Intratumoural heterogeneity measured using FDG PET and MRI is associated with tumour-stroma ratio and clinical outcome in head and neck squamous cell carcinoma.

AIM To evaluate the association between the tumour-stroma ratio and intratumoural heterogeneity measured using 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET) and magnetic resonance imaging (MRI), and further investigate the prognostic significance of imaging biomarkers in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS Textural-based imaging parameters of the primary tumour were extracted in 44 patients. In addition, the difference between the minimum and maximum apparent diffusion coefficient (ADC) values (ADCdiff) was calculated on MRI. The relationships between the tumour-stroma ratio and imaging parameters were evaluated. The associations between imaging parameters and recurrence-free survival (RFS) were assessed using Cox proportional hazard regression models. RESULTS Coarseness (r=-0.382) on PET and ADCdiff (r=0.534) on MRI were significantly correlated with the proportion of stroma. The best imaging biomarkers for the 2-year RFS prediction were coarseness (AUC=0.741) and ADCdiff (AUC=0.779). Multivariate analysis showed that coarseness (hazard ratio=10.549, 95% confidence interval=2.544-43.748, p=0.001) was an independent prognostic factor for RFS. CONCLUSION Heterogeneity imaging parameters are significantly associated with the tumour-stroma ratio. These imaging biomarkers may help to facilitate the risk stratification for tumour recurrence in HNSCC.

[1]  Cristina Lavini,et al.  Contrast-enhanced perfusion magnetic resonance imaging for head and neck squamous cell carcinoma: a systematic review. , 2015, Oral oncology.

[2]  Omar S. Al-Kadi,et al.  Texture Analysis of Aggressive and Nonaggressive Lung Tumor CE CT Images , 2008, IEEE Transactions on Biomedical Engineering.

[3]  Camille Stephan-Otto Attolini,et al.  Stromal gene expression defines poor-prognosis subtypes in colorectal cancer , 2015, Nature Genetics.

[4]  Houqiang Liu,et al.  Tumor-Stroma Ratio Is an Independent Predictor for Survival in Esophageal Squamous Cell Carcinoma , 2012, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[5]  C. Manegold.  Non-small Cell Lung Cancer Treatment , 2007 .

[6]  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.

[7]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[8]  Vicky Goh,et al.  Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? , 2013, The Journal of Nuclear Medicine.

[9]  Raghu Kalluri,et al.  Fibroblasts in cancer , 2006, Nature Reviews Cancer.

[10]  T. Yen,et al.  Clinical Utility of Multimodality Imaging with Dynamic Contrast-Enhanced MRI, Diffusion-Weighted MRI, and 18F-FDG PET/CT for the Prediction of Neck Control in Oropharyngeal or Hypopharyngeal Squamous Cell Carcinoma Treated with Chemoradiation , 2014, PloS one.

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

[12]  Hein Putter,et al.  Tumor–stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients , 2011, Breast Cancer Research and Treatment.

[13]  Hung-Ming Wang,et al.  Development and Evaluation of an Open-Source Software Package “CGITA” for Quantifying Tumor Heterogeneity with Molecular Images , 2014, BioMed research international.

[14]  Eun Sook Ko,et al.  Apparent diffusion coefficient in estrogen receptor-positive invasive ductal breast carcinoma: correlations with tumor-stroma ratio. , 2014, Radiology.

[15]  N. Thacker,et al.  Quantifying heterogeneity in human tumours using MRI and PET. , 2012, European journal of cancer.

[16]  J. Pollard,et al.  Microenvironmental regulation of metastasis , 2009, Nature Reviews Cancer.

[17]  A. A. Abdel Razek,et al.  Nasopharyngeal carcinoma: correlation of apparent diffusion coefficient value with prognostic parameters , 2013, La radiologia medica.

[18]  A. Razek,et al.  Dynamic susceptibility contrast perfusion MR imaging in distinguishing malignant from benign head and neck tumors: a pilot study. , 2011, European journal of radiology.

[19]  C. Chatwin,et al.  Hepatic entropy and uniformity: additional parameters that can potentially increase the effectiveness of contrast enhancement during abdominal CT. , 2007, Clinical radiology.

[20]  Soo Hyun Kwon,et al.  Prognostic significance of the intratumoral heterogeneity of 18F‐FDG uptake in oral cavity cancer , 2014, Journal of surgical oncology.

[21]  A. Razek,et al.  Correlation of Choline/Creatine and Apparent Diffusion Coefficient values with the prognostic parameters of Head and Neck Squamous Cell Carcinoma , 2016, NMR in biomedicine.

[22]  Hans J. Tanke,et al.  The Carcinoma–Stromal Ratio of Colon Carcinoma Is an Independent Factor for Survival Compared to Lymph Node Status and Tumor Stage , 2007, Cellular oncology : the official journal of the International Society for Cellular Oncology.

[23]  Huan Yu,et al.  Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning , 2009, IEEE Transactions on Medical Imaging.

[24]  Dong Soo Lee,et al.  Recent Trends in PET Image Interpretations Using Volumetric and Texture-based Quantification Methods in Nuclear Oncology , 2014, Nuclear Medicine and Molecular Imaging.

[25]  C. Terhaard,et al.  Diffusion-weighted MR imaging in laryngeal and hypopharyngeal carcinoma: association between apparent diffusion coefficient and histologic findings. , 2014, Radiology.

[26]  Z. Werb,et al.  New functions for the matrix metalloproteinases in cancer progression , 2002, Nature Reviews Cancer.

[27]  G. Parker,et al.  Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome , 2014, Clinical Cancer Research.

[28]  Dow-Mu Koh,et al.  Practical aspects of assessing tumors using clinical diffusion-weighted imaging in the body. , 2007, Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine.

[29]  Toshinori Hirai,et al.  Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method. , 2009, Radiology.

[30]  Jingjing Xu,et al.  Tangled fibroblasts in tumor‐stroma interactions , 2011, International journal of cancer.

[31]  Naoko Mori,et al.  Detection of invasive components in cases of breast ductal carcinoma in situ on biopsy by using apparent diffusion coefficient MR parameters , 2013, European Radiology.

[32]  Bauke Ylstra,et al.  Comprehensive genomic meta-analysis identifies intra-tumoural stroma as a predictor of survival in patients with gastric cancer , 2012, Gut.

[33]  Vicky Goh,et al.  Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis , 2012, European Journal of Nuclear Medicine and Molecular Imaging.

[34]  Yemi Kim,et al.  Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ , 2015, European Radiology.

[35]  S. Hyun,et al.  Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma , 2016, European Journal of Nuclear Medicine and Molecular Imaging.