Association of Radiomics and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme.

PURPOSE To build a framework for investigation of the associations between imaging, clinical target volumes (CTVs), and metabolic tumor volumes (MTVs) features for better understanding of the underlying information in the CTVs and dependencies between these volumes. High-throughput extraction of imaging and metabolomic quantitative features from magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging of glioblastoma multiforme (GBM) results in tens of variables per patient. In radiation therapy of GBM the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl aspartate (NAA) and choline (Cho). The corresponding clinical target volumes (CTVs) for radiation therapy are based on contrast-enhanced T1-weighted (CE-T1w) and T2-weighted (T2w)/fluid-attenuated inversion recovery MRI. METHODS AND MATERIALS Necrotic portions, enhancing lesion, and edema were manually contoured on CE-T1w/T2w images for 17 GBM patients. Clinical target volumes and MTVs for NAA (MTVNAA) and Cho (MTVCho) were constructed. Imaging and metabolic features related to size, shape, and signal intensities of the volumes were extracted. Tumors were also scored categorically for 10 semantic imaging traits by a neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and CTVs/MTVs features were visualized as heatmaps. Associations between MTVNAA and MTVCho and imaging features were studied using Spearman correlation. RESULTS Forty-eight imaging features were extracted per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. Twenty features were extracted from CTVs and MTVs. A series of semantic imaging traits were replaced with automatically extracted continuous variables. There were multiple (22) significant correlations of imaging measures with CTVs/MTVNAA, whereas there were only 6 with CTVs/MTVCho. CONCLUSIONS A framework for investigation of codependencies between MRI and magnetic resonance spectroscopic imaging radiomic features and CTVs/MTVs has been established. The MTV for NAA was found to be closely associated with MRI volumes, whereas very few imaging features were related to MTVCho, indicating that Cho provides additional information to imaging.

[1]  Eduard Schreibmann,et al.  Whole-brain spectroscopic MRI biomarkers identify infiltrating margins in glioblastoma patients. , 2016, Neuro-oncology.

[2]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

[3]  William D. Dunn,et al.  MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. , 2013, Radiology.

[4]  A. Škoch,et al.  Potential of MR spectroscopy for assessment of glioma grading , 2013, Clinical Neurology and Neurosurgery.

[5]  W P Dillon,et al.  Preoperative proton MR spectroscopic imaging of brain tumors: correlation with histopathologic analysis of resection specimens. , 2001, AJNR. American journal of neuroradiology.

[6]  J. Bloem,et al.  RECIST revised: implications for the radiologist. A review article on the modified RECIST guideline , 2009, European Radiology.

[7]  Mauricio Reyes,et al.  Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features , 2015, Scientific Reports.

[8]  Tej D. Azad,et al.  Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities , 2015, Science Translational Medicine.

[9]  Robert J. Gillies,et al.  Test–Retest Reproducibility Analysis of Lung CT Image Features , 2014, Journal of Digital Imaging.

[10]  M. Berger,et al.  Histopathological validation of a three-dimensional magnetic resonance spectroscopy index as a predictor of tumor presence. , 2002, Journal of neurosurgery.

[11]  K. Aldape,et al.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules , 2008, Proceedings of the National Academy of Sciences.

[12]  Thomas J. Bruno,et al.  Relating Complex Fluid Composition and Thermophysical Properties with the Advanced Distillation Curve Approach , 2010 .

[13]  Ilwoo Park,et al.  Patterns of recurrence analysis in newly diagnosed glioblastoma multiforme after three-dimensional conformal radiation therapy with respect to pre-radiation therapy magnetic resonance spectroscopic findings. , 2007, International journal of radiation oncology, biology, physics.

[14]  Neema Jamshidi,et al.  Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation. , 2013, Radiology.

[15]  A. Markoe,et al.  Volumetric spectroscopic imaging of glioblastoma multiforme radiation treatment volumes. , 2014, International journal of radiation oncology, biology, physics.

[16]  S. Delorme,et al.  Applications of MRS in the evaluation of focal malignant brain lesions , 2006, Cancer imaging : the official publication of the International Cancer Imaging Society.

[17]  Shiao Y. Woo,et al.  Evaluation of peritumoral edema in the delineation of radiotherapy clinical target volumes for glioblastoma. , 2007, International journal of radiation oncology, biology, physics.

[18]  Jordan M. Malof,et al.  Imaging descriptors improve the predictive power of survival models for glioblastoma patients. , 2013, Neuro-oncology.

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

[20]  R. Stupp,et al.  For Personal Use. Only Reproduce with Permission from the Lancet Publishing Group. Review Temozolomide for Brain Tumours Current and Future Developments in the Use of Temozolomide for the Treatment of Brain Tumours , 2022 .

[21]  M. Martel,et al.  Patterns of failure following high-dose 3-D conformal radiotherapy for high-grade astrocytomas: a quantitative dosimetric study. , 1999, International journal of radiation oncology, biology, physics.

[22]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[23]  Mitchel S Berger,et al.  3D MRSI for resected high-grade gliomas before RT: tumor extent according to metabolic activity in relation to MRI. , 2004, International journal of radiation oncology, biology, physics.

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

[25]  Maria Werner-Wasik,et al.  Randomized comparison of stereotactic radiosurgery followed by conventional radiotherapy with carmustine to conventional radiotherapy with carmustine for patients with glioblastoma multiforme: report of Radiation Therapy Oncology Group 93-05 protocol. , 2004, International journal of radiation oncology, biology, physics.

[26]  E J Lee,et al.  Potential role of advanced MRI techniques for the peritumoural region in differentiating glioblastoma multiforme and solitary metastatic lesions. , 2013, Clinical radiology.

[27]  S. Plevritis,et al.  Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. , 2015, Radiology.

[28]  J. Flickinger,et al.  The American Society for Therapeutic Radiology and Oncology (ASTRO) evidence-based review of the role of radiosurgery for malignant glioma. , 2005, International journal of radiation oncology, biology, physics.

[29]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[30]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[31]  C. Law,et al.  Effects of turbulence and flame instability on flame front evolution , 2006 .

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

[33]  D. Amelio,et al.  Patterns of failure and comparison of different target volume delineations in patients with glioblastoma treated with conformal radiotherapy plus concomitant and adjuvant temozolomide. , 2010, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[34]  L O Hall,et al.  Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging , 2006, NMR in biomedicine.

[35]  Daniel M. Spielman,et al.  Utility of multiparametric 3-T MRI for glioma characterization , 2013, Neuroradiology.

[36]  Matthew B Schabath,et al.  Semiquantitative Computed Tomography Characteristics for Lung Adenocarcinoma and Their Association With Lung Cancer Survival. , 2015, Clinical lung cancer.

[37]  M J Gleason,et al.  Outcomes and prognostic factors in recurrent glioma patients enrolled onto phase II clinical trials. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[38]  Ewald Moser,et al.  Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomas , 2004, NeuroImage.