Artificial Intelligence in Oncology- Technologies being used and scope in India


  • Anushka Sharma RUHS CMS Jaipur
  • Shivangi Sharma McAfee LLC, Bangalore
  • Mridula Trehan NIMS Dental College & Hospital, NIMS University, Jaipur, Rajasthan
  • Sunil Sharma NIMS Dental College & Hospital, NIMS University, Jaipur



Cancer is a top cause of mortality in the world. Late detection and remissions are the biggest threats facing patients. Early detection of the same and a prediction of the prognosis and recurrence is one of the best chances we have against the fight with the disease. It has been found that artificial intelligence (AI) could be more accurate than even multivariate and statistical analysis which are themselves more accurate than empirical analysis. There has been a recent surge in literature pertaining to the use of machine learning and deep learning models for the diagnosis and prognosis of cancers. This article comprises a look at the AI technologies being used with respect to three of the most common cancers in the world - breast cancer, lung cancer, and gastric cancer. It also further delves the status of AI for Oncology in developing nations like India and the opportunities and the challenges facing an wide adoption of AI in such nations.

Author Biographies

Anushka Sharma, RUHS CMS Jaipur

III Year MBBS Student

Shivangi Sharma , McAfee LLC, Bangalore

B.E.(Hons.) Computer Science, Data Analyst, McAfee LLC,

Mridula Trehan, NIMS Dental College & Hospital, NIMS University, Jaipur, Rajasthan

 Dean & Principal, Professor & Head,

Department of Orthodontics & Dentofacial Orthopaedics,

Sunil Sharma, NIMS Dental College & Hospital, NIMS University, Jaipur

Pro- President, Professor & Head, Department of Oral & Maxillofacial Surgery, 


References -

World Health Organization. (2018). Global Health Observatory. Geneva: World Health Organization; 2018.

Dhillon, P. K., Mathur, P., Nandakumar, A., Fitzmaurice, C., Kumar, G. A., Mehrotra, R., ... & Thakur, J. S. (2018). The burden of cancers and their variations across the states of India: the Global Burden of Disease Study 1990–2016. The Lancet Oncology, 19(10), 1289-1306.

Mahajan, A., Vaidya, T., Gupta, A., Rane, S., & Gupta, S. (2019). Artificial intelligence in healthcare in developing nations: The beginning of a transformative journey. Cancer Research, Statistics, and Treatment, 2(2), 182.

Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016). Radiomics: images are more than pictures, they are data. Radiology, 278(2), 563-577.

Allahyar, A., Ubels, J., & De Ridder, J. (2019). A data-driven interactome of synergistic genes improves network-based cancer outcome prediction. PLoS computational biology, 15(2), e1006657.

Mitchell, M. J., Jain, R. K., & Langer, R. (2017). Engineering and physical sciences in oncology: challenges and opportunities. Nature Reviews Cancer, 17(11), 659.

Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500-510.

Saritas, I. (2012). Prediction of breast cancer using artificial neural networks. Journal of Medical Systems, 36(5), 2901-2907.

Atieh Graham, D. M., McNamara, D. M., Waintraub, S. E., Goldberg, S. L., Norden, A. D., Hervey, J., ... & Jungbluth, N. (2018). 1589P Are treatment recommendations provided by cognitive computing supported by real world data (Watson for Oncology with Cota RWE) concordant with expert opinions?. Annals of Oncology, 29(suppl_8), mdy297-031.

Sathe, P., Bombay, M., Mani, G. S., Kalathil, D., & Phadtare, A. (2008). Cancer Detection using Machine Learning.

Liu, M. C., Oxnard, G. R., Klein, E. A., Swanton, C., Seiden, M. V., Liu, M. C., ... & Yeatman, T. J. (2020). Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Annals of Oncology.

Beck, A. H., Sangoi, A. R., Leung, S., Marinelli, R. J., Nielsen, T. O., Van De Vijver, M. J., ... & Koller, D. (2011). Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Science translational medicine, 3(108), 108ra113-108ra113.

Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Gräf, S., Chin, S. F., ... & Johnson, N. (2012). Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Science translational medicine, 4(157), 157ra143-157ra143.

Hou, M. F., Chuang, H. Y., Ou-Yang, F., Wang, C. Y., Huang, C. L., Fan, H. M., ... & Huang, T. J. (2002). Comparison of breast mammography, sonography and physical examination for screening women at high risk of breast cancer in Taiwan. Ultrasound in medicine & biology, 28(4), 415-420.

Haddadnia, J., Hashemian, M., & Hassanpour, K. (2012). Diagnosis of breast cancer using a combination of genetic algorithm and artificial neural network in medical infrared thermal imaging. Iranian Journal of Medical Physics, 9(4), 265-274.

Dheeba, J., & Selvi, S. T. (2011, December). A CAD system for breast cancer diagnosis using modified genetic algorithm optimized artificial neural network. In International Conference on Swarm, Evolutionary, and Memetic Computing (pp. 349-357). Springer, Berlin, Heidelberg.

Sadoughi, F., Kazemy, Z., Hamedan, F., Owji, L., Rahmanikatigari, M., & Azadboni, T. T. (2018). Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review. Breast Cancer: Targets and Therapy, 10, 219.

Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., ... & Wallis, M. G. (2019). Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. JNCI: Journal of the National Cancer Institute, 111(9), 916-922.

Zafiropoulos, E., Maglogiannis, I., & Anagnostopoulos, I. (2006, June). A support vector machine approach to breast cancer diagnosis and prognosis. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 500-507). Springer, Boston, MA.

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.

Russell, S., & Norvig, P. (2002). Artificial intelligence: a modern approach.

Karabatak, M. (2015). A new classifier for breast cancer detection based on Naïve Bayesian. Measurement, 72, 32-36.

Saini, S., & Vijay, R. (2015, April). Mammogram analysis using feed-forward back propagation and cascade-forward back propagation artificial neural network. In 2015 Fifth International Conference on Communication Systems and Network Technologies (pp. 1177-1180). IEEE.

Medjahed, S. A., Saadi, T. A., & Benyettou, A. (2013). Breast cancer diagnosis by using k-nearest neighbor with different distances and classification rules. International Journal of Computer Applications, 62(1).

Huang, S., Yang, J., Fong, S., & Zhao, Q. (2020). Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Letters, 471, 61-71.

Abu-Naser, S. S., Almasri, A., Abu Sultan, Y. S., & Zaqout, I. S. (2011). A prototype decision support system for optimizing the effectiveness of elearning in educational institutions.

Nasser, I. M., & Abu-Naser, S. S. (2019). Lung Cancer Detection Using Artificial Neural Network. International Journal of Engineering and Information Systems (IJEAIS), 3(3), 17-23.

Wang, S., Yang, D. M., Rong, R., Zhan, X., Fujimoto, J., Liu, H., ... & Xiao, G. (2019). Artificial intelligence in lung cancer pathology image analysis. Cancers, 11(11), 1673.

Wang, S., Chen, A., Yang, L., Cai, L., Xie, Y., Fujimoto, J., ... & Xiao, G. (2018). Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Scientific reports, 8(1), 1-9.

Li, Z., Hu, Z., Xu, J., Tan, T., Chen, H., Duan, Z., ... & Tang, Y. (2018). Computer-aided diagnosis of lung carcinoma using deep learning-a pilot study. arXiv preprint arXiv:1803.05471.

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., ... & Tsirigos, A. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature medicine, 24(10), 1559-1567.

Espinoza, J. L., & Dong, L. T. (2020). Artificial Intelligence Tools for Refining Lung Cancer Screening. Journal of Clinical Medicine, 9(12), 3860.

Wang, X., Janowczyk, A., Zhou, Y., Thawani, R., Fu, P., Schalper, K., ... & Madabhushi, A. (2017). Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images. Scientific reports, 7(1), 1-10.

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., ... & Van Arnam, J. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell reports, 23(1), 181-193.

Wang, S., Wang, T., Yang, L., Yang, D. M., Fujimoto, J., Yi, F., ... & Moran, C. (2019). ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine, 50, 103-110.

Islami, F., DeSantis, C. E., & Jemal, A. (2019). Incidence trends of esophageal and gastric cancer subtypes by race, ethnicity, and age in the United States, 1997–2014. Clinical Gastroenterology and Hepatology, 17(3), 429-439.

Steevens, J., Botterweck, A. A., Dirx, M. J., van den Brandt, P. A., & Schouten, L. J. (2010). Trends in incidence of oesophageal and stomach cancer subtypes in Europe. European journal of gastroenterology & hepatology, 22(6), 669-678.

Ferlay, J., Colombet, M., Soerjomataram, I., Mathers, C., Parkin, D. M., Piñeros, M., ... & Bray, F. (2019). Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. International journal of cancer, 144(8), 1941-1953.

Niu, P. H., Zhao, L. L., Wu, H. L., Zhao, D. B., & Chen, Y. T. (2020). Artificial intelligence in gastric cancer: Application and future perspectives. World Journal of Gastroenterology, 26(36), 5408.

Jiang, Y., Xie, J., Han, Z., Liu, W., Xi, S., Huang, L., ... & Yu, J. (2018). Immunomarker support vector machine classifier for prediction of gastric cancer survival and adjuvant chemotherapeutic benefit. Clinical Cancer Research, 24(22), 5574-5584.

Lu, F., Chen, Z., Yuan, X., Li, Q., Du, Z., Luo, L., & Zhang, F. (2017, November). MMHG: Multi-modal Hypergraph Learning for Overall Survival After D2 Gastrectomy for Gastric Cancer. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 164-169). IEEE.

Kangi, A. K., & Bahrampour, A. (2018). Predicting the Survival of Gastric Cancer Patients Using Artificial and Bayesian Neural Networks. Asian Pacific journal of cancer prevention: APJCP, 19(2), 487.

Zhang, W., Fang, M., Dong, D., Wang, X., Ke, X., Zhang, L., ... & Shan, X. (2020). Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer. Radiotherapy and Oncology, 145, 13-20.

Liu, B., Tan, J., Wang, X., & Liu, X. (2018). Identification of recurrent risk-related genes and establishment of support vector machine prediction model for gastric cancer. Neoplasma, 65(3), 360-366.

Bollschweiler, E. H., Mönig, S. P., Hensler, K., Baldus, S. E., Maruyama, K., & Hölscher, A. H. (2004). Artificial neural network for prediction of lymph node metastases in gastric cancer: a phase II diagnostic study. Annals of surgical oncology, 11(5), 506-511.

Hensler, K., Waschulzik, T., Mönig, S. P., Maruyama, K., Hölscher, A. H., & Bollschweiler, E. (2005). Quality-assured Efficient Engineering of Feedforward Neural Networks (QUEEN). Methods of information in medicine, 44(05), 647-654.

Jagric, T., Potrc, S., & Jagric, T. (2010). Prediction of liver metastases after gastric cancer resection with the use of learning vector quantization neural networks. Digestive diseases and sciences, 55(11), 3252-3261.

Li, Y., Li, X., Xie, X., & Shen, L. (2018, April). Deep learning based gastric cancer identification. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 182-185). IEEE..

Sharma, H., Zerbe, N., Klempert, I., Hellwich, O., & Hufnagl, P. (2017). Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Computerized Medical Imaging and Graphics, 61, 2-13.

P.M. Speight, A. Elliott, J.A. Jullien, M.C. Downer, J.M. Zakzrewska. The use of artificial intelligence to identify people at risk of oral cancer and precancer. Br Dent J, 179 (1995), pp. 382-387

H.J. van Staveren, R.L. van Veen, O.C. Speelman, M.J. Witjes, W.M. Star, J.L.Roodenburg. Classification of clinical autofluorescence spectra of oral leukoplakia using an artificial neural network: a pilot study. Oral Oncol, 36 (2000), pp. 286-293

R.R. Paul, A. Mukherjee, P.K. Dutta, S. Banerjee, M. Pal, J. Chatterjee, et al. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition. J Clin Pathol, 58 (2005), pp. 932-938

B.H. Kann, S. Aneja, G.V. Loganadane, J.R. Kelly, S.M. Smith, R.H. Decker, et al. Pretreatment identification of head and neck cancer nodal metastasis and extranodal extension using deep learning neural networks. Sci Rep, 8 (2018), p. 14036

S.W. Chang, S. Abdul-Kareem, A. Merican, R. Zain. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinf, 14 (2013), p. 170

M.Y. Chen, J.W. Chen, L.W. Wu, K.C. Huang, J.Y. Chen, W.S. Wu, et al. Carcinogenesis of male oral submucous fibrosis alters salivary microbiomes. J Dent Res (2020)

Mahajan, A., Goh, V., Basu, S., Vaish, R., Weeks, A. J., Thakur, M. H., & Cook, G. J. (2015). Bench to bedside molecular functional imaging in translational cancer medicine: to image or to imagine?. Clinical radiology, 70(10), 1060-1082. n.d. AINDRA | Home. [online] Available at: <> [Accessed 9 December 2020]. n.d. Niramai – A Novel Breast Cancer Screening Solution. [online] Available at: <> [Accessed 9 December 2020].

Bhattacharya, S., Pradhan, K. B., Bashar, M. A., Tripathi, S., Semwal, J., Marzo, R. R., ... & Singh, A. (2019). Artificial intelligence enabled healthcare: A hype, hope or harm. Journal of Family Medicine and Primary Care, 8(11), 3461.

2018. [online] Available at: < embrace-ai-on-a-war-footing> [Accessed 9 December 2020].

Medium. n.d. Medium. [online] Available at: < niti-aayogs-national-strategy-for-artificial-intelligence-india-5d6 865e95090> [Accessed 9 December 2020].




How to Cite

Sharma, A. . ., Shivangi Sharma, Mridula Trehan, & Sunil Sharma. (2021). Artificial Intelligence in Oncology- Technologies being used and scope in India. UNIVERSITY JOURNAL MAXILLOFACIAL SURGERY AND ORAL SCIENCES, 1(2).