Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study

Shucheng Bi1, Han Zhang1, Hexiang Wang1, Yaqiong Ge2, Peng Zhang3, Zhenchang Wang3, Dapeng Hao1
1Department of Radiology, the Affiliated Hospital of Qingdao University, Qingdao, China
2GE Healthcare China, Shanghai, China
3Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China

Tóm tắt

ObjectivesTo investigate the efficacy of multi-parametric MRI-based radiomics nomograms for preoperative distinction between benign and malignant sinonasal tumors.MethodsData of 244 patients with sinonasal tumor (training set, n=192; test set, n=52) who had undergone pre-contrast MRI, and 101 patients who underwent post-contrast MRI (training set, n=74; test set, n=27) were retrospectively analyzed. Independent predictors of malignancy were identified and their performance were evaluated. Seven radiomics signatures (RSs) using maximum relevance minimum redundancy (mRMR), and the least absolute shrinkage selection operator (LASSO) algorithm were established. The radiomics nomograms, comprising the clinical model and the RS algorithms were built: one based on pre-contrast MRI (RNWOC); the other based on pre-contrast and post-contrast MRI (RNWC). The performances of the models were evaluated with area under the curve (AUC), calibration, and decision curve analysis (DCA) respectively.ResultsThe efficacy of the clinical model (AUC=0.81) of RNWC was higher than that of the model (AUC=0.76) of RNWOC in the test set. There was no significant difference in the AUC of radiomic algorithms in the test set. The RS-T1T2 (AUC=0.74) and RS-T1T2T1C (RSWC, AUC=0.81) achieved a good distinction efficacy in the test set. The RNWC and the RNWOC showed excellent distinction (AUC=0.89 and 0.82 respectively) in the test set. The DCA of the nomograms showed better clinical usefulness than the clinical models and radiomics signatures.ConclusionsThe radiomics nomograms combining the clinical model and RS can be accurately, safely and efficiently used to distinguish between benign and malignant sinonasal tumors.

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Tài liệu tham khảo

Dammann, 2014, Diagnostic Imaging Modalities in Head and Neck Disease, Deutsches Arzteblatt Int, 111, 10.3238/arztebl.2014.0417

Ginat, 2020, Squamous Cell Carcinoma Arising From Sinonasal Inverted Papilloma, AJNR Am J Neurorad, 41, 10.3174/ajnr.A6583

Harnsberger, 2011, Diagnostic Imaging: Head and Neck, Am Assoc Neurol Surgeons, 104, 10.3171/jns.2006.104.1.167a

Bossi, 2016, Paranasal Sinus Cancer, Crit Rev Oncol/Hematol, 98, 45, 10.1016/j.critrevonc.2015.09.009

Wood, 2012, Inverted papillomas and benign nonneoplastic lesions of the nasal cavity, Am J Rhinol Allergy, 26, 10.2500/ajra.2012.26.3732

Carta, 2012, Surgical Management of Inverted Papilloma: Approaching a New Standard for Surgery, Head Neck, 35, 10.1002/hed.23159

Banhiran, 2005, Endoscopic Sinus Surgery for Benign and Malignant Nasal and Sinus Neoplasm, Curr Opin Otolaryngol Head Neck Surg, 13, 10.1097/00020840-200502000-00012

Harvey, 2009, Surgical Management of Benign Sinonasal Masses, Otolaryngol Clin North Am, 10.1016/j.otc.2009.01.006

Contrera, 2020, Clinical management of emerging sinonasal malignancies, Head Neck, 42, 10.1002/hed.26150

Tabaee, 2011, Indications, Technique, Safety, and Accuracy of Office-Based Nasal Endoscopy With Biopsy for Sinonasal Neoplasm, Int Forum Allergy Rhinol, 1, 10.1002/alr.20035

Eggesbo, 2012, Imaging of Sinonasal Tumours, Cancer Imaging, 12, 10.1102/1470-7330.2012.0015

Koeller, 2016, Radiologic Features of Sinonasal Tumors, Head Neck Pathol, 10, 1, 10.1007/s12105-016-0686-9

Bossi, 2015, Sinonasal Tumors: Computed Tomography and MR Imaging Features, Neuroimaging Clinics North America, 25, 595, 10.1016/j.nic.2015.07.006

Sasaki, 2011, Apparent Diffusion Coefficient Mapping for Sinonasal Diseases: Differentiation of Benign and Malignant Lesions, AJNR Am J Neuroradiol, 32, 10.3174/ajnr.A2434

Sasaki, 2009, Imaging of Sinonasal Tumors, Semin Ultrasound CT MR, 30, 25, 10.1053/j.sult.2008.10.013

Gillies, 2016, Radiomics: Images are More Than Pictures, They Are Data, Radiology, 278, 10.1148/radiol.2015151169

Lambin, 2012, Radiomics: Extracting More Information From Medical Images Using Advanced Feature Analysis, Eur J Cancer, 48, 10.1016/j.ejca.2011.11.036

Yang, 2019, A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma, Liver Cancer, 8, 10.1159/000494099

Conti, 2020, Radiomics in Breast Cancer Classification and Prediction, Semin Cancer Biol, 10.1016/j.semcancer.2020.04.002

Bi, 2019, Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications, CA: Cancer J Clin, 69, 10.3322/caac.21552

Lambin, 2020, Radiomics: The Bridge Between Medical Imaging and Personalized Medicine, Nat Rev Clin Oncol, 14, 10.1038/nrclinonc.2017.141

Collins, 2015, Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement, BMJ, 350, g7594, 10.1136/bmj.g7594

Thompson, 2018, New Tumor Entities in the 4th Edition of the World Health Organization Classification of Head and Neck Tumors: Nasal Cavity, Paranasal Sinuses and Skull Base, Virchows Archiv, 472, 10.1007/s00428-017-2116-0

Gomes, 2020, Accuracy of ITK-SNAP Software for 3D Analysis of a non-Regular Topography Structure, Oral Radiol, 36, 10.1007/s11282-019-00397-y

Griethuysen, 2017, Computational Radiomics System to Decode the Radiographic Phenotype, Cancer Res, 77, 10.1158/0008-5472.CAN-17-0339

Lucia, 2018, External Validation of a Combined PET and MRI Radiomics Model for Prediction of Recurrence in Cervical Cancer Patients Treated With Chemoradiotherapy, Eur J Nucl Med Mol Imaging, 46, 10.1007/s00259-018-4231-9

Fanny, 2019, Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics, Radiology, 291, 10.1148/radiol.2019182023

Lambin, 2017, Harmonization of Multi-Site Diffusion Tensor Imaging Data, NeuroImage, 161, 10.1016/j.neuroimage.2017.08.047

El-Gerby, 2017, Differentiating Benign From Malignant Sinonasal Lesions: Feasibility of Diffusion Weighted MRI, Int Arch Otorhinolaryngol, 21, 10.1055/s-0036-1597323

Prabasaj, 2013, Standardizing the Power of the Hosmer-Lemeshow Goodness of Fit Test in Large Data Sets, Stat Med, 32, 67, 10.1002/sim.5525

El-Gerby, 2008, Extensions to Decision Curve Analysis, a Novel Method for Evaluating Diagnostic Tests, Prediction Models and Molecular Markers, BMC Med Inf Decis Mak, 8, 10.1186/1472-6947-8-53

Vrionis, 2004, Malignant Tumors of the Anterior Skull Base, Cancer Control, 11, 10.1177/107327480401100302

Attlmayr, 2017, Management of Inverted Papilloma: Review, J Laryngol Otol, 131, 10.1017/s0022215117000172

Zhang, 2020, Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions, J Magn Reson Imaging, 52, 596, 10.1002/jmri.27098

Wang, 2020, Radiomics Nomogram for Differentiating Between Benign and Malignant Soft-Tissue Masses of the Extremities, J Magn Reson Imaging, 51, 10.1002/jmri.26818

Lu, 2019, Radiomic Analysis for Preoperative Prediction of Cervical Lymph Node Metastasis in Patients With Papillary Thyroid Carcinoma, Eur J Radiol, 118, 10.1016/j.ejrad.2019.07.018

Gui, 2005, Penalized Cox Regression Analysis in the High-Dimensional and Low-Sample Size Settings, With Applications to Microarray Gene Expression Data, Bioinformatics, 21, 10.1093/bioinformatics/bti422

Wang, 2017, High-Resolution Diffusion-Weighted Imaging Improves the Diagnostic Accuracy of Dynamic Contrast-Enhanced Sinonasal Magnetic Resonance Imaging, J Comput Assist Tomogr, 41, 199, 10.1097/rct.0000000000000502

Xiao, 2018, Intravoxel Incoherent Motion MR Imaging in the Differentiation of Benign and Malignant Sinonasal Lesions: Comparison With Conventional Diffusion-Weighted MR Imaging, AJNR Am J Neurorad, 39, 10.3174/ajnr.A5532

Zhang, 2020, An MRI Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors, J Magn Reson Imaging, 12, 10.1002/jmri.27298