AI in Oncology - Precision Therapy & Prognosis

Anirban Chakraborty

doi:10.47494/mesb.2021.19.945

(1) Anirban Chakraborty Research Scholar, Department of Artificial Intelligence, Lovely Professional University, Jalandhar, Punjab, India
India
(*) Corresponding Author

DOI:

https://doi.org/10.47494/mesb.2021.19.945

Keywords:

Machines, Deep Learning, Artificial Intelligence, Oncology, Cancer, Profoundly

Abstract

Artificial intelligence (AI) has strong logical reasoning abilities and the ability to learn on its own, and it can mimic the human brain's thought process. Machine learning and other AI technologies have the potential to greatly enhance the existing method of anticancer medicine development. However, AI currently has several limits. This study investigates the evolution of artificial intelligence technologies in anti-cancer therapeutic research, such as deep learning and machine learning. At the same time, we are optimistic about AI's future.

Downloads

Download data is not yet available.

References

C. M. Lo, U. Iqbal, Y. J. Li, Cancer quantification from data mining to artificial intelligence, Comput. Methods Programs Biomed. 145 (2017) A1.

J. Abbasi, Artificial intelligence tools for Sepsis and Cancer, JAMA 320 (22) (2018) 2303.

W. L. Bi, A. Hosny, M. B. Schabath, M. L. Giger, N. J. Birkbak, A. Mehrtash, T. Allison, O. Arnaout, C. Abbosh, I. F. Dunn, R. H. Mak, R. M. Tamimi, C. M. Tempany, C. Swanton, U. Hoffmann, L. H. Schwartz, R. J. Gillies, R. Y. Huang, H. Aerts, Artificial intelligence in cancer imaging: clinical challenges and applications, CA Cancer J. Clin. 69 (2) (2019) 127–157.

A. Tartar, A. Akan, N. Kilic, A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers, Conference proceedings : … annual International Conference of the IEEE engineering in medicine and biology society, IEEE Engineering in Medicine and Biology Society. Annual Conference 2014 (2014) 4651–4654.

van der Waal, Skin cancer diagnosed using artificial intelligence on clinical images, Oral Dis. 24 (6) (2018) 873–874.

Li, B. Hu, H. Li, B. You, Application of artificial intelligence in the diagnosis of multiple primary lung cancer, Thorac. Cancer 10 (11) (2019) 2168–2174.

N. Houssami, G. Kirkpatrick-Jones, N. Noguchi, C. I. Lee, Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice, Expert Rev. Med. Devices 16 (5) (2019) 351–362.

G. V. Sherbet, W. L. Woo, S. Dlay, Application of artificial intelligence-based technology in Cancer management: a commentary on the deployment of artificial neural networks, Anticancer Res. 38 (12) (2018) 6607–6613.

A. P. Lind, P. C. Anderson, Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties, PLoS One 14 (7) (2019) e0219774.

Y. Wang, Z. Wang, J. Xu, J. Li, S. Li, M. Zhang, D. Yang, Systematic identification of non-coding pharmacogenomic landscape in cancer, Nat. Commun. 9 (1) (2018) 3192.

M. A. Hossain, S. M. Saiful Islam, J. M. W. Quinn, F. Huq, M. A. Moni, Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality, J. Biomed. Inform. 100 (2019) 103313.

E. S. Paik, J. W. Lee, J. Y. Park, J. H. Kim, M. Kim, T. J. Kim, C. H. Choi, B. G. Kim, D. S. Bae, S. W. Seo, Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods, J. Gynecol. Oncol. 30 (4) (2019) e65.

J. F. McDonald, Back to the future - the integration of big data with machine learning is re-establishing the importance of predictive correlations in ovarian cancer diagnostics and therapeutics, Gynecol. Oncol. 149 (2) (2018) 230–231.

Q. Li, L. Qi, Q. X. Feng, C. Liu, S. W. Sun, J. Zhang, G. Yang, Y. Q. Ge, Y. D. Zhang, X. S. Liu, Machine learning-based computational models derived from large-scale radiographic-radiomic images can help predict adverse histopathological status of gastric Cancer, Clin. Transl. Gastroenterol. 10 (10) (2019) e00079.

J. Taninaga, Y. Nishiyama, K. Fujibayashi, T. Gunji, N. Sasabe, K. Iijima, T. Naito, Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: a case-control study, Sci. Rep. 9 (1) (2019) 12384.

C. Liu, L. Qi, Q. X. Feng, S. W. Sun, Y. D. Zhang, X. S. Liu, Performance of a machine learning-based decision model to help clinicians decide the extent of lymphadenectomy (D1 vs. D2) in gastric cancer before surgical resection, Abdom. Radiol. (NY) 44 (9) (2019) 3019–3029.

A. Stanzione, R. Cuocolo, R. Del Grosso, A. Nardiello, V. Romeo, A. Travaglino, A. Raffone, G. Bifulco, F. Zullo, L. Insabato, S. Maurea, P. P. Mainenti, Deep myometrial infiltration of endometrial Cancer on MRI: a radiomics-powered machine learning pilot study, Acad. Radiol. (2020).

E. Gunakan, S. Atan, A. N. Haberal, I. A. Kucukyildiz, E. Gokce, A. Ayhan, A novel prediction method for lymph node involvement in endometrial cancer: machine learning, Int. J. Gynecol. Cancer 29 (2) (2019) 320–324.

J. T. Beck, M. Rammage, G. P. Jackson, A. M. Preininger, I. Dankwa-Mullan, M. C. Roebuck, A. Torres, H. Holtzen, S. E. Coverdill, M. P. Williamson, Q. Chau, K. Rhee, M. Vinegra, Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community Cancer center, JCO Clin. Cancer Inform. 4 (2020) 50–59.

S. L. Goldenberg, G. Nir, S. E. Salcudean, A new era: artificial intelligence and machine learning in prostate cancer, Nat. Rev. Urol. 16 (7) (2019) 391–403.

K. Leventakos, J. Helgeson, A. S. Mansfield, E. Deering, A. Schwecke, A. Adjei, J. Molina, C. Hocum, T. Halfdanarson, R. Marks, K. Parikh, K. Pomerleau, S. Coverdill, M. Rammage, T. Haddad, Implementation of artificial intelligence (AI) for lung cancer clinical trial matching in a tertiary cancer center, Ann. Oncol. 30 (Suppl 2) (2019) ii74.

G. Chen, A. Tsoi, H. Xu, W. J. Zheng, Predict effective drug combination by deep belief network and ontology fingerprints, J. Biomed. Inform. 85 (2018) 149–154.

M. N. Levine, G. Alexander, A. Sathiyapalan, A. Agrawal, G. Pond, Learning health system for breast Cancer: pilot project experience, JCO Clin. Cancer Inform. 3 (2019) 1–11.

M. Smail-Tabbone, B. Rance, I. Y. S. O. B. Section Editors for the I. Translational, Contributions from the 2018 literature on bioinformatics and translational informatics, Yearb. Med. Inform. 28 (1) (2019) 190–193.

Zhu, M. C. M. Lin, W. Fan, L. Tian, J. Wang, S. S. Ng, M. Wang, H. Kung, D. Li, An intronic polymorphism in GRP78 improves chemotherapeutic prediction in nonsmall cell lung cancer, Chest 141 (6) (2012) 1466–1472.

A. J. Pantuck, D. -K. Lee, T. Kee, P. Wang, S. Lakhotia, M. H. Silverman, C. Mathis, A. Drakaki, A. S. Belldegrun, C. -M. Ho, D. Ho, Modulating BET bromodomain inhibitor ZEN-3694 and enzalutamide combination dosing in a metastatic prostate Cancer patient using CURATE. AI, an artificial intelligence platform, Adv. Ther. 1 (6) (2018) 1800104, https://doi. org/10. 1002/adtp. 201800104.

D. C. Gulhan, J. J. Lee, G. E. M. Melloni, I. Cortes-Ciriano, P. J. Park, Detecting the mutational signature of homologous recombination deficiency in clinical samples, Nat. Genet. 51 (5) (2019) 912–919.

S. N. Dorman, K. Baranova, J. H. Knoll, B. L. Urquhart, G. Mariani, M. L. Carcangiu, P. K. Rogan, Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning, Mol. Oncol. 10 (1) (2016) 85–100.

X. Tang, Y. Huang, J. Lei, H. Luo, X. Zhu, The single-cell sequencing: new developments and medical applications, Cell Biosci. 9 (2019) 53.

H. Peng, D. Dong, M. -J. Fang, L. Li, L. -L. Tang, L. Chen, W. -F. Li, Y. -P. Mao, W. Fan, L. -Z. Liu, L. Tian, A. -H. Lin, Y. Sun, J. Tian, J. Ma, Prognostic value of deep learning PET/CT-Based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma, Clin. Cancer Res. 25 (2019) 4271–4279.

C. Fiorino, M. Guckemberger, M. Schwarz, U. A. van der Heide, B. Heijmen, Technology-driven research for radiotherapy innovation, Mol. Oncol. (2020), https://doi. org/10. 1002/1878-0261. 12659 [Epub ahead of print].

B. Lou, S. Doken, T. Zhuang, D. Wingerter, M. Gidwani, N. Mistry, L. Ladic, A. Kamen, M. E. Abazeed, An image-based deep learning framework for individualizing radiotherapy dose, Lancet Digit. Health 1 (3) (2019) e136–e147.

P. Meyer, V. Noblet, C. Mazzara, A. Lallement, Survey on deep learning for radiotherapy, Comput. Biol. Med. 98 (2018) 126–146.

L. Lin, Q. Dou, Y. M. Jin, G. Q. Zhou, Y. Q. Tang, W. L. Chen, B. A. Su, F. Liu, C. J. Tao, N. Jiang, J. Y. Li, L. L. Tang, C. M. Xie, S. M. Huang, J. Ma, P. A. Heng, J. T. S. Wee, M. L. K. Chua, H. Chen, Y. Sun, Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma, Radiology 291 (3) (2019) 677–686.

K. H. Cha, L. Hadjiiski, H. P. Chan, A. Z. Weizer, A. Alva, R. H. Cohan, E. M. Caoili, C. Paramagul, R. K. Samala, Bladder Cancer treatment response assessment in CT using radiomics with deep-learning, Sci. Rep. 7 (1) (2017) 8738.

A. Babier, J. J. Boutilier, A. L. McNiven, T. C. Y. Chan, Knowledge-based automated planning for oropharyngeal cancer, Med. Phys. 45 (7) (2018) 2875–2883.

P. Jabbari, N. Rezaei, Artificial intelligence and immunotherapy, Expert Rev. Clin. Immunol. 15 (7) (2019) 689–691.

S. Trebeschi, S. G. Drago, N. J. Birkbak, I. Kurilova, A. M. Calin, A. Delli Pizzi, F. Lalezari, D. M. J. Lambregts, M. W. Rohaan, C. Parmar, E. A. Rozeman, K. J. Hartemink, C. Swanton, J. Haanen, C. U. Blank, E. F. Smit, R. G. H. Beets-Tan, H. Aerts, Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers, Ann. Oncol. 30 (6) (2019) 998–1004.

J. Abbasi, Electronic nose" predicts immunotherapy response, JAMA 322 (18) (2019) 1756.

S. Tan, D. Li, X. Zhu, Cancer immunotherapy: pros, cons and beyond, Biomed. Pharmacother. 124 (2020) 109821.

R. Sun, E. J. Limkin, M. Vakalopoulou, L. Dercle, S. Champiat, S. R. Han, L. Verlingue, D. Brandao, A. Lancia, S. Ammari, A. Hollebecque, J. -Y. Scoazec, A. Marabelle, C. Massard, J. -C. Soria, C. Robert, N. Paragios, E. Deutsch, C. Ferté, A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study, Lancet Oncol. 19 (9) (2018) 1180–1191.

B. Bulik-Sullivan, J. Busby, C. D. Palmer, M. J. Davis, T. Murphy, A. Clark, M. Busby, F. Duke, A. Yang, L. Young, N. C. Ojo, K. Caldwell, J. Abhyankar, T. Boucher, M. G. Hart, V. Makarov, V. T. Montpreville, O. Mercier, T. A. Chan, G. Scagliotti, P. Bironzo, S. Novello, N. Karachaliou, R. Rosell, I. Anderson, N. Gabrail, J. Hrom, C. Limvarapuss, K. Choquette, A. Spira, R. Rousseau, C. Voong, N. A. Rizvi, E. Fadel, M. Frattini, K. Jooss, M. Skoberne, J. Francis, R. Yelensky, Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification, Nat. Biotechnol. (2018), https://doi. org/10. 1038/nbt. 4313 [Epub ahead of print].

L. Hu, D. Bell, S. Antani, Z. Xue, K. Yu, M. P. Horning, N. Gachuhi, B. Wilson, M. S. Jaiswal, B. Befano, L. R. Long, R. Herrero, M. H. Einstein, R. D. Burk, M. Demarco, J. C. Gage, A. C. Rodriguez, N. Wentzensen, M. Schiffman, An observational study of deep learning and automated evaluation of cervical images for Cancer screening, J. Natl. Cancer Inst. 111 (2019) 923–932.

M. Bahl, R. Barzilay, A. B. Yedidia, N. J. Locascio, L. Yu, C. D. Lehman, High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision, Radiology 286 (3) (2018) 810–818.

C. Liu, X. Liu, F. Wu, M. Xie, Y. Feng, C. Hu, Using artificial intelligence (Watson for oncology) for treatment recommendations amongst chinese patients with lung Cancer: feasibility study, J. Med. Internet Res. 20 (9) (2018) e11087.

G. Bogani, D. Rossetti, A. Ditto, F. Martinelli, V. Chiappa, L. Mosca, U. Leone Roberti Maggiore, S. Ferla, D. Lorusso, F. Raspagliesi, Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer, J. Gynecol. Oncol. 29 (5) (2018) e66.

J. A. Golden, Deep learning algorithms for detection of lymph node metastases from breast Cancer: helping artificial intelligence Be seen, JAMA 318 (22) (2017) 2184–2186.

S. Walsh, E. E. C. de Jong, J. E. van Timmeren, A. Ibrahim, I. Compter, J. Peerlings, S. Sanduleanu, T. Refaee, S. Keek, R. Larue, Y. van Wijk, A. J. G. Even, A. Jochems, M. S. Barakat, R. T. H. Leijenaar, P. Lambin, Decision support systems in oncology, JCO Clin. Cancer Inform. 3 (2019) 1–9.