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A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma

Published:November 30, 2022DOI:https://doi.org/10.1016/j.acra.2022.11.007

      Rationale and Objectives

      Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC.

      Materials and Methods

      A total of 204 patients with HNSCC who underwent CECT scans were enrolled in this study. The participants recruited from two hospitals were split into a training set (n=124, 74 well/moderately differentiated and 50 poorly differentiated) of patients from one hospital and an external test set of patients from the other hospital (n=80, 49 well/moderately differentiated and 31 poorly differentiated). CECT-based manually-extracted radiomics (MER) features and deep learning (DL) features were extracted and selected. The selected MER features and DL features were then combined to construct a DLRN via multivariate logistic regression. The predictive performance of the DLRN was assessed using ROCs and decision curve analysis (DCA).

      Results

      Three MER features and seven DL features were finally selected. The DLRN incorporating the selected MER and DL features showed good predictive value for the histological differentiation grades of HNSCC (well/moderately differentiated vs. poorly differentiated) in both the training (AUC, 0.878) and test (AUC, 0.822) sets. DCA demonstrated that the DLRN was clinically useful for predicting histological differentiation grades of HNSCC.

      Conclusion

      A CECT-based DLRN was constructed to predict histological differentiation grades of HNSCC. The DLRN showed good predictive efficacy and might be useful for prognostic evaluation of patients with HNSCC.

      Key Words

      Abbreviations:

      3D (Three-dimensional), AUC (Area under the curve), CECT (Contrast-enhanced CT), CI (Confidence interval), CNN (Convolutional neural network), DCA (Decision curve analysis), DICOM (Digital imaging and communications in medicine), DL (Deep learning), DLRN (Deep learning radiomics nomogram), GLCM (Gray level co-occurrence matrix), GLDM (Gray level dependence matrix), GLRLM (Gray level run length matrix), GLSZM (Gray level size zone matrix), HNSCC (Head and neck squamous cell carcinoma), HU (Hounsfield units), ICC (Inter-/intra- class correlation coefficient), LASSO (Least absolute shrinkage and selection operator), MER (Manually-extracted radiomics), NGTDM (Neighbouring gray tone difference matrix), PNG (Portable network graphics), ROC (Operating characteristics curve), ROI (Region of interest), SD (Standard deviation)
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      References

        • Kacew AJ
        • Harris EJ
        • Lorch JH
        • et al.
        Chemotherapy after immune checkpoint blockade in patients with recurrent, metastatic squamous cell carcinoma of the head and neck.
        Oral Oncol. 2020; 105104676
        • Cohen EEW
        • Soulieres D
        • Le Tourneau C
        • et al.
        Pembrolizumab versus methotrexate, docetaxel, or cetuximab for recurrent or metastatic head-and-neck squamous cell carcinoma (KEYNOTE-040): a randomised, open-label, phase 3 study.
        Lancet. 2019; 393: 156-167
        • Ferris RL
        • Blumenschein Jr., G
        • Fayette J
        • et al.
        Nivolumab for recurrent squamous-cell carcinoma of the head and neck.
        N Engl J Med. 2016; 375: 1856-1867
        • Roland NJ
        • Caslin AW
        • Nash J
        • Stell PM.
        Value of grading squamous cell carcinoma of the head and neck.
        Head Neck. 1992; 14: 224-229
        • Thomas B
        • Stedman M
        • Davies L.
        Grade as a prognostic factor in oral squamous cell carcinoma: a population-based analysis of the data.
        Laryngoscope. 2014; 124: 688-694
        • Gillies RJ
        • Kinahan PE
        • Hricak H.
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Moon SH
        • Kim J
        • Joung JG
        • et al.
        Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer.
        Eur J Nucl Med Mol Imaging. 2019; 46: 446-454
        • Choi ER
        • Lee HY
        • Jeong JY
        • et al.
        Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma.
        Oncotarget. 2016; 7: 67302-67313
        • Coppola F
        • Giannini V
        • Gabelloni M
        • et al.
        Radiomics and magnetic resonance imaging of rectal cancer: from engineering to clinical practice.
        Diagnostics (Basel). 2021; 11: 756
        • Yip SS
        • Aerts HJ.
        Applications and limitations of radiomics.
        Phys Med Biol. 2016; 61: R150-R166
        • Giannini V
        • Mazzetti S
        • Bertotto I
        • et al.
        Predicting locally advanced rectal cancer response to neoadjuvant therapy with (18)F-FDG PET and MRI radiomics features.
        Eur J Nucl Med Mol Imaging. 2019; 46: 878-888
        • Verma V
        • Simone CB
        • Krishnan S
        • et al.
        The rise of radiomics and implications for oncologic management.
        J Natl Cancer Inst. 2017; 109 (10.1093/jnci/djx055)
        • Chang N
        • Cui L
        • Luo Y
        • et al.
        Development and multicenter validation of a CT-based radiomics signature for discriminating histological grades of pancreatic ductal adenocarcinoma.
        Quant Imaging Med Surg. 2020; 10: 692-702
        • Tang X
        • Bai G
        • Wang H
        • et al.
        Elaboration of multiparametric MRI-based radiomics signature for the preoperative quantitative identification of the histological grade in patients with non-small-cell lung cancer.
        J Magn Reson Imaging. 2022; 56: 579-589
        • Zheng T
        • Yang L
        • Du J
        • et al.
        Combination analysis of a radiomics-based predictive model with clinical indicators for the preoperative assessment of histological grade in endometrial carcinoma.
        Front Oncol. 2021; 11582495
        • Fan M
        • Yuan W
        • Zhao W
        • et al.
        Joint prediction of breast cancer histological grade and Ki-67 expression level based on DCE-MRI and DWI radiomics.
        IEEE J Biomed Health Inform. 2020; 24: 1632-1642
        • Wu W
        • Ye J
        • Wang Q
        • et al.
        CT-based radiomics signature for the preoperative discrimination between head and neck squamous cell carcinoma grades.
        Front Oncol. 2019; 9: 821
        • Liu S
        • Sun W
        • Yang S
        • et al.
        Deep learning radiomic nomogram to predict recurrence in soft tissue sarcoma: a multi-institutional study.
        Eur Radiol. 2022; 32: 793-805
        • Bo L
        • Zhang Z
        • Jiang Z
        • et al.
        Differentiation of brain abscess from cystic glioma using conventional MRI based on deep transfer learning features and hand-crafted radiomics features.
        Front Med (Lausanne). 2021; 8748144
        • Tanaka S
        • Kadoya N
        • Sugai Y
        • et al.
        A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy.
        Sci Rep. 2022; 12: 8899
        • Zhang Z
        • Xiao J
        • Wu S
        • et al.
        Deep convolutional radiomic features on diffusion tensor images for classification of glioma grades.
        J Digit Imaging. 2020; 33: 826-837
        • Fujima N
        • Yoshida D
        • Sakashita T
        • et al.
        Usefulness of pseudocontinuous arterial spin-labeling for the assessment of patients with head and neck squamous cell carcinoma by measuring tumor blood flow in the pretreatment and early treatment period.
        AJNR Am J Neuroradiol. 2016; 37: 342-348
        • Chow LQM.
        Head and neck cancer.
        N Engl J Med. 2020; 382: 60-72
        • Truhn D
        • Schrading S
        • Haarburger C
        • et al.
        Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI.
        Radiology. 2019; 290: 290-297
        • Zhao Z
        • Li Y
        • Wu Y
        • et al.
        Deep learning-based model for predicting progression in patients with head and neck squamous cell carcinoma.
        Cancer Biomark. 2020; 27: 19-28
        • Fujima N
        • Andreu-Arasa VC
        • Meibom SK
        • et al.
        Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images.
        BMC Cancer. 2021; 21: 900
        • Sekhar A
        • Biswas S
        • Hazra R
        • et al.
        Brain tumor classification using fine-tuned googLeNet features and machine learning algorithms: IoMT enabled CAD system.
        IEEE J Biomed Health Inform. 2022; 26: 983-991
        • Dai M
        • Liu Y
        • Hu Y
        • et al.
        Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas.
        Eur Radiol. 2022; 32: 7988-7997
        • Hong JH
        • Jung JY
        • Jo A
        • et al.
        Development and validation of a radiomics model for differentiating bone islands and osteoblastic bone metastases at abdominal CT.
        Radiology. 2021; 299: 626-632
        • Zwanenburg A
        • Vallieres M
        • Abdalah MA
        • et al.
        The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.
        Radiology. 2020; 295: 328-338
        • Chen J
        • Lu S
        • Mao Y
        • et al.
        An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: a multi-cohort study.
        Eur Radiol. 2021; 32: 1548-1557
        • Bogowicz M
        • Riesterer O
        • Ikenberg K
        • et al.
        Computed tomography radiomics predicts HPV status and local tumor control after definitive radiochemotherapy in head and neck squamous cell carcinoma.
        Int J Radiat Oncol Biol Phys. 2017; 99: 921-928