CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes.

OBJECTIVE The purpose of the present study is to determine whether CT texture features of newly diagnosed primary renal cell carcinomas (RCCs) correlate with pathologic features and oncologic outcomes. MATERIALS AND METHODS CT texture analysis was performed on large (> 7 cm; mean size, 9.9 cm) untreated RCCs in 157 patients (52 women and 105 men; mean age, 60.3 years). Measures of tumor heterogeneity, including entropy, kurtosis, skewness, mean, mean of positive pixels, and SD of pixel distribution histogram were derived from multiphasic CT using various filter settings: unfiltered (spatial scaling factor, 0), fine (spatial scaling factor, 2), medium (spatial scaling factor, 3-4), or coarse (spatial scaling factor, 5-6). Texture values were correlated with histologic subtype, nuclear grade, pathologic stage, and clinical outcome. RESULTS When a coarse filter setting (spatial scaling factor, 6) was used, entropy on portal venous phase CT images was positively associated with clear cell histologic findings (odds ratio [OR], 134; 95% CI, 16-1110; p < 0.001) and was negatively associated with non-clear cell subtype findings (papillary spatial scale factor, 6; OR, 0.016; 95% CI, 0.002-0.132; p < 0.001). ROC curve analysis for entropy (on portal venous phase images obtained with a spatial scaling factor of 6) revealed an AUC of 0.943 (95% CI, 0.892-0.993) for clear cell histologic findings, with similar values noted for non-clear cell histologic findings. The mean of positive pixels and the SD of the pixel distribution histogram were statistically significantly associated with histologic cell type in a similar fashion. Entropy, the SD of the pixel distribution histogram, and the mean of positive pixels were associated with nuclear grade, most prominently when fine or medium texture filters were used (p < 0.05). There was a statistically significant association of texture features noted on unenhanced CT, including the SD of the pixel distribution histogram, the mean of positive pixels, and entropy, with the time to disease recurrence and death due to disease (e.g., for entropy noted on unenhanced CT images obtained with a spatial scaling factor of 6, the hazard ratio was 3.49 [95% CI, 1.55-7.84]; p = 0.002). CONCLUSION CT texture features (in particular, entropy, the mean of positive pixels, and the SD of the pixel distribution histogram) are associated with tumor histologic findings, nuclear grade, and outcome measures. The contrast phase does seem to affect heterogeneity measures.

[1]  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.

[2]  I. Tannock,et al.  Systemic therapy for non-clear cell renal cell carcinomas: a systematic review and meta-analysis. , 2015, European urology.

[3]  V Goh,et al.  Assessment of changes in tumor heterogeneity following neoadjuvant chemotherapy in primary esophageal cancer. , 2015, Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus.

[4]  Elliot K Fishman,et al.  CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology. , 2014, Academic radiology.

[5]  T. Choueiri,et al.  Cytoreductive nephrectomy in patients with synchronous metastases from renal cell carcinoma: results from the International Metastatic Renal Cell Carcinoma Database Consortium. , 2014, European urology.

[6]  R. Korn,et al.  Noninvasive Image Texture Analysis Differentiates K-ras Mutation from Pan-Wildtype NSCLC and Is Prognostic , 2014, PloS one.

[7]  B. K. Park,et al.  Subtype differentiation of renal cell carcinoma using diffusion-weighted and blood oxygenation level-dependent MRI. , 2014, AJR. American journal of roentgenology.

[8]  Liangping Zhou,et al.  Magnetic Resonance Imaging and Computed Tomography Characteristics of Renal Cell Carcinoma Associated with Xp11.2 Translocation/TFE3 Gene Fusion , 2014, PloS one.

[9]  Aaron D Ward,et al.  Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. , 2014, Medical physics.

[10]  Peter J. Ell,et al.  Multifunctional Imaging Signature for V-KI-RAS2 Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) Mutations in Colorectal Cancer , 2014, The Journal of Nuclear Medicine.

[11]  Jae-Hun Kim,et al.  Quantitative CT Variables Enabling Response Prediction in Neoadjuvant Therapy with EGFR-TKIs: Are They Different from Those in Neoadjuvant Concurrent Chemoradiotherapy? , 2014, PloS one.

[12]  R. Abouassaly,et al.  Trends in cytoreductive nephrectomy in the eras of immuno and targeted therapy. , 2014 .

[13]  V. Goh,et al.  Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy. , 2013, Radiology.

[14]  H. Hricak,et al.  Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. , 2013, Radiology.

[15]  Y. Shibamoto,et al.  MRI for differentiation of renal cell carcinoma with sarcomatoid component from other renal tumor types , 2014, Abdominal Imaging.

[16]  M. Stampfer,et al.  Mounting evidence for prediagnostic use of statins in reducing risk of lethal prostate cancer. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  Michael E Griswold,et al.  Locally advanced squamous cell carcinoma of the head and neck: CT texture and histogram analysis allow independent prediction of overall survival in patients treated with induction chemotherapy. , 2013, Radiology.

[18]  E. Fishman,et al.  Chromophobe renal cell carcinoma: multiphase MDCT enhancement patterns and morphologic features. , 2013, AJR. American journal of roentgenology.

[19]  Dina Muin,et al.  Texture-based classification of different gastric tumors at contrast-enhanced CT. , 2013, European journal of radiology.

[20]  M. P. Hayball,et al.  CT texture analysis using the filtration-histogram method: what do the measurements mean? , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.

[21]  J. Ellis,et al.  Differentiation of papillary renal cell carcinoma subtypes on CT and MRI. , 2013, AJR. American journal of roentgenology.

[22]  Daniel Margolis,et al.  Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT. , 2013, Radiology.

[23]  Han Liu,et al.  Clinical and pathologic impact of select chromatin-modulating tumor suppressors in clear cell renal cell carcinoma. , 2013, European urology.

[24]  Balaji Ganeshan,et al.  Quantifying tumour heterogeneity with CT , 2013, Cancer imaging : the official publication of the International Cancer Imaging Society.

[25]  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.

[26]  P. Kapur,et al.  Effects on survival of BAP1 and PBRM1 mutations in sporadic clear-cell renal-cell carcinoma: a retrospective analysis with independent validation. , 2013, The Lancet. Oncology.

[27]  B. Trock,et al.  Multiphasic enhancement patterns of small renal masses (≤4 cm) on preoperative computed tomography: utility for distinguishing subtypes of renal cell carcinoma, angiomyolipoma, and oncocytoma. , 2012, Urology.

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

[29]  V. Goh,et al.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. , 2013, Radiology.

[30]  Pheroze Tamboli,et al.  Limitations of preoperative biopsy in patients with metastatic renal cell carcinoma: comparison to surgical pathology in 405 cases , 2012, BJU international.

[31]  G. Sonpavde,et al.  Trends in the use of cytoreductive nephrectomy in the United States. , 2012, Clinical genitourinary cancer.

[32]  A. Belldegrun,et al.  Impact of pathological tumour characteristics in patients with sarcomatoid renal cell carcinoma , 2012, BJU international.

[33]  V. Goh,et al.  Quantitative Imaging Biomarkers from PET-CT as potential correlates for angiogenesis and hypoxia in colorectal cancer , 2012 .

[34]  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.

[35]  R. Arceci.  Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing , 2012 .

[36]  W. Linehan,et al.  Sarcomatoid renal cell carcinoma: a comprehensive review of the biology and current treatment strategies. , 2012, The oncologist.

[37]  K. Miles,et al.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival , 2012, European Radiology.

[38]  V. Goh,et al.  Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. , 2011, Radiology.

[39]  A. Ravaud,et al.  Combination of Gemcitabine and Doxorubicin in Rapidly Progressive Metastatic Renal Cell Carcinoma and/or Sarcomatoid Renal Cell Carcinoma , 2011, Oncology.

[40]  Michael J Wallace,et al.  Percutaneous biopsy of primary tumor in metastatic renal cell carcinoma to predict high risk pathological features: comparison with nephrectomy assessment. , 2010, The Journal of urology.

[41]  Balaji Ganeshan,et al.  Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[42]  Chris R Chatwin,et al.  Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. , 2009, European journal of radiology.

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

[44]  Chaya S Moskowitz,et al.  Solid renal cortical tumors: differentiation with CT. , 2007, Radiology.

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

[46]  M. Jinzaki,et al.  Double-Phase Helical CT of Small Renal Parenchymal Neoplasms: Correlation with Pathologic Findings and Tumor Angiogenesis , 2000, Journal of computer assisted tomography.