Radiographic prediction of meningioma grade by semantic and radiomic features

Objectives The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making. Methods A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44). Results Twelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62–0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84). Conclusions We found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.

[1]  Maximilian Reiser,et al.  Radiomic Analysis Reveals Prognostic Information in T1-Weighted Baseline Magnetic Resonance Imaging in Patients With Glioblastoma , 2017, Investigative radiology.

[2]  Hugo J. W. L. Aerts,et al.  Radiomic‐Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC , 2017, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[3]  M. Hatt,et al.  Radiomics in PET/CT: More Than Meets the Eye? , 2017, The Journal of Nuclear Medicine.

[4]  Philippe Lambin,et al.  Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures , 2017, The British journal of radiology.

[5]  Chintan Parmar,et al.  Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT , 2017, PloS one.

[6]  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.

[7]  H. Aerts.  The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. , 2016, JAMA oncology.

[8]  H. Kingma,et al.  An Exploratory Study to Detect Ménière’s Disease in Conventional MRI Scans Using Radiomics , 2016, Front. Neurol..

[9]  M. Götz,et al.  Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response , 2016, Clinical Cancer Research.

[10]  R. Beroukhim,et al.  Genomic landscape of intracranial meningiomas. , 2016, Journal of neurosurgery.

[11]  Raymond H Mak,et al.  CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  H. Aerts,et al.  Applications and limitations of radiomics , 2016, Physics in medicine and biology.

[13]  I. Dunn,et al.  Meningioma Genomics: Diagnostic, Prognostic, and Therapeutic Applications , 2016, Front. Surg..

[14]  M. Hatt,et al.  Erratum to: Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

[15]  Raymond H Mak,et al.  Radiomic phenotype features predict pathological response in non-small cell lung cancer. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[16]  D. Reardon,et al.  Checkpoint inhibition in meningiomas. , 2016, Immunotherapy.

[17]  Ginu A. Thomas,et al.  Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity , 2016, Scientific Reports.

[18]  Shadia S. Mikhael,et al.  Imaging signatures of meningioma and low-grade glioma: a diffusion tensor, magnetization transfer and quantitative longitudinal relaxation time MRI study. , 2016, Magnetic resonance imaging.

[19]  A. Wienke,et al.  Use of Diffusion Weighted Imaging in Differentiating Between Maligant and Benign Meningiomas. A Multicenter Analysis. , 2016, World neurosurgery.

[20]  Anant Madabhushi,et al.  Radiomics Analysis on FLT-PET/MRI for Characterization of Early Treatment Response in Renal Cell Carcinoma: A Proof-of-Concept Study1 , 2016, Translational oncology.

[21]  W. Tsai,et al.  Reproducibility of radiomics for deciphering tumor phenotype with imaging , 2016, Scientific Reports.

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

[23]  Florent Tixier,et al.  Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III , 2016, European Journal of Nuclear Medicine and Molecular Imaging.

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

[25]  Shao Hui Huang,et al.  External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma , 2015, Acta oncologica.

[26]  Benjamin Haibe-Kains,et al.  Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer , 2015, Scientific Reports.

[27]  Jun Pan,et al.  Preoperative Radiologic Classification of Convexity Meningioma to Predict the Survival and Aggressive Meningioma Behavior , 2015, PloS one.

[28]  P. Lambin,et al.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[29]  M. Hatt,et al.  18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi–Cancer Site Patient Cohort , 2015, The Journal of Nuclear Medicine.

[30]  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.

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

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

[33]  D. Takhtani,et al.  Correlation of apparent diffusion coefficient with Ki-67 proliferation index in grading meningioma. , 2014, AJR. American journal of roentgenology.

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

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

[36]  Lawrence H. Schwartz,et al.  Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes. , 2013, Radiology.

[37]  Vinod Kumar,et al.  A dual neural network ensemble approach for multiclass brain tumor classification , 2012, International journal for numerical methods in biomedical engineering.

[38]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[39]  Figen Soylemezoglu,et al.  Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? , 2012, European journal of radiology.

[40]  P. A. Futreal,et al.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. , 2012, The New England journal of medicine.

[41]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[42]  Thomas Backer-Grøndahl,et al.  The histopathological spectrum of human meningiomas. , 2012, International journal of clinical and experimental pathology.

[43]  Yi Liu,et al.  Automatic Annotation of Radiological Observations in Liver CT Images , 2012, AMIA.

[44]  Aedín C. Culhane,et al.  survcomp: an R/Bioconductor package for performance assessment and comparison of survival models , 2011, Bioinform..

[45]  Hiroshi Iseki,et al.  1H-MRS of intracranial meningiomas: What it can add to known clinical and MRI predictors of the histopathological and biological characteristics of the tumor? , 2011, Clinical Neurology and Neurosurgery.

[46]  Noriyuki Tomiyama,et al.  Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: correlation with pathologic prognostic factors. , 2010, Lung cancer.

[47]  R. Scienza,et al.  Diffusion-weighted imaging does not predict histological grading in meningiomas , 2010, Acta Neurochirurgica.

[48]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[49]  M. Radoš,et al.  Characteristics of typical and atypical meningiomas on ADC maps with respect to schwannomas. , 2008, Clinical imaging.

[50]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[51]  Bettina Grün,et al.  How constrained a response: a comparison of binary, ordinal and metric answer formats , 2007 .

[52]  D. Louis.  WHO classification of tumours of the central nervous system , 2007 .

[53]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[54]  B. Hakyemez,et al.  The contribution of diffusion-weighted MR imaging to distinguishing typical from atypical meningiomas , 2006, Neuroradiology.

[55]  Ron Kikinis,et al.  3D Slicer , 2012, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[56]  M. Bondy,et al.  Epidemiology and etiology of intracranial meningiomas: A review , 1996, Journal of Neuro-Oncology.

[57]  A. Paetau,et al.  Computed tomography and angiography do not reliably discriminate malignant meningiomas from benign ones , 2004, Neuroradiology.

[58]  S. Nakasu,et al.  Preoperative identification of meningiomas that are highly likely to recur. , 1999, Journal of neurosurgery.

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

[60]  J. Smirniotopoulos,et al.  Typical, atypical, and misleading features in meningioma. , 1991, Radiographics : a review publication of the Radiological Society of North America, Inc.

[61]  D. Louw,et al.  Incidence and clinicopathological features of meningioma. , 1989, Journal of neurosurgery.

[62]  R. Barnard,et al.  The classification of tumours of the central nervous system. , 1982, Neuropathology and applied neurobiology.