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      Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

      research-article
      a , a , b , c , a , a , a , a , a , a , b , c , b , c , b , c , a , a , b , c , d , *
      Clinical and Translational Radiation Oncology
      Elsevier
      QUS, Quantitative ultrasound, HNSCC, Head and neck squamous cell carcinoma, RT, Radiotherapy, CR, Complete responders, PR, Partial responders, RFS, Recurrence-free survival, R, Recurrence, NR, Non-recurrence, HN, Head and neck, HPV, Human papillomavirus, EBV, Epstein-Barr virus, IMRT, Intensity-modulated radiation therapy, IGRT, Image-guided radiation therapy, MRI, Magnetic resonance imaging, CT, Computed tomography, PET, Positron emission tomography, US, Ultrasound, RF, Radiofrequency, ROI, Region of interest, SS, Spectral slope, SI, Spectral intercept, MBF, Mid-band fit, AAC, Average acoustic concentration, ASD, Average scatterer diameter, ACE, Attenuation co-efficient estimate, SAS, Spacing among scatterers, GLCM, Grey level co-occurrence matrix, ENE, Energy, CON, Contrast, HOM, Homogeneity, COR, Correlation, FLD, Fisher’s linear discriminant, kNN, k nearest neighbors, SVM, Support vector machine, TP, True positive, TN, True negative, FP, False positive, FN, False negative, Sn, Sensitivity, SP, Specificity, Acc, Accuracy, AUC, Area under the curve, FDG-PET, 18F-fluorodeoxyglucose positron emission tomography, Radiomics, Delta-radiomics, Head and neck malignancy, Radiotherapy squamous cell carcinoma, Recurrence, Quantitative ultrasound, Machine learning

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          Graphical abstract

          Highlights

          • Quantitative ultrasound (QUS) delta-radiomics to predict recurrence.

          • Machine learning model can predict recurrence with accuracy >80%.

          • Classification performances improve when features are taken from weeks 1 and 4.

          Abstract

          Purpose

          This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC).

          Methods

          Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation.

          Results

          The median follow up for the entire group was 38 months (range 7–64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment.

          Conclusion

          QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.

          Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

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          Most cited references25

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

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            This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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              Radiomics: the bridge between medical imaging and personalized medicine

              Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.
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                Author and article information

                Contributors
                Journal
                Clin Transl Radiat Oncol
                Clin Transl Radiat Oncol
                Clinical and Translational Radiation Oncology
                Elsevier
                2405-6308
                12 March 2021
                May 2021
                12 March 2021
                : 28
                : 62-70
                Affiliations
                [a ]Physical Sciences, Sunnybrook Research Institute, Toronto, Canada
                [b ]Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada
                [c ]Department of Radiation Oncology, University of Toronto, Toronto, Canada
                [d ]Department of Medical Biophysics, University of Toronto, Toronto, Canada
                Author notes
                [* ]Corresponding author at: Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, T2, Toronto, Ontario M4N3M5, Canada. gregory.czarnota@ 123456sunnybrook.ca
                Article
                S2405-6308(21)00027-6
                10.1016/j.ctro.2021.03.002
                7985224
                3f93d0ad-8577-4b6d-801f-89f943d42293
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 23 December 2020
                : 23 February 2021
                : 7 March 2021
                Categories
                Original Research Article

                qus, quantitative ultrasound,hnscc, head and neck squamous cell carcinoma,rt, radiotherapy,cr, complete responders,pr, partial responders,rfs, recurrence-free survival,r, recurrence,nr, non-recurrence,hn, head and neck,hpv, human papillomavirus,ebv, epstein-barr virus,imrt, intensity-modulated radiation therapy,igrt, image-guided radiation therapy,mri, magnetic resonance imaging,ct, computed tomography,pet, positron emission tomography,us, ultrasound,rf, radiofrequency,roi, region of interest,ss, spectral slope,si, spectral intercept,mbf, mid-band fit,aac, average acoustic concentration,asd, average scatterer diameter,ace, attenuation co-efficient estimate,sas, spacing among scatterers,glcm, grey level co-occurrence matrix,ene, energy,con, contrast,hom, homogeneity,cor, correlation,fld, fisher’s linear discriminant,knn, k nearest neighbors,svm, support vector machine,tp, true positive,tn, true negative,fp, false positive,fn, false negative,sn, sensitivity,sp, specificity,acc, accuracy,auc, area under the curve,fdg-pet, 18f-fluorodeoxyglucose positron emission tomography,radiomics,delta-radiomics,head and neck malignancy,radiotherapy squamous cell carcinoma,recurrence,quantitative ultrasound,machine learning

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