respectively). Some second and third order textural features were found highly correlated between CE-CT and NCE-CT, such as small zone-size emphasis SZSE (rs = 0.729, p < 0.001) and zone-size percentage (rs = 0.770, p < 0.001). Grey-levels discretization methods influenced these correlations. A few of the third order NCE-CT and CE-CT features were significantly associated with survival. CONCLUSION Some radiomics features with moderate correlations between nonenhanced and enhanced CT images were found to be associated with survival, thus suggesting that complementary prognostic value may be extracted from both modalities when available.

[1]  M. Hatt,et al.  Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET , 2012, The Journal of Nuclear Medicine.

[2]  Yanqi Huang,et al.  Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  O. Mawlawi,et al.  Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. , 2014, International journal of radiation oncology, biology, physics.

[4]  Wolfgang Weber,et al.  Reliability of PET/CT Shape and Heterogeneity Features in Functional and Morphologic Components of Non–Small Cell Lung Cancer Tumors: A Repeatability Analysis in a Prospective Multicenter Cohort , 2016, The Journal of Nuclear Medicine.

[5]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[6]  V. Goh,et al.  Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. , 2013, Radiology.

[7]  Simon Wan,et al.  Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer , 2013, Clinical Cancer Research.

[8]  Chris R Chatwin,et al.  Hepatic enhancement in colorectal cancer: texture analysis correlates with hepatic hemodynamics and patient survival. , 2007, Academic Radiology.

[9]  Di Dong,et al.  The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer , 2016, Oncotarget.

[10]  P. Lambin,et al.  Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation , 2014, PloS one.

[11]  P. Lambin,et al.  Radiomics Digital Phantom , 2016 .

[12]  Robert J. Gillies,et al.  The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis , 2015, Scientific Reports.

[13]  Ronald Boellaard,et al.  Repeatability of Radiomic Features in Non-Small-Cell Lung Cancer [18F]FDG-PET/CT Studies: Impact of Reconstruction and Delineation , 2016, Molecular Imaging and Biology.

[14]  M. Hatt,et al.  Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.

[15]  Hongmin Cai,et al.  Quantitative analysis and prediction of regional lymph node status in rectal cancer based on computed tomography imaging , 2011, European Radiology.

[16]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[17]  Florent Tixier,et al.  Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[18]  Balaji Ganeshan,et al.  Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. , 2009, Radiology.

[19]  K. Miles,et al.  Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.

[20]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.

[21]  Vicky Goh,et al.  Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? , 2013, European journal of radiology.

[22]  C. Compton,et al.  AJCC Cancer Staging Manual , 2002, Springer New York.

[23]  Howard Y. Chang,et al.  Decoding global gene expression programs in liver cancer by noninvasive imaging , 2007, Nature Biotechnology.

[24]  Marek Kretowski,et al.  Toward a better understanding of texture in vascular CT scan simulated images , 2001, IEEE Transactions on Biomedical Engineering.

[25]  Dimitris Visvikis,et al.  Characterization of PET/CT images using texture analysis: the past, the present… any future? , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

[26]  P. Lambin Data from: Radiomics Digital Phantom , 2016 .

[27]  Alejandro Munoz del Rio,et al.  CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes , 2015, Abdominal Imaging.

[28]  L Cozzi,et al.  PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology , 2017, Scientific Reports.

[29]  Lorena Losi,et al.  Evolution of intratumoral genetic heterogeneity during colorectal cancer progression. , 2005, Carcinogenesis.

[30]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[31]  Marco Greco,et al.  Worldwide burden of colorectal cancer: a review , 2016, Updates in Surgery.

[32]  Jinzhong Yang,et al.  Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors , 2016, Comput. Medical Imaging Graph..