Deep Learning based Classification of Thyroid Cancer using Different Medical Imaging Modalities : A Systematic Review

Authors

  • Maheen Ilyas Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
  • Hassaan Malik Department of Computer Science, University of Management and Technology
  • Muhammad Adnan Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
  • Umair Bashir Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
  • Wajahat Anwaar Bukhari Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
  • Muhammad Imran Ali Khan Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan
  • Adnan Ahmad Department of Computer Science, National College of Business Administration & Economics Lahore, Multan, Pakistan

DOI:

https://doi.org/10.21015/vtse.v9i4.736

Abstract

Deep learning algorithms have achieved a tremendous triumph in task-specific feature classification. Deep learning methods are very much effective when a large amount of training data is scarce. It has been significantly applied for disease classification from medical imaging. The paper aims to identify and summarize the scenario of current research on thyroid cancer using deep learning methods through different medical imaging modalities which are found at present so that reseachers become capable to select a useful and the most relevant approach which might be fruitful in dealing with thyroid cancer. This may also raise a need for more work out while dealing with future challenges. This Systematic literature review (SLR) has been presented by reviewing research articles published in well-reputed venues between 2017 to 2021. A comprehensive review was performed to appraise the deep learning approaches that have been applied in classifying a thyroid nodule disorder from different medical imaging modalities. The analysis is performed based on different parameters reported in selected research studies which include classification accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of 2,149 research studies have been obtained by applying search queries in different journals’ databases, out of them 40 papers have been selected for this SLR. Among them 22 studies have contributed sufficiently to the construction of the evaluation table which enabled the test process of methods of deep learning, having sensitivity varies between 75% to 100% (mean 89.50%) and specificity ranged from 64% to 100% (mean 84.4 %). The outputs revealed that the Convolutional Neural Network (CNN) has produced significant accuracy and has been extensively applied in the diagnosis of thyroid cancer by medical professionals. Furthermore, it is concluded that if the thyroid cancer exposure is inappropriate then it may restrict the deep learning mechanism and make its reliability challenge able.

References

Davies, L., & Welch, H. G. (2006). Increasing incidence of thyroid cancer in the United States, 1973-2002. Jama, 295(18), 2164-2167. DOI: https://doi.org/10.1001/jama.295.18.2164

Kim, T. Y., &Shong, Y. K. (2017). Active surveillance of papillary thyroid microcarcinoma: a mini-review from Korea. Endocrinology and Metabolism, 32(4), 399-406. DOI: https://doi.org/10.3803/EnM.2017.32.4.399

Ezzat, S., Sarti, D. A., Cain, D. R., &Braunstein, G. D. (1994). Thyroid incidentalomas: prevalence by palpation and ultrasonography. Archives of internal medicine, 154(16), 1838-1840. DOI: https://doi.org/10.1001/archinte.154.16.1838

Reiners, C., Wegscheider, K., Schicha, H., Theissen, P., Vaupel, R., Wrbitzky, R., &Schumm-Draeger, P. M. (2004). Prevalence of thyroid disorders in the working population of Germany: ultrasonography screening in 96,278 unselected employees. Thyroid, 14(11), 926-932. DOI: https://doi.org/10.1089/thy.2004.14.926

Vickers, N. J. (2017). Animal Communication: When I’m Calling You, Will You Answer Too?. Current Biology, 27(14), R713-R715. DOI: https://doi.org/10.1016/j.cub.2017.05.064

Werga, P., Wallin, G., Skoog, L., &Hamberger, B. (2000). Expanding role of fine-needle aspiration cytology in thyroid diagnosis and management. World journal of surgery, 24(8), 907-912. DOI: https://doi.org/10.1007/s002680010163

Theoharis, C. G., Schofield, K. M., Hammers, L., Udelsman, R., &Chhieng, D. C. (2009). The Bethesda thyroid fine-needle aspiration classification system: year 1 at an academic institution. Thyroid, 19(11), 1215-1223. DOI: https://doi.org/10.1089/thy.2009.0155

McIver, B., Hay, I. D., Giuffrida, D. F., Dvorak, C. E., Grant, C. S., Thompson, G. B.,&Goellner, J. R. (2001). Anaplastic thyroid carcinoma: a 50-year experience at a single institution. Surgery, 130(6), 1028-1034. DOI: https://doi.org/10.1067/msy.2001.118266

Lin, S., Huang, H., Liu, X., Li, Q., Yang, A. K., Zhang, Q., & Chen, Y. (2014). Treatments for complications of tracheal sleeve resection for papillary thyroid carcinoma with tracheal invasion. European Journal of Surgical Oncology (EJSO), 40(2), 176-181. DOI: https://doi.org/10.1016/j.ejso.2013.12.008

Cronan, J. J. (2008). Thyroid nodules: is it time to turn off the US machines? Radiology, 247(3), 602-604. DOI: https://doi.org/10.1148/radiol.2473072233

Zahir, S. T., Vakili, M., Ghaneei, A., &Heidari, F. (2016). Ultrasound assistance in differentiating malignant thyroid nodules from benign ones. Journal of Ayub Medical College Abbottabad, 28(4), 644-649.

Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Haugen, B. R., Sawka, A. M., Alexander, E. K., Bible, K. C., Caturegli, P. D., & Doherty, G. (2017). The ATA guidelines on management of thyroid nodules and differentiated thyroid cancer task force review and recommendation on the proposed renaming of eFVPTC without invasion to NIFTP. Thyroid, 27, 481-483. DOI: https://doi.org/10.1089/thy.2016.0628

Smith-Bindman, R., Lebda, P., Feldstein, V. A., Sellami, D., Goldstein, R. B., Brasic, N., &Kornak, J. (2013). Risk of thyroid cancer based on thyroid ultrasound imaging characteristics: results of a population-based study. JAMA internal medicine, 173(19), 1788-1795. DOI: https://doi.org/10.1001/jamainternmed.2013.9245

Brito, J. P., Gionfriddo, M. R., Al Nofal, A., Boehmer, K. R., Leppin, A. L., Reading, C., ... & Murad, M. H. (2014). The accuracy of thyroid nodule ultrasound to predict thyroid cancer: systematic review and meta-analysis. The Journal of Clinical Endocrinology & Metabolism, 99(4), 1253-1263. DOI: https://doi.org/10.1210/jc.2013-2928

Shin, J. H., Baek, J. H., Chung, J., Ha, E. J., Kim, J. H., Lee, Y. H.,& Choi, Y. J. (2016). Ultrasonography diagnosis and imaging-based management of thyroid nodules: revised Korean Society of Thyroid Radiology consensus statement and recommendations. Korean journal of radiology, 17(3), 370-395. DOI: https://doi.org/10.3348/kjr.2016.17.3.370

Park, C. S., Kim, S. H., Jung, S. L., Kang, B. J., Kim, J. Y., Choi, J. J., .&Jeong, S. H. (2010). Observer variability in the sonographic evaluation of thyroid nodules. Journal of Clinical Ultrasound, 38(6), 287-293. DOI: https://doi.org/10.1002/jcu.20689

Acharya, U. R., Swapna, G., Sree, S. V., Molinari, F., Gupta, S., Bardales, R. H.,& Suri, J. S. (2014). A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technology in cancer research & treatment, 13(4), 289-301.

Shankar, K., Lakshmanaprabu, S. K., Gupta, D., Maseleno, A., & De Albuquerque, V. H. C. (2020). Optimal feature-based multi-kernel SVM approach for thyroid disease classification. The journal of supercomputing, 76(2), 1128-1143.

Gupta, N., Jain, R., Gupta, D., Khanna, A., & Khamparia, A. (2020). Modified ant lion optimization algorithm for improved diagnosis of thyroid disease. In Cognitive Informatics and Soft Computing (pp. 599-610). Springer, Singapore.

Song, W., Li, S., Liu, J., Qin, H., Zhang, B., Zhang, S., &Hao, A. (2018). Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE journal of biomedical and health informatics, 23(3), 1215-1224.

Chi, J., Walia, E., Babyn, P., Wang, J., Groot, G., &Eramian, M. (2017). Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. Journal of digital imaging, 30(4), 477-486. DOI: https://doi.org/10.1007/s10278-017-9997-y

B. Kitchenham, “Procedures for undertaking systematic reviews: joint technical report,” Dept. Comput. Sci., Keele Univ., Nat. ICT Australia, Keele, U.K., Tech. Rep. TR/SE-0401, 2004.

B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, “Systematic literature reviews in software engineering_A systematic literature review,'' Inf. Softw. Technol., vol. 51, no. 1, pp. 7_15, 2009. DOI: https://doi.org/10.1016/j.infsof.2008.09.009

Gharib, H., Papini, E., Paschke, R., Duick, D., Valcavi, R., Hegedüs, L., & Vitti, P. (2010). American Association of Clinical Endocrinologists, Associazione Medici Endocrinologi, and European Thyroid Association medical guidelines for clinical practice for the diagnosis and management of thyroid nodules. Endocrine Practice, 16(Supplement 1), 1-43. DOI: https://doi.org/10.4158/10024.GL

Santin, M., Brama, C., Théro, H., Ketheeswaran, E., El-Karoui, I., Bidault, F., ... & Blum, A. (2019). Detecting abnormal thyroid cartilages on CT using deep learning. Diagnostic and interventional imaging, 100(4), 251-257.

Zhou, H., Jin, Y., Dai, L., Zhang, M., Qiu, Y., Tian, J., & Zheng, J. (2020). Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images. European Journal of Radiology, 108992.

Prochazka, A., Gulati, S., Holinka, S., & Smutek, D. (2019). Classification of thyroid nodules in ultrasound images using direction-independent features extracted by two-threshold binary decomposition. Technology in cancer research & treatment, 18, 1533033819830748.

Zhang, S., Du, H., Jin, Z., Zhu, Y., Zhang, Y., Xie, F., ... & Luo, Y. (2020). A Novel Interpretable Computer-Aided Diagnosis System of Thyroid Nodules on Ultrasound Based on Clinical Experience. IEEE Access, 8, 53223-53231.

Guan, Q., Wang, Y., Du, J., Qin, Y., Lu, H., Xiang, J., & Wang, F. (2019). Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study. Annals of Translational Medicine, 7(7).

Wang, L., Yang, S., Yang, S., Zhao, C., Tian, G., Gao, Y., ... & Lu, Y. (2019). Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network. World journal of surgical oncology, 17(1), 1-9.

Moon, J. H., & Steinhubl, S. R. (2019). Digital medicine in thyroidology: a new era of managing thyroid disease. Endocrinology and Metabolism, 34(2), 124-131.

Gitto, S., Grassi, G., De Angelis, C., Monaco, C. G., Sdao, S., Sardanelli, F., ... & Mauri, G. (2019). A computer-aided diagnosis system for the assessment and characterization of low-to-high suspicion thyroid nodules on ultrasound. La radiologia medica, 124(2), 118-125.

Jeong, E. Y., Kim, H. L., Ha, E. J., Park, S. Y., Cho, Y. J., & Han, M. (2019). Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators. European radiology, 29(4), 1978-1985.

Kim, H. L., Ha, E. J., & Han, M. (2019). Real-world performance of computer-aided diagnosis system for thyroid nodules using ultrasonography. Ultrasound in medicine & biology, 45(10), 2672-2678.

Ko, S. Y., Lee, J. H., Yoon, J. H., Na, H., Hong, E., Han, K., ... & Lee, E. (2019). Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Head & Neck, 41(4), 885-891.

Liang, X. W., Cai, Y. Y., Yu, J. S., Liao, J. Y., & Chen, Z. Y. (2019). Update on thyroid ultrasound: a narrative review from diagnostic criteria to artificial intelligence techniques. Chinese Medical Journal, 132(16), 1974.

Liu, R., Li, H., Liang, F., Yao, L., Liu, J., Li, M., ... & Song, B. (2019). Diagnostic accuracy of different computer-aided diagnostic systems for malignant and benign thyroid nodules classification in ultrasound images: A systematic review and meta-analysis protocol. Medicine, 98(29). DOI: https://doi.org/10.1097/MD.0000000000016227

Song, J., Chai, Y. J., Masuoka, H., Park, S. W., Kim, S. J., Choi, J. Y., ... & Yi, K. H. (2019). Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules. Medicine, 98(15). DOI: https://doi.org/10.1097/MD.0000000000015133

Zhao, W. J., Fu, L. R., Huang, Z. M., Zhu, J. Q., & Ma, B. Y. (2019). Effectiveness evaluation of computer-aided diagnosis system for the diagnosis of thyroid nodules on ultrasound: A systematic review and meta-analysis. Medicine, 98(32). DOI: https://doi.org/10.1097/MD.0000000000016379

Li, X., Zhang, S., Zhang, Q., Wei, X., Pan, Y., Zhao, J., ... & Yang, F. (2019). Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study. The Lancet Oncology, 20(2), 193-201.

Orloff, L. A. (2020). Thyroid Ultrasound: Machine Beats Humans at Detecting Malignant Nodules. Clinical Thyroidology, 32(2), 69-71.

Zhang, B., Tian, J., Pei, S., Chen, Y., He, X., Dong, Y., ... & Zhang, S. (2019). Machine learning–assisted system for thyroid nodule diagnosis. Thyroid, 29(6), 858-867.

Wildman-Tobriner, B., Buda, M., Hoang, J. K., Middleton, W. D., Thayer, D., Short, R. G., ... & Mazurowski, M. A. (2019). Using artificial intelligence to revise ACR TI-RADS risk stratification of thyroid nodules: diagnostic accuracy and utility. Radiology, 292(1), 112-119.

Xu, L., Gao, J., Wang, Q., Yin, J., Yu, P., Bai, B., ... & Wan, M. (2020). Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis. European Thyroid Journal, 9(4), 186-193.

Wang, Y., Wei, K., & Wan, P. (2018). A method of ultrasonic image recognition for thyroid papillary carcinoma based on deep convolution neural network. NeuroQuantology, 16(5).

Ouyang, F. S., Guo, B. L., Ouyang, L. Z., Liu, Z. W., Lin, S. J., Meng, W., ... & Yang, S. M. (2019). Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules. European journal of radiology, 113, 251-257.

Sollini, M., Cozzi, L., Chiti, A., & Kirienko, M. (2018). Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: Where do we stand?. European journal of radiology, 99, 1-8. DOI: https://doi.org/10.1016/j.ejrad.2017.12.004

Song, W., Li, S., Liu, J., Qin, H., Zhang, B., Zhang, S., & Hao, A. (2018). Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE journal of biomedical and health informatics, 23(3), 1215-1224.

Yoo, Y. J., Ha, E. J., Cho, Y. J., Kim, H. L., Han, M., & Kang, S. Y. (2018). Computer-aided diagnosis of thyroid nodules via ultrasonography: initial clinical experience. Korean journal of radiology, 19(4), 665-672.

Lauria, A. P., Maddaloni, E., Briganti, S. I., Beretta, G. A., Perrella, E., Taffon, C., ... & Crescenzi, A. (2018). Differences between ATA, AACE/ACE/AME and ACR TI-RADS ultrasound classifications performance in identifying cytological high-risk thyroid nodules. European journal of endocrinology, 178(6), 595-603. DOI: https://doi.org/10.1530/EJE-18-0083

Zhang, Y., Zhang, M. B., Luo, Y. K., Li, J., Wang, Z. L., & Tang, J. (2018). The value of peripheral enhancement pattern for diagnosing thyroid cancer using contrast-enhanced ultrasound. International journal of endocrinology, 2018.

Liu, C., Huang, Y., Ozolek, J. A., Hanna, M. G., Singh, R., & Rohde, G. K. (2018). SetSVM: an approach to set classification in nuclei-based cancer detection. IEEE Journal of Biomedical and Health Informatics, 23(1), 351-361. DOI: https://doi.org/10.1109/JBHI.2018.2803793

Mauri, G., Pacella, C. M., Papini, E., Solbiati, L., Goldberg, S. N., Ahmed, M., & Sconfienza, L. M. (2019). Image-guided thyroid ablation: proposal for standardization of terminology and reporting criteria. Thyroid, 29(5), 611-618.

Caresio, C., Caballo, M., Deandrea, M., Garberoglio, R., Mormile, A., Rossetto, R., ... & Molinari, F. (2018). Quantitative analysis of thyroid tumors vascularity: A comparison between 3‐D contrast‐enhanced ultrasound and 3‐D Power Doppler on benign and malignant thyroid nodules. Medical physics, 45(7), 3173-3184. DOI: https://doi.org/10.1002/mp.12971

Sun, C., Zhang, Y., Chang, Q., Liu, T., Zhang, S., Wang, X., ... & Niu, L. (2020). Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images. Medical Physics, 47(9), 3952-3960.

Abdolali, F., Kapur, J., Jaremko, J. L., Noga, M., Hareendranathan, A. R., & Punithakumar, K. (2020). Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks. Computers in Biology and Medicine, 122, 103871.

Li, Y., Chen, P., Li, Z., Su, H., Yang, L., & Zhong, D. (2020). Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning. Artificial Intelligence in Medicine, 108, 101918.

Li, L. R., Du, B., Liu, H. Q., & Chen, C. (2021). Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Frontiers in Oncology, 10, 3360.

Yoon, J., Lee, E., Koo, J. S., Yoon, J. H., Nam, K. H., Lee, J., ... & Kwak, J. Y. (2020). Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer. PloS one, 15(11), e0242806.

Han, M., Ha, E. J., & Park, J. H. (2021). Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes. American Journal of Neuroradiology, 42(3), 559-565.

Guo, X., Zhao, H., & Tang, Z. (2020, April). An Improved Deep Learning Approach for Thyroid Nodule Diagnosis. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 296-299). IEEE.

Chen, D., Zhang, J., & Li, W. (2018, October). Thyroid Nodule Classification Using Two Levels Attention-Based Bi-Directional LSTM with Ultrasound Reports. In 2018 9th Intenational Conference on Information Technology in Medcine and Education (ITME) (pp. 309-312). IEEE.

González, J. R., Conci, A., Moran, M. B. H., Araujo, A. S., Paes, A., Damião, C., & Fiirst, W. G. (2019, November). Analysis of Static and Dynamic Infrared Images for Thyroid Nodules Investigation. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-7). IEEE.

Zhang, H., Zhao, C., Guo, L., Li, X., Luo, Y., Lu, J., & Xu, H. (2019, October). Diagnosis of Thyroid Nodules in Ultrasound Images Using Two Combined Classification Modules. In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (pp. 1-5). IEEE.

Bhatti, M. H., Khan, J., Khan, M. U. G., Iqbal, R., Aloqaily, M., Jararweh, Y., & Gupta, B. (2019). Soft computing-based EEG classification by optimal feature selection and neural networks. IEEE Transactions on Industrial Informatics, 15(10), 5747-5754.

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., ... & Dong, J. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131. DOI: https://doi.org/10.1016/j.cell.2018.02.010

Long, E., Lin, H., Liu, Z., Wu, X., Wang, L., Jiang, J., ... & Li, J. (2017). An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nature biomedical engineering, 1(2), 1-8. DOI: https://doi.org/10.1038/s41551-016-0024

Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine, 25(1), 65.

Acharya, U. R., Swapna, G., Sree, S. V., Molinari, F., Gupta, S., Bardales, R. H., ... & Suri, J. S. (2014). A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technology in cancer research & treatment, 13(4), 289-301. DOI: https://doi.org/10.7785/tcrt.2012.500381

Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University 33(2004),1-26.

Giannoula, E., Iakovou, I., Katsikavelas, I., Antoniou, P., Raftopoulos, V., Chatzipavlidou, V., ... & Bamidis, P. (2020). A mobile app for thyroid cancer patients aiming to enhance their quality of life: protocol for a quasiexperimental interventional pilot study. JMIR research protocols, 9(3), e13409.

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2021-12-31

How to Cite

Ilyas, M., Malik, H., Adnan, M., Bashir, U., Bukhari, W. A., Khan, M. I. A., & Ahmad, A. (2021). Deep Learning based Classification of Thyroid Cancer using Different Medical Imaging Modalities : A Systematic Review. VFAST Transactions on Software Engineering, 9(4), 1–17. https://doi.org/10.21015/vtse.v9i4.736