Volume 15, Issue 3 ( Special Issue (AI in Medicine) - August 2023 2023)                   Iranian Journal of Blood and Cancer 2023, 15(3): 13-41 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Delshad M, Omrani M A, Pourbagheri-Sigaroodi A, Bashash D. Oncology in the modern era: Artificial Intelligence is reshaping cancer diagnosis, prognosis and treatment. Iranian Journal of Blood and Cancer 2023; 15 (3) :13-41
URL: http://ijbc.ir/article-1-1439-en.html
Oncology in the modern era: Artificial Intelligence is reshaping cancer diagnosis, prognosis and treatment
Mahda Delshad1 , Mohammad Amin Omrani2 , Atieh Pourbagheri-Sigaroodi3 , Davood Bashash * 4
1- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Department of Laboratory Sciences, School of Allied Medical Sciences, Zanjan University of Medical Sciences, Zanjan, Iran.
2- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
3- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
4- Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. , D.bashash@sbmu.ac.ir
Abstract:   (380 Views)
The field of cancer research has been profoundly impacted by the utilization of artificial intelligence (AI), particularly through the analysis of medical records encompassing genomics, transcriptomics, proteomics, and imaging data. Subdomains of AI, such as machine learning (ML) and deep learning (DL), possess the capability to analyze intricate patterns within these records. This allows for groundbreaking advancements in cancer diagnosis, prognosis, and treatment by extracting valuable insights from sources such as histology and radiology imaging. The integration of AI-based models has led to improved prediction, diagnosis, and even treatment of various types of cancer, resulting in enhanced performance within the field of oncology. However, AI also faces challenges including ethical and legal considerations, data quality and accessibility, and issues pertaining to model interpretability. It is crucial to develop and evaluate AI-based systems in collaboration with clinicians and researchers to ensure their safety, reliability, and validity in cancer research.
 
Keywords: Artificial intelligence (AI), Machine learning, Deep learning, Cancer
Full-Text [PDF 9212 kb]   (231 Downloads)    
: Review Article | Subject: AI in Medicine
Received: 2023/07/1 | Accepted: 2023/08/1 | Published: 2023/08/7
References
1. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine. 2006;27(4):12-.
2. AI vs Machine Learning vs Deep Learning: Know the Differences Last updated on Feb 23, 2023 [Available from: https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/ai-vs-machine-learning-vs-deep-learning.
3. Artificial Intelligence vs. Machine Learning vs. Deep Learning: What's the Difference? 2023 [Available from: https://builtin.com/artificial-intelligence/ai-vs-machine-learning.
4. Güneş ED, Yaman H, Cekyay B, Verter V. Matching patient and physician preferences in designing a primary care facility network. Journal of the Operational Research Society. 2014;65:483-96. [DOI:10.1057/jors.2012.71]
5. Ekins S. The next era: deep learning in pharmaceutical research. Pharmaceutical research. 2016;33(11):2594-603. [DOI:10.1007/s11095-016-2029-7]
6. Jing Y, Bian Y, Hu Z, Wang L, Xie X-QS. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. The AAPS journal. 2018;20:1-10. https://doi.org/10.1208/s12248-018-0243-4 [DOI:10.1208/s12248-018-0210-0]
7. Papachristou I, Bosanquet N. Improving the prevention and diagnosis of melanoma on a national scale: A comparative study of performance in the United Kingdom and Australia. Journal of Public Health Policy. 2020;41:28-38. [DOI:10.1057/s41271-019-00187-0]
8. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama. 2016;316(22):2402-10. [DOI:10.1001/jama.2016.17216]
9. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer letters. 2020;471:61-71. [DOI:10.1016/j.canlet.2019.12.007]
10. Patel SK, George B, Rai V. Artificial intelligence to decode cancer mechanism: beyond patient stratification for precision oncology. Frontiers in Pharmacology. 2020;11:1177. [DOI:10.3389/fphar.2020.01177]
11. Song H, Ruan C, Xu Y, Xu T, Fan R, Jiang T, Cao M, Song J. Survival stratification for colorectal cancer via multi-omics integration using an autoencoder-based model. Experimental Biology and Medicine. 2022;247(11):898-909. [DOI:10.1177/15353702211065010]
12. Griffith M, Miller CA, Griffith OL, Krysiak K, Skidmore ZL, Ramu A, Walker JR, Dang HX, Trani L, Larson DE. Optimizing cancer genome sequencing and analysis. Cell systems. 2015;1(3):210-23. [DOI:10.1016/j.cels.2015.08.015]
13. Gaublomme JT, Li B, McCabe C, Knecht A, Yang Y, Drokhlyansky E, Van Wittenberghe N, Waldman J, Dionne D, Nguyen L. Nuclei multiplexing with barcoded antibodies for single-nucleus genomics. Nature communications. 2019;10(1):2907. [DOI:10.1038/s41467-019-10756-2]
14. Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, Schüffler PJ, Grolimund D, Buhmann JM, Brandt S. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature methods. 2014;11(4):417-22. [DOI:10.1038/nmeth.2869]
15. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202-14. [DOI:10.1016/j.cell.2015.05.002]
16. Moffitt JR, Hao J, Wang G, Chen KH, Babcock HP, Zhuang X. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proceedings of the National Academy of Sciences. 2016;113(39):11046-51. [DOI:10.1073/pnas.1612826113]
17. Wang X, Yu G, Yan Z, Wan L, Wang W, Lizhen LCC. Lung cancer subtype diagnosis by fusing image-genomics data and hybrid deep networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2021. [DOI:10.1109/TCBB.2021.3132292]
18. Vanderbilt CM, Bowman AS, Middha S, Petrova-Drus K, Tang Y-W, Chen X, Wang Y, Chang J, Rekhtman N, Busam KJ. Defining novel DNA virus-tumor associations and genomic correlates using prospective clinical tumor/normal matched sequencing data. The Journal of Molecular Diagnostics. 2022;24(5):515-28. [DOI:10.1016/j.jmoldx.2022.01.011]
19. Buzdin A, Sorokin M, Garazha A, Sekacheva M, Kim E, Zhukov N, Wang Y, Li X, Kar S, Hartmann C, editors. Molecular pathway activation-new type of biomarkers for tumor morphology and personalized selection of target drugs. Seminars in Cancer Biology; 2018: Elsevier. [DOI:10.1016/j.semcancer.2018.06.003]
20. Supplitt S, Karpinski P, Sasiadek M, Laczmanska I. Current achievements and applications of transcriptomics in personalized cancer medicine. International Journal of Molecular Sciences. 2021;22(3):1422. [DOI:10.3390/ijms22031422]
21. Montani F, Marzi MJ, Dezi F, Dama E, Carletti RM, Bonizzi G, Bertolotti R, Bellomi M, Rampinelli C, Maisonneuve P. miR-Test: a blood test for lung cancer early detection. Journal of the National Cancer Institute. 2015;107(6):djv063. [DOI:10.1093/jnci/djv063]
22. Nadal E, Truini A, Nakata A, Lin J, Reddy RM, Chang AC, Ramnath N, Gotoh N, Beer DG, Chen G. A novel serum 4-microRNA signature for lung cancer detection. Scientific reports. 2015;5(1):12464. [DOI:10.1038/srep12464]
23. Warnat-Herresthal S, Perrakis K, Taschler B, Becker M, Baßler K, Beyer M, Günther P, Schulte-Schrepping J, Seep L, Klee K. Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. Iscience. 2020;23(1). [DOI:10.1016/j.isci.2019.100780]
24. Azzouz FB, Michel B, Lasla H, Gouraud W, François A-F, Girka F, Lecointre T, Guérin-Charbonnel C, Juin PP, Campone M. Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches. Computers in Biology and Medicine. 2021;129:104171. [DOI:10.1016/j.compbiomed.2020.104171]
25. Alkhateeb A, Rezaeian I, Singireddy S, Cavallo-Medved D, Porter LA, Rueda L. Transcriptomics signature from next-generation sequencing data reveals new transcriptomic biomarkers related to prostate cancer. Cancer informatics. 2019;18:1176935119835522. [DOI:10.1177/1176935119835522]
26. Masuda T, Mori A, Ito S, Ohtsuki S. Quantitative and targeted proteomics-based identification and validation of drug efficacy biomarkers. Drug Metabolism and Pharmacokinetics. 2021;36:100361. [DOI:10.1016/j.dmpk.2020.09.006]
27. Islam Khan MZ, Tam SY, Law HKW. Advances in high throughput proteomics profiling in establishing potential biomarkers for gastrointestinal cancer. Cells. 2022;11(6):973. [DOI:10.3390/cells11060973]
28. Li N, Long Y, Fan X, Liu H, Li C, Chen L, Wang Z. Proteomic analysis of differentially expressed proteins in hepatitis B virus-related hepatocellular carcinoma tissues. Journal of Experimental & Clinical Cancer Research. 2009;28:1-10. [DOI:10.1186/1756-9966-28-122]
29. Murata M, Matsuzaki K, Yoshida K, Sekimoto G, Tahashi Y, Mori S, Uemura Y, Sakaida N, Fujisawa J, Seki T. Hepatitis B virus X protein shifts human hepatic transforming growth factor (TGF)‐β signaling from tumor suppression to oncogenesis in early chronic hepatitis B. Hepatology. 2009;49(4):1203-17. [DOI:10.1002/hep.22765]
30. Wu Z, Doondeea JB, Gholami AM, Janning MC, Lemeer S, Kramer K, Eccles SA, Gollin SM, Grenman R, Walch A. Quantitative chemical proteomics reveals new potential drug targets in head and neck cancer. Molecular & Cellular Proteomics. 2011;10(12). [DOI:10.1074/mcp.M111.011635]
31. Nambu M, Masuda T, Ito S, Kato K, Kojima T, Daiko H, Ito Y, Honda K, Ohtsuki S. Leucine-rich alpha-2-glycoprotein 1 in serum is a possible biomarker to predict response to preoperative chemoradiotherapy for esophageal cancer. Biological and Pharmaceutical Bulletin. 2019;42(10):1766-71. [DOI:10.1248/bpb.b19-00395]
32. Huang X, Zeng Y, Xing X, Zeng J, Gao Y, Cai Z, Xu B, Liu X, Huang A, Liu J. Quantitative proteomics analysis of early recurrence/metastasis of huge hepatocellular carcinoma following radical resection. Proteome science. 2014;12:1-14. https://doi.org/10.1186/1477-5956-12-1 [DOI:10.1186/1477-5956-12-22]
33. Gerdes H, Casado P, Dokal A, Hijazi M, Akhtar N, Osuntola R, Rajeeve V, Fitzgibbon J, Travers J, Britton D. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs. Nature communications. 2021;12(1):1850. [DOI:10.1038/s41467-021-22170-8]
34. Farinella F, Merone M, Bacco L, Capirchio A, Ciccozzi M, Caligiore D. Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers. Scientific Reports. 2022;12(1):3041. [DOI:10.1038/s41598-022-06788-2]
35. Tong L, Wu H, Wang MD. Integrating multi-omics data by learning modality invariant representations for improved prediction of overall survival of cancer. Methods. 2021;189:74-85. [DOI:10.1016/j.ymeth.2020.07.008]
36. Lewis JE, Kemp ML. Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance. Nature Communications. 2021;12(1):2700. [DOI:10.1038/s41467-021-22989-1]
37. Nicora G, Vitali F, Dagliati A, Geifman N, Bellazzi R. Integrated multi-omics analyses in oncology: a review of machine learning methods and tools. Frontiers in oncology. 2020;10:1030. [DOI:10.3389/fonc.2020.01030]
38. Wang Y, Yang Y, Chen S, Wang J. DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration. Briefings in Bioinformatics. 2021;22(5):bbab048. [DOI:10.1093/bib/bbab048]
39. Ma T, Zhang A. Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE). BMC genomics. 2019;20:1-11. [DOI:10.1186/s12864-019-6285-x]
40. Wang Z, Zhao Y, Shen X, Zhao Y, Zhang Z, Yin H, Zhao X, Liu H, Shi Q. Single-cell genomics-based molecular algorithm for early cancer detection. Analytical Chemistry. 2022;94(5):2607-14. [DOI:10.1021/acs.analchem.1c04968]
41. Poirion OB, Jing Z, Chaudhary K, Huang S, Garmire LX. DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data. Genome medicine. 2021;13(1):1-15. [DOI:10.1186/s13073-021-00930-x]
42. Araújo ALD, Arboleda LPA, Palmier NR, Fonseca JM, de Pauli Paglioni M, Gomes-Silva W, Ribeiro ACP, Brandao TB, Simonato LE, Speight PM. The performance of digital microscopy for primary diagnosis in human pathology: a systematic review. Virchows Archiv. 2019;474:269-87. [DOI:10.1007/s00428-018-02519-z]
43. Hou Q, Bing Z-T, Hu C, Li M-Y, Yang K-H, Mo Z, Xie X-W, Liao J-L, Lu Y, Horie S. RankProd combined with genetic algorithm optimized artificial neural network establishes a diagnostic and prognostic prediction model that revealed C1QTNF3 as a biomarker for prostate cancer. EBioMedicine. 2018;32:234-44. [DOI:10.1016/j.ebiom.2018.05.010]
44. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology-new tools for diagnosis and precision oncology. Nature reviews Clinical oncology. 2019;16(11):703-15. [DOI:10.1038/s41571-019-0252-y]
45. Mukhopadhyay S, Feldman MD, Abels E, Ashfaq R, Beltaifa S, Cacciabeve NG, Cathro HP, Cheng L, Cooper K, Dickey GE. Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: a multicenter blinded randomized noninferiority study of 1992 cases (pivotal study). The American journal of surgical pathology. 2018;42(1):39. [DOI:10.1097/PAS.0000000000000948]
46. Tosta TAA, de Faria PR, Neves LA, do Nascimento MZ. Color normalization of faded H&E-stained histological images using spectral matching. Computers in biology and medicine. 2019;111:103344. [DOI:10.1016/j.compbiomed.2019.103344]
47. Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead DR, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE transactions on medical imaging. 2016;35(5):1196-206. [DOI:10.1109/TMI.2016.2525803]
48. Martins J, Magalhães C, Rocha M, Osório NS. Machine learning-enhanced T cell neoepitope discovery for immunotherapy design. Cancer informatics. 2019;18:1176935119852081. [DOI:10.1177/1176935119852081]
49. Araújo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Polónia A, Campilho A. Classification of breast cancer histology images using convolutional neural networks. PloS one. 2017;12(6):e0177544. [DOI:10.1371/journal.pone.0177544]
50. Wang L, Ding L, Liu Z, Sun L, Chen L, Jia R, Dai X, Cao J, Ye J. Automated identification of malignancy in whole-slide pathological images: identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning. British Journal of Ophthalmology. 2020;104(3):318-23. [DOI:10.1136/bjophthalmol-2018-313706]
51. Iizuka O, Kanavati F, Kato K, Rambeau M, Arihiro K, Tsuneki M. Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Scientific reports. 2020;10(1):1504. [DOI:10.1038/s41598-020-58467-9]
52. Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity. 2013;39(1):11-26. [DOI:10.1016/j.immuni.2013.07.008]
53. Koelzer VH, Sirinukunwattana K, Rittscher J, Mertz KD. Precision immunoprofiling by image analysis and artificial intelligence. Virchows Archiv. 2019;474:511-22. [DOI:10.1007/s00428-018-2485-z]
54. Qaiser T, Mukherjee A, Reddy Pb C, Munugoti SD, Tallam V, Pitkäaho T, Lehtimäki T, Naughton T, Berseth M, Pedraza A. Her 2 challenge contest: a detailed assessment of automated her 2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology. 2018;72(2):227-38. [DOI:10.1111/his.13333]
55. Win KP, Kitjaidure Y, Hamamoto K, Myo Aung T. Computer-assisted screening for cervical cancer using digital image processing of pap smear images. Applied Sciences. 2020;10(5):1800. [DOI:10.3390/app10051800]
56. Leon F, Gelvez M, Jaimes Z, Gelvez T, Arguello H, editors. Supervised classification of histopathological images using convolutional neuronal networks for gastric cancer detection. 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA); 2019: IEEE. [DOI:10.1109/STSIVA.2019.8730284]
57. Dov D, Kovalsky SZ, Cohen J, Range DE, Henao R, Carin L, editors. Thyroid cancer malignancy prediction from whole slide cytopathology images. Machine Learning for Healthcare Conference; 2019: PMLR.
58. Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nature medicine. 2019;25(7):1054-6. [DOI:10.1038/s41591-019-0462-y]
59. Abdous S, Abdollahzadeh R, Rohban MH. KS-GNNExplainer: Global Model Interpretation Through Instance Explanations On Histopathology images. arXiv preprint arXiv:230408240. 2023.
60. Tabibu S, Vinod P, Jawahar C. Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images using deep learning. Scientific reports. 2019;9(1):10509. [DOI:10.1038/s41598-019-46718-3]
61. Wu M, Yan C, Liu H, Liu Q, Yin Y. Automatic classification of cervical cancer from cytological images by using convolutional neural network. Bioscience reports. 2018;38(6):BSR20181769. [DOI:10.1042/BSR20181769]
62. Jiang Y, Chen L, Zhang H, Xiao X. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS one. 2019;14(3):e0214587. [DOI:10.1371/journal.pone.0214587]
63. Aprupe L, Litjens G, Brinker TJ, van der Laak J, Grabe N. Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks. PeerJ. 2019;7:e6335. [DOI:10.7717/peerj.6335]
64. Swiderska-Chadaj Z, Pinckaers H, van Rijthoven M, Balkenhol M, Melnikova M, Geessink O, Manson Q, Sherman M, Polonia A, Parry J. Learning to detect lymphocytes in immunohistochemistry with deep learning. Medical image analysis. 2019;58:101547. [DOI:10.1016/j.media.2019.101547]
65. Paul PR, Bhowmik MK, Bhattacharjee D, editors. Automated cervical cancer detection using Pap smear images. Proceedings of Fourth International Conference on Soft Computing for Problem Solving: SocProS 2014, Volume 1; 2015: Springer. [DOI:10.1007/978-81-322-2217-0_23]
66. Malathi M, Sinthia P. Brain tumour segmentation using convolutional neural network with tensor flow. Asian Pacific journal of cancer prevention: APJCP. 2019;20(7):2095. [DOI:10.31557/APJCP.2019.20.7.2095]
67. Kalaiselvi T, Nagaraja P. A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique. International journal of imaging systems and technology. 2015;1(25):77-85. [DOI:10.1002/ima.22123]
68. Saha A, Harowicz MR, Wang W, Mazurowski MA. A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models. Journal of cancer research and clinical oncology. 2018;144:799-807. [DOI:10.1007/s00432-018-2595-7]
69. Brossard C, Lemasson B, Attyé A, De Busschère J-A, Payen J-F, Barbier EL, Grèze J, Bouzat P. Contribution of CT-scan analysis by artificial intelligence to the clinical care of TBI patients. Frontiers in Neurology. 2021;12:666875. [DOI:10.3389/fneur.2021.666875]
70. Kaheel H, Hussein A, Chehab A. AI-based image processing for COVID-19 detection in chest CT scan images. Frontiers in Communications and Networks. 2021;2:645040. [DOI:10.3389/frcmn.2021.645040]
71. Godkhindi AM, Gowda RM, editors. Automated detection of polyps in CT colonography images using deep learning algorithms in colon cancer diagnosis. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS); 2017: IEEE. [DOI:10.1109/ICECDS.2017.8389744]
72. Chillakuru YR, Kranen K, Doppalapudi V, Xiong Z, Fu L, Heydari A, Sheth A, Seo Y, Vu T, Sohn JH. High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels. BMC medical imaging. 2021;21:1-13. [DOI:10.1186/s12880-021-00594-4]
73. Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific reports. 2018;8(1):15497. [DOI:10.1038/s41598-018-33860-7]
74. Shen Y-T, Chen L, Yue W-W, Xu H-X. Artificial intelligence in ultrasound. European Journal of Radiology. 2021;139:109717. [DOI:10.1016/j.ejrad.2021.109717]
75. Poudel P, Illanes A, Sheet D, Friebe M. Evaluation of commonly used algorithms for thyroid ultrasound images segmentation and improvement using machine learning approaches. Journal of healthcare engineering. 2018;2018. [DOI:10.1155/2018/8087624]
76. Dong H, Yang G, Liu F, Mo Y, Guo Y, editors. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11-13, 2017, Proceedings 21; 2017: Springer.
77. Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM. Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal. 2018;3(1):68-71. [DOI:10.1016/j.fcij.2017.12.001]
78. Alam MS, Rahman MM, Hossain MA, Islam MK, Ahmed KM, Ahmed KT, Singh BC, Miah MS. Automatic human brain tumor detection in MRI image using template-based K means and improved fuzzy C means clustering algorithm. Big Data and Cognitive Computing. 2019;3(2):27. [DOI:10.3390/bdcc3020027]
79. Le T-N, Huynh HT. Liver tumor segmentation from MR images using 3D fast marching algorithm and single hidden layer feedforward neural network. BioMed research international. 2016;2016. [DOI:10.1155/2016/3219068]
80. Liu S, Zheng H, Feng Y, Li W, editors. Prostate cancer diagnosis using deep learning with 3D multiparametric MRI. Medical imaging 2017: computer-aided diagnosis; 2017: SPIE. [DOI:10.1117/12.2277121]
81. Yoo S, Gujrathi I, Haider MA, Khalvati F. Prostate cancer detection using deep convolutional neural networks. Scientific reports. 2019;9(1):19518. [DOI:10.1038/s41598-019-55972-4]
82. Skalski A, Jakubowski J, Drewniak T, editors. Kidney tumor segmentation and detection on computed tomography data. 2016 IEEE International Conference on Imaging Systems and Techniques (IST); 2016: IEEE. [DOI:10.1109/IST.2016.7738230]
83. Han S, Hwang SI, Lee HJ. The classification of renal cancer in 3-phase CT images using a deep learning method. Journal of digital imaging. 2019;32:638-43. [DOI:10.1007/s10278-019-00230-2]
84. Senthil Kumar K, Venkatalakshmi K, Karthikeyan K. Lung cancer detection using image segmentation by means of various evolutionary algorithms. Computational and mathematical methods in medicine. 2019;2019. [DOI:10.1155/2019/4909846]
85. Ausawalaithong W, Thirach A, Marukatat S, Wilaiprasitporn T, editors. Automatic lung cancer prediction from chest X-ray images using the deep learning approach. 2018 11th biomedical engineering international conference (BMEiCON); 2018: IEEE. [DOI:10.1109/BMEiCON.2018.8609997]
86. Chiu H-Y, Peng RH-T, Lin Y-C, Wang T-W, Yang Y-X, Chen Y-Y, Wu M-H, Shiao T-H, Chao H-S, Chen Y-M. Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images. Biomedicines. 2022;10(11):2839. [DOI:10.3390/biomedicines10112839]
87. Guan Q, Wang Y, Du J, Qin Y, Lu H, Xiang J, Wang F. Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study. Annals of translational medicine. 2019;7(7). [DOI:10.21037/atm.2019.04.34]
88. Nugroho HA, Frannita EL, Ardiyanto I, Choridah L. Computer aided diagnosis for thyroid cancer system based on internal and external characteristics. Journal of King Saud University-Computer and Information Sciences. 2021;33(3):329-39. [DOI:10.1016/j.jksuci.2019.01.007]
89. Ragab DA, Sharkas M, Marshall S, Ren J. Breast cancer detection using deep convolutional neural networks and support vector machines. PeerJ. 2019;7:e6201. [DOI:10.7717/peerj.6201]
90. Bidard F-C, Mathiot C, Delaloge S, Brain E, Giachetti S, De Cremoux P, Marty M, Pierga J-Y. Single circulating tumor cell detection and overall survival in nonmetastatic breast cancer. Annals of Oncology. 2010;21(4):729-33. [DOI:10.1093/annonc/mdp391]
91. Kaur P, Singh G, Kaur P. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Informatics in Medicine Unlocked. 2019;16:100151. https://doi.org/10.1016/j.imu.2019.01.001 [DOI:10.1016/j.imu.2019.100239]
92. Patil RS, Biradar N. Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network. Evolutionary intelligence. 2021;14:1459-74. [DOI:10.1007/s12065-020-00403-x]
93. Trang NTH, Long KQ, An PL, Dang TN. Development of an Artificial Intelligence-Based Breast Cancer Detection Model by Combining Mammograms and Medical Health Records. Diagnostics. 2023;13(3):346. [DOI:10.3390/diagnostics13030346]
94. Borrelli P, Ly J, Kaboteh R, Ulén J, Enqvist O, Trägårdh E, Edenbrandt L. AI-based detection of lung lesions in [18F] FDG PET-CT from lung cancer patients. EJNMMI physics. 2021;8(1):1-11. [DOI:10.1186/s40658-021-00376-5]
95. Huang B, Sollee J, Luo Y-H, Reddy A, Zhong Z, Wu J, Mammarappallil J, Healey T, Cheng G, Azzoli C. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT. EBioMedicine. 2022;82. [DOI:10.1016/j.ebiom.2022.104127]
96. Alongi P, Stefano A, Comelli A, Spataro A, Formica G, Laudicella R, Lanzafame H, Panasiti F, Longo C, Midiri F. Artificial intelligence applications on restaging [18F] FDG PET/CT in metastatic colorectal cancer: A preliminary report of morpho-functional radiomics classification for prediction of disease outcome. Applied Sciences. 2022;12(6):2941. [DOI:10.3390/app12062941]
97. Yamada M, Saito Y, Imaoka H, Saiko M, Yamada S, Kondo H, Takamaru H, Sakamoto T, Sese J, Kuchiba A. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Scientific reports. 2019;9(1):14465. [DOI:10.1038/s41598-019-50567-5]
98. Zhang R, Zheng Y, Mak TWC, Yu R, Wong SH, Lau JY, Poon CC. Automatic detection and classification of colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE journal of biomedical and health informatics. 2016;21(1):41-7. [DOI:10.1109/JBHI.2016.2635662]
99. Shin Y, Qadir HA, Aabakken L, Bergsland J, Balasingham I. Automatic colon polyp detection using region based deep CNN and post learning approaches. IEEE Access. 2018;6:40950-62. [DOI:10.1109/ACCESS.2018.2856402]
100. Khryashchev V, Stepanova O, Lebedev A, Kashin S, Kuvaev R, editors. Deep learning for gastric pathology detection in endoscopic images. Proceedings of the 3rd International Conference on Graphics and Signal Processing; 2019. [DOI:10.1145/3338472.3338492]
101. Shibata T, Teramoto A, Yamada H, Ohmiya N, Saito K, Fujita H. Automated detection and segmentation of early gastric cancer from endoscopic images using mask R-CNN. Applied Sciences. 2020;10(11):3842. [DOI:10.3390/app10113842]
102. Sakai Y, Takemoto S, Hori K, Nishimura M, Ikematsu H, Yano T, Yokota H, editors. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2018: IEEE. [DOI:10.1109/EMBC.2018.8513274]
103. Mane S, Shinde S, editors. A method for melanoma skin cancer detection using dermoscopy images. 2018 Fourth international conference on computing communication control and automation (ICCUBEA); 2018: IEEE. [DOI:10.1109/ICCUBEA.2018.8697804]
104. Udrea A, Mitra GD, editors. Generative adversarial neural networks for pigmented and non-pigmented skin lesions detection in clinical images. 2017 21st international conference on control systems and computer science (CSCS); 2017: IEEE. [DOI:10.1109/CSCS.2017.56]
105. Hasan M, Barman SD, Islam S, Reza AW, editors. Skin cancer detection using convolutional neural network. Proceedings of the 2019 5th international conference on computing and artificial intelligence; 2019. [DOI:10.1145/3330482.3330525]
106. Marchetti MA, Cowen EA, Kurtansky NR, Weber J, Dauscher M, DeFazio J, Deng L, Dusza SW, Haliasos H, Halpern AC. Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study). NPJ Digital Medicine. 2023;6(1):127. [DOI:10.1038/s41746-023-00872-1]
107. Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, Malik K, Raza S, Abbas A, Pezzani R. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer cell international. 2021;21(1):1-11. [DOI:10.1186/s12935-021-01981-1]
108. Bibault J-E, Chang DT, Xing L. Development and validation of a model to predict survival in colorectal cancer using a gradient-boosted machine. Gut. 2021;70(5):884-9. [DOI:10.1136/gutjnl-2020-321799]
109. Liu B, He H, Luo H, Zhang T, Jiang J. Artificial intelligence and big data facilitated targeted drug discovery. Stroke and vascular neurology. 2019;4(4). [DOI:10.1136/svn-2019-000290]
110. Liu A-A, Zhai Y, Xu N, Nie W, Li W, Zhang Y. Region-aware image captioning via interaction learning. IEEE Transactions on Circuits and Systems for Video Technology. 2021;32(6):3685-96. [DOI:10.1109/TCSVT.2021.3107035]
111. Yang S, Li Q, Li W, Li X, Liu A-A. Dual-level representation enhancement on characteristic and context for image-text retrieval. IEEE Transactions on Circuits and Systems for Video Technology. 2022;32(11):8037-50. [DOI:10.1109/TCSVT.2022.3182426]
112. Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlentherapie und Onkologie. 2020;196:879-87. [DOI:10.1007/s00066-020-01625-9]
113. Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlentherapie und Onkologie. 2020;196:888-99. [DOI:10.1007/s00066-020-01615-x]
114. Kocher M, Ruge MI, Galldiks N, Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlentherapie und Onkologie. 2020;196:856-67. [DOI:10.1007/s00066-020-01626-8]
115. Danaee P, Ghaeini R, Hendrix DA, editors. A deep learning approach for cancer detection and relevant gene identification. Pacific symposium on biocomputing 2017; 2017: World Scientific. [DOI:10.1142/9789813207813_0022]
116. Bębas E, Borowska M, Derlatka M, Oczeretko E, Hładuński M, Szumowski P, Mojsak M. Machine-learning-based classification of the histological subtype of non-small-cell lung cancer using MRI texture analysis. Biomedical Signal Processing and Control. 2021;66:102446. [DOI:10.1016/j.bspc.2021.102446]
117. Jhajharia S, Varshney HK, Verma S, Kumar R, editors. A neural network based breast cancer prognosis model with PCA processed features. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI); 2016: IEEE. [DOI:10.1109/ICACCI.2016.7732327]
118. Ching T, Zhu X, Garmire LX. Cox-nnet: an artificial neural network method for prognosis prediction of high-throughput omics data. PLoS computational biology. 2018;14(4):e1006076. [DOI:10.1371/journal.pcbi.1006076]
119. Bomane A, Gonçalves A, Ballester PJ. Paclitaxel response can be predicted with interpretable multi-variate classifiers exploiting DNA-methylation and miRNA data. Frontiers in genetics. 2019;10:1041. [DOI:10.3389/fgene.2019.01041]
120. Kuo R-J, Huang M-H, Cheng W-C, Lin C-C, Wu Y-H. Application of a two-stage fuzzy neural network to a prostate cancer prognosis system. Artificial intelligence in medicine. 2015;63(2):119-33. [DOI:10.1016/j.artmed.2014.12.008]
121. Zhang S, Xu Y, Hui X, Yang F, Hu Y, Shao J, Liang H, Wang Y. Improvement in prediction of prostate cancer prognosis with somatic mutational signatures. Journal of Cancer. 2017;8(16):3261. [DOI:10.7150/jca.21261]
122. Lind AP, Anderson PC. Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties. PloS one. 2019;14(7):e0219774. [DOI:10.1371/journal.pone.0219774]
123. Paik ES, Lee J-W, Park J-Y, Kim J-H, Kim M, Kim T-J, Choi CH, Kim B-G, Bae D-S, Seo SW. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. Journal of gynecologic oncology. 2019;30(4). [DOI:10.3802/jgo.2019.30.e65]
124. McDonald JF. 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. Gynecologic oncology. 2018;149(2):230-1. [DOI:10.1016/j.ygyno.2018.03.053]
125. Li Q, Qi L, Feng Q-X, Liu C, Sun S-W, Zhang J, Yang G, Ge Y-Q, Zhang Y-D, Liu X-S. Machine learning-based computational models derived from large-scale radiographic-radiomic images can help predict adverse histopathological status of gastric cancer. Clinical and Translational Gastroenterology. 2019;10(10). [DOI:10.14309/ctg.0000000000000079]
126. Taninaga J, Nishiyama Y, Fujibayashi K, Gunji T, Sasabe N, Iijima K, Naito T. Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study. Scientific reports. 2019;9(1):12384. [DOI:10.1038/s41598-019-48769-y]
127. Liu C, Qi L, Feng Q-X, Sun S-W, Zhang Y-D, Liu X-S. 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. Abdominal Radiology. 2019;44:3019-29. [DOI:10.1007/s00261-019-02098-w]
128. Stanzione A, Cuocolo R, Del Grosso R, Nardiello A, Romeo V, Travaglino A, Raffone A, Bifulco G, Zullo F, Insabato L. Deep myometrial infiltration of endometrial cancer on MRI: A radiomics-powered machine learning pilot study. Academic radiology. 2021;28(5):737-44. [DOI:10.1016/j.acra.2020.02.028]
129. Günakan E, Atan S, Haberal AN, Küçükyıldız İA, Gökçe E, Ayhan A. A novel prediction method for lymph node involvement in endometrial cancer: machine learning. International Journal of Gynecologic Cancer. 2019;29(2). [DOI:10.1136/ijgc-2018-000033]
130. Beck JT, Rammage M, Jackson GP, Preininger AM, Dankwa-Mullan I, Roebuck MC, Torres A, Holtzen H, Coverdill SE, Williamson MP. Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center. JCO clinical cancer informatics. 2020;4:50-9. [DOI:10.1200/CCI.19.00079]
131. Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology. 2019;16(7):391-403. [DOI:10.1038/s41585-019-0193-3]
132. Nascimento AC, Prudêncio RB, Costa IG. A drug-target network-based supervised machine learning repurposing method allowing the use of multiple heterogeneous information sources. Computational Methods for Drug Repurposing. 2019:281-9. [DOI:10.1007/978-1-4939-8955-3_17]
133. Sharma A, Rani R. Ensembled machine learning framework for drug sensitivity prediction. IET Systems Biology. 2020;14(1):39-46. [DOI:10.1049/iet-syb.2018.5094]
134. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M. Applications of machine learning in drug discovery and development. Nature reviews Drug discovery. 2019;18(6):463-77. [DOI:10.1038/s41573-019-0024-5]
135. Xia X, Gong J, Hao W, Yang T, Lin Y, Wang S, Peng W. Comparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-I lung adenocarcinomas in CT scan. Frontiers in oncology. 2020;10:418. [DOI:10.3389/fonc.2020.00418]
136. Baskin II. The power of deep learning to ligand-based novel drug discovery. Expert opinion on drug discovery. 2020;15(7):755-64. [DOI:10.1080/17460441.2020.1745183]
137. Kadurin A, Aliper A, Kazennov A, Mamoshina P, Vanhaelen Q, Khrabrov K, Zhavoronkov A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017;8(7):10883. [DOI:10.18632/oncotarget.14073]
138. Juwara L, Arora N, Gornitsky M, Saha-Chaudhuri P, Velly AM. Identifying predictive factors for neuropathic pain after breast cancer surgery using machine learning. International journal of medical informatics. 2020;141:104170. [DOI:10.1016/j.ijmedinf.2020.104170]
139. Kenngott H, Wagner M, Nickel F, Wekerle A, Preukschas A, Apitz M, Schulte T, Rempel R, Mietkowski P, Wagner F. Computer-assisted abdominal surgery: new technologies. Langenbeck's archives of surgery. 2015;400:273-81. [DOI:10.1007/s00423-015-1289-8]
140. Kingham TP, Scherer MA, Neese BW, Clements LW, Stefansic JD, Jarnagin WR. Image-guided liver surgery: intraoperative projection of computed tomography images utilizing tracked ultrasound. Hpb. 2012;14(9):594-603. [DOI:10.1111/j.1477-2574.2012.00487.x]
141. Okada T, Kawada K, Sumii A, Itatani Y, Hida K, Hasegawa S, Sakai Y. Stereotactic Navigation for Rectal Surgery: Comparison of 3-Dimensional C-Arm− Based Registration to Paired-Point Registration. Diseases of the Colon & Rectum. 2020;63(5):693-700. [DOI:10.1097/DCR.0000000000001608]
142. Mahmud S, Abbas TO, Mushtak A, Prithula J, Chowdhury ME. Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata. Cancers. 2023;15(12):3189. [DOI:10.3390/cancers15123189]
143. Satava RM. Robotic surgery: from past to future-a personal journey. Surgical Clinics. 2003;83(6):1491-500. [DOI:10.1016/S0039-6109(03)00168-3]
144. Cao L, Yang Z, Qi L, Chen M. Robot-assisted and laparoscopic vs open radical prostatectomy in clinically localized prostate cancer: perioperative, functional, and oncological outcomes: A Systematic review and meta-analysis. Medicine. 2019;98(22). [DOI:10.1097/MD.0000000000015770]
145. Ngiam KY, Khor W. Big data and machine learning algorithms for health-care delivery. The Lancet Oncology. 2019;20(5):e262-e73. [DOI:10.1016/S1470-2045(19)30149-4]
146. Chen G, Tsoi A, Xu H, Zheng WJ. Predict effective drug combination by deep belief network and ontology fingerprints. Journal of biomedical informatics. 2018;85:149-54. [DOI:10.1016/j.jbi.2018.07.024]
147. Levine MN, Alexander G, Sathiyapalan A, Agrawal A, Pond G. Learning health system for breast cancer: pilot project experience. JCO clinical cancer informatics. 2019;3:1-11. [DOI:10.1200/CCI.19.00032]
148. Smaïl-Tabbone M, Rance B. Contributions from the 2019 Literature on Bioinformatics and Translational Informatics. Yearbook of Medical Informatics. 2020;29(01):188-92. [DOI:10.1055/s-0040-1702002]
149. Pantuck AJ, Lee DK, Kee T, Wang P, Lakhotia S, Silverman MH, Mathis C, Drakaki A, Belldegrun AS, Ho CM. Modulating BET bromodomain inhibitor ZEN‐3694 and enzalutamide combination dosing in a metastatic prostate cancer patient using CURATE. AI, an artificial intelligence platform. Advanced Therapeutics. 2018;1(6):1800104. [DOI:10.1002/adtp.201800104]
150. Gulhan DC, Lee JJ-K, Melloni GE, Cortes-Ciriano I, Park PJ. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nature genetics. 2019;51(5):912-9. [DOI:10.1038/s41588-019-0390-2]
151. Tang X, Huang Y, Lei J, Luo H, Zhu X. The single-cell sequencing: new developments and medical applications. Cell & bioscience. 2019;9:1-9. [DOI:10.1186/s13578-019-0314-y]
152. Peng H, Dong D, Fang M-J, Li L, Tang L-L, Chen L, Li W-F, Mao Y-P, Fan W, Liu L-Z. Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clinical Cancer Research. 2019;25(14):4271-9. [DOI:10.1158/1078-0432.CCR-18-3065]
153. Staffurth J. A review of the clinical evidence for intensity-modulated radiotherapy. Clinical oncology. 2010;22(8):643-57. [DOI:10.1016/j.clon.2010.06.013]
154. Otto K. Volumetric modulated arc therapy: IMRT in a single gantry arc. Medical physics. 2008;35(1):310-7. [DOI:10.1118/1.2818738]
155. Crooks S, Wu X, Takita C, Watzich M, Xing L. Aperture modulated arc therapy. Physics in medicine & biology. 2003;48(10):1333. [DOI:10.1088/0031-9155/48/10/307]
156. Palma DA, Olson R, Harrow S, Gaede S, Louie AV, Haasbeek C, Mulroy L, Lock M, Rodrigues GB, Yaremko BP. Stereotactic ablative radiotherapy versus standard of care palliative treatment in patients with oligometastatic cancers (SABR-COMET): a randomised, phase 2, open-label trial. The Lancet. 2019;393(10185):2051-8. [DOI:10.1016/S0140-6736(18)32487-5]
157. Timmerman RD, Kavanagh BD, Cho LC, Papiez L, Xing L. Stereotactic body radiation therapy in multiple organ sites. Journal of Clinical Oncology. 2007;25(8):947-52. [DOI:10.1200/JCO.2006.09.7469]
158. Langen KM, Jones DT. Organ motion and its management. International Journal of Radiation Oncology* Biology* Physics. 2001;50(1):265-78. [DOI:10.1016/S0360-3016(01)01453-5]
159. van Lin ENT, van der Vight LP, Witjes JA, Huisman HJ, Leer JW, Visser AG. The effect of an endorectal balloon and off-line correction on the interfraction systematic and random prostate position variations: a comparative study. International Journal of Radiation Oncology* Biology* Physics. 2005;61(1):278-88. [DOI:10.1016/j.ijrobp.2004.09.042]
160. Shen L, Zhao W, Xing L. Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning. Nature biomedical engineering. 2019;3(11):880-8. [DOI:10.1038/s41551-019-0466-4]
161. Zhao W, Shen L, Islam MT, Qin W, Zhang Z, Liang X, Zhang G, Xu S, Li X. Artificial intelligence in image-guided radiotherapy: a review of treatment target localization. Quantitative Imaging in Medicine and Surgery. 2021;11(12):4881. [DOI:10.21037/qims-21-199]
162. Fiorino C, Guckenberger M, Schwarz M, van der Heide UA, Heijmen B. Technology‐driven research for radiotherapy innovation. Molecular oncology. 2020;14(7):1500-13. [DOI:10.1002/1878-0261.12659]
163. Lou B, Doken S, Zhuang T, Wingerter D, Gidwani M, Mistry N, Ladic L, Kamen A, Abazeed ME. An image-based deep learning framework for individualising radiotherapy dose: a retrospective analysis of outcome prediction. The Lancet Digital Health. 2019;1(3):e136-e47. [DOI:10.1016/S2589-7500(19)30058-5]
164. Lin H, Xiao H, Dong L, Teo KB-K, Zou W, Cai J, Li T. Deep learning for automatic target volume segmentation in radiation therapy: a review. Quantitative Imaging in Medicine and Surgery. 2021;11(12):4847. [DOI:10.21037/qims-21-168]
165. Cha KH, Hadjiiski L, Chan H-P, Weizer AZ, Alva A, Cohan RH, Caoili EM, Paramagul C, Samala RK. Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Scientific reports. 2017;7(1):8738. [DOI:10.1038/s41598-017-09315-w]
166. Babier A, Boutilier JJ, McNiven AL, Chan TC. Knowledge‐based automated planning for oropharyngeal cancer. Medical physics. 2018;45(7):2875-83. [DOI:10.1002/mp.12930]
167. Jabbari P, Rezaei N. Artificial intelligence and immunotherapy. Expert Review of Clinical Immunology. 2019;15(7):689-91. [DOI:10.1080/1744666X.2019.1623670]
168. Trebeschi S, Drago S, Birkbak N, Kurilova I, Cǎlin A, Pizzi AD, Lalezari F, Lambregts D, Rohaan M, Parmar C. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Annals of Oncology. 2019;30(6):998-1004. [DOI:10.1093/annonc/mdz108]
169. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. The Lancet Oncology. 2018;19(9):1180-91. [DOI:10.1016/S1470-2045(18)30413-3]
170. Bulik-Sullivan B, Busby J, Palmer CD, Davis MJ, Murphy T, Clark A, Busby M, Duke F, Yang A, Young L. Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification. Nature biotechnology. 2019;37(1):55-63. [DOI:10.1038/nbt.4313]
171. Boehm KM, Bhinder B, Raja VJ, Dephoure N, Elemento O. Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome. BMC bioinformatics. 2019;20:1-11. [DOI:10.1186/s12859-018-2561-z]
172. Ge P, Wang W, Li L, Zhang G, Gao Z, Tang Z, Dang X, Wu Y. Profiles of immune cell infiltration and immune-related genes in the tumor microenvironment of colorectal cancer. Biomedicine & Pharmacotherapy. 2019;118:109228. [DOI:10.1016/j.biopha.2019.109228]
173. Schmidt J, Guillaume P, Dojcinovic D, Karbach J, Coukos G, Luescher I. In silico and cell-based analyses reveal strong divergence between prediction and observation of T-cell-recognized tumor antigen T-cell epitopes. Journal of Biological Chemistry. 2017;292(28):11840-9. [DOI:10.1074/jbc.M117.789511]
174. Tosolini M, Pont F, Poupot M, Vergez F, Nicolau-Travers M-L, Vermijlen D, Sarry J-E, Dieli F, Fournié J-J. Assessment of tumor-infiltrating TCRV γ 9V δ 2 γδ lymphocyte abundance by deconvolution of human cancers microarrays. Oncoimmunology. 2017;6(3):e1284723. [DOI:10.1080/2162402X.2017.1284723]
175. Thurtle DR, Greenberg DC, Lee LS, Huang HH, Pharoah PD, Gnanapragasam VJ. Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model. PLoS medicine. 2019;16(3):e1002758. [DOI:10.1371/journal.pmed.1002758]
176. Feng H, Gu Z-Y, Li Q, Liu Q-H, Yang X-Y, Zhang J-J. Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis. Journal of ovarian research. 2019;12:1-9. [DOI:10.1186/s13048-019-0508-2]
177. Qian D, Liu H, Wang X, Ge J, Luo S, Patz Jr EF, Moorman PG, Su L, Shen S, Christiani DC. Potentially functional genetic variants in the complement‐related immunity gene‐set are associated with non‐small cell lung cancer survival. International journal of cancer. 2019;144(8):1867-76. [DOI:10.1002/ijc.31896]
178. Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: challenges and opportunities. Journal of pathology informatics. 2018;9(1):38. [DOI:10.4103/jpi.jpi_53_18]
179. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC medicine. 2019;17:1-9. [DOI:10.1186/s12916-019-1426-2]
180. Dlamini Z, Francies FZ, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Computational and structural biotechnology journal. 2020;18:2300-11. [DOI:10.1016/j.csbj.2020.08.019]
181. Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The lancet digital health. 2019;1(6):e271-e97. [DOI:10.1016/S2589-7500(19)30123-2]
182. Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World journal of gastroenterology. 2019;25(14):1666. [DOI:10.3748/wjg.v25.i14.1666]
183. Delclaux C. No need for pulmonologists to interpret pulmonary function tests. Eur Respiratory Soc; 2019. [DOI:10.1183/13993003.00829-2019]
184. Kumar Y, Gupta S, Singla R, Hu Y-C. A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Archives of Computational Methods in Engineering. 2021:1-28.
185. Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. British Journal of Cancer. 2022;126(1):4-9. [DOI:10.1038/s41416-021-01633-1]
186. Yang F, Darsey JA, Ghosh A, Li H-Y, Yang MQ, Wang S. Artificial intelligence and cancer drug development. Recent Patents on Anti-Cancer Drug Discovery. 2022;17(1):2-8. [DOI:10.2174/1574892816666210728123758]
187. You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduction and Targeted Therapy. 2022;7(1):156. [DOI:10.1038/s41392-022-00994-0]
188. Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial intelligence and the implementation challenge. Journal of medical Internet research. 2019;21(7):e13659. [DOI:10.2196/13659]
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 All Rights Reserved | Iranian Journal of Blood and Cancer

Designed & Developed by : Yektaweb