Transforming Oncology with Artificial Intelligence. Current Applications and Prospective Advances, Modern Novel Insights

Authors

  • Mehak Ali Department of Biomedical Sciences, National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • Shumail Saeed Department of Medical Imaging, Riphah International University, Islamabad, Pakistan
  • Romana Javeed Institute of Biochemistry, Biotechnology and Bioinformatics, Islamia University, Bahawalpur, Pakistan
  • Faha Yousaf Faculty of Pharmacy, University of Sargodha, Sargodha , Pakistan
  • Shazia Azhar Institute of Medical Technology, Baqai Medical University Karachi, Pakistan
  • Affaf Aween Department of Medicine and Surgery, Mohtarma Benazir Bhutto Shaheed Medical College, Mirpur, Azad Kashmir
  • Malyla Amir Oral surgery and Periodontology, Women Medical and Dental College, Abbottabad, Pakistan
  • Sitara Ejaz Department of Zoology, University of Punjab, Lahore, Pakistan
  • Ifrah Hameed Department of Biosciences, COMSATS University, Islamabad, Pakistan
  • Fatima Ali Department of Molecular Biology, University of Florence, Florence, Italy
  • Mohammed Al-Razi Abed Max Planck MP Neuroscience, International Max Planck Research School IMPRS, Germany
  • Amna Naheed Khan Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad, Pakistan

DOI:

https://doi.org/10.31580/jqpbzv21

Keywords:

Artificial intelligence, Cancer diagnostics, Deep learning, Machine learning, Oncology, Personalized treatment, Predictive analysis, Predictive modeling

Abstract

Objective: The objectives of this work was to provide an overview of the current and potential use of AI in oncology by: The study evaluated the role of AI technologies in diagnostics; individual treatment; and, prognosis concerning patients with cancer.

 

Methodology: A systematic electronic search was conducted in PubMed, Science Direct, and Google Scholar from year 2014 to 2024. Thus, the included studies explored various AI interventions in oncology, including machine learning algorithms, deep learning models, diagnostic tools powered by artificial intelligence, etc. Two key areas were evaluated: analyses of AI in considering diagnostics in comparison with the more commonly used imaging and pathology and in determining treatment outcomes and patients’ survival rates. Data from Asia Pacific, Europe, and the Americas was obtained to get a broad view of the incorporation of AI within oncology.

 

Results: The systematic review highlighted improved performances of AI in early cancer diagnosis, treatment planning, and prognosis. Previous studies revealed that AI models offer better diagnostic performance than traditional diagnostic technologies, mainly in medical imaging and molecular pathology. Moreover, a new perception of AI for targeted therapies revealed a higher survival rate and quality of life in patients. As AI technologies advance, impediments like data privacy and big data required for AI were described but considered as conquerable.

 

Conclusion: Artificial intelligence is gradually becoming an innovative solution in the field of disease treatment, where diagnosis, improving the course of treatment, and, therefore, the prognosis for the patient’s condition, are significantly more accurate. Its incorporation in the clinical setting can reasonably improve the future landscape of cancer treatment, making it work more effectively as well as outcomes more predictable. It is so doing its current and continuing research and the cooperation demonstrated therein.

References

Pulumati A, Pulumati A, Dwarakanath BS, Verma A, Papineni RV. Technological advancements in cancer diagnostics: Improvements and limitations. Cancer Reports. 2023;6(2):e1764.

Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. British Journal of Cancer. 2022;126(1):4-9.

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.

Udegbe FC, Ebulue OR, Ebulue CC, Ekesiobi CS. AI's impact on personalized medicine: Tailoring treatments for improved health outcomes. Engineering Science & Technology Journal. 2024;5(4):1386-94.

Cuocolo R, Caruso M, Perillo T, Ugga L, Petretta M. Machine learning in oncology: a clinical appraisal. Cancer letters. 2020;481:55-62.

Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. Journal of internal medicine. 2020 Jul;288(1):62-81.

Istasy P, Lee WS, Iansavichene A, Upshur R, Gyawali B, Burkell J, Sadikovic B, Lazo-Langner A, Chin-Yee B. The impact of artificial intelligence on health equity in oncology: scoping review. Journal of medical Internet research. 2022;24(11):e39748.

Daimiel Naranjo I, Gibbs P, Reiner JS, Lo Gullo R, Sooknanan C, Thakur SB, Jochelson MS, Sevilimedu V, Morris EA, Baltzer PA, Helbich TH. Radiomics and machine learning with multiparametric breast MRI for improved diagnostic accuracy in breast cancer diagnosis. Diagnostics. 2021;11(6):919.

Patra R. Prediction of lung cancer using machine learning classifier. InComputing Science, Communication and Security: First International Conference, COMS2 2020, Gujarat, India, March 26–27, 2020, Revised Selected Papers 1 2020 (pp. 132-142). Springer Singapore.

Erdem E, Bozkurt F. A comparison of various supervised machine learning techniques for prostate cancer prediction. Avrupa Bilim ve Teknoloji Dergisi. 2021(21):610-20.

Yu C, Helwig EJ. The role of AI technology in prediction, diagnosis and treatment of colorectal cancer. Artificial intelligence review. 2022;55(1):323-43.

Mikdadi D, O’Connell KA, Meacham PJ, Dugan MA, Ojiere MO, Carlson TB, Klenk JA. Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery. Cancer Biomarkers. 2022;33(2):173-84.

Liu X, Hou Y, Wang X, Yu L, Wang X, Jiang L, Yang Z. Machine learning-based development and validation of a scoring system for progression-free survival in liver cancer. Hepatology international. 2020;14:567-76.

Escalé-Besa A, Yélamos O, Vidal-Alaball J, Fuster-Casanovas A, Miró Catalina Q, Börve A, Ander-Egg Aguilar R, Fustà-Novell X, Cubiró X, Rafat ME, López-Sanchez C. Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care. Scientific Reports. 2023;13(1):4293.

Radakovich N, Cortese M, Nazha A. Acute myeloid leukemia and artificial intelligence, algorithms and new scores. Best practice & research. Clinical haematology. 2020 ;33(3):101192.

Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Theranostics. 2022;12(16):6931.

Sudhi M, Shukla VK, Shetty DK, Gupta V, Desai AS, Naik N, Hameed BZ. Advancements in bladder cancer management: a comprehensive review of artificial intelligence and machine learning applications. Engineered Science. 2023;26(2):1003.

Chaurasia V, Pal S. Applications of machine learning techniques to predict diagnostic breast cancer. SN Computer Science. 2020;1(5):270.

Shakeel PM, Burhanuddin MA, Desa MI. Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Computing and Applications. 2022:1-4.

Wang KS, Yu G, Xu C, Meng XH, Zhou J, Zheng C, Deng Z, Shang L, Liu R, Su S, Zhou X. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC medicine. 2021;19:1-2.

Alarcón-Zendejas AP, Scavuzzo A, Jiménez-Ríos MA, Álvarez-Gómez RM, Montiel-Manríquez R, Castro-Hernández C, Jiménez-Dávila MA, Pérez-Montiel D, González-Barrios R, Jiménez-Trejo F, Arriaga-Canon C. The promising role of new molecular biomarkers in prostate cancer: From coding and non-coding genes to artificial intelligence approaches. Prostate cancer and prostatic diseases. 2022;25(3):431-43.

Liu Z, Liu Y, Zhang W, Hong Y, Meng J, Wang J, Zheng S, Xu X. Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study. Hepatology international. 2022;16(3):577-89.

Xu HL, Gong TT, Liu FH, Chen HY, Xiao Q, Hou Y, Huang Y, Sun HZ, Shi Y, Gao S, Lou Y. Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. EClinicalMedicine. 2022;53.

Hayashi H, Uemura N, Matsumura K, Zhao L, Sato H, Shiraishi Y, Yamashita YI, Baba H. Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma. World journal of gastroenterology. 2021;27(43):7480.

Gandi C, Vaccarella L, Bientinesi R, Racioppi M, Pierconti F, Sacco E. Bladder cancer in the time of machine learning: Intelligent tools for diagnosis and management. Urologia Journal. 2021;88(2):94-102.

Sharma AK, Tiwari S, Aggarwal G, Goenka N, Kumar A, Chakrabarti P, Chakrabarti T, Gono R, Leonowicz Z, Jasiński M. Dermatologist-level classification of skin cancer using cascaded ensembling of convolutional neural network and handcrafted features based deep neural network. IEEE Access. 2022;10:17920-32.

Ram M, Afrash MR, Moulaei K, Parvin M, Esmaeeli E, Karbasi Z, Heydari S, Sabahi A. Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review. BMC cancer. 2024;24(1):1026.

Jacob M, Reddy RP, Garcia RI, Reddy AP, Khemka S, Roghani AK, Pattoor V, Sehar U, Reddy PH. Harnessing Artificial Intelligence for the detection and management of Colorectal Cancer Treatment. Cancer Prevention Research. 2024:OF1-7.

Perincheri S, Levi AW, Celli R, Gershkovich P, Rimm D, Morrow JS, Rothrock B, Raciti P, Klimstra D, Sinard J. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Modern Pathology. 2021;34(8):1588-95.

Yogeshappa VG. AI-DRIVEN PRECISION MEDICINE: REVOLUTIONIZING PERSONALIZED TREATMENT PLANS. International Journal of Computer Engineering and Technology (IJCET). 2024;15(5):455-74.

Shreve JT, Khanani SA, Haddad TC. Artificial intelligence in oncology: current capabilities, future opportunities, and ethical considerations. American Society of Clinical Oncology Educational Book. 2022;42:842-51.

Tasci E, Zhuge Y, Camphausen K, Krauze AV. Bias and class imbalance in oncologic data—towards inclusive and transferrable AI in large scale oncology data sets. Cancers. 2022 ;14(12):2897.

Kann BH, Hosny A, Aerts HJ. Artificial intelligence for clinical oncology. Cancer Cell. 2021;39(7):916-27.

Iqbal MJ, Javed Z, Sadia H, Qureshi IA, Irshad A, Ahmed R, Malik K, Raza S, Abbas A, Pezzani R, Sharifi-Rad J. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer cell international. 2021;21(1):270.

Lu SC, Swisher CL, Chung C, Jaffray D, Sidey-Gibbons C. On the importance of interpretable machine learning predictions to inform clinical decision making in oncology. Frontiers in Oncology. 2023;13:1129380.

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.

Sultan AS, Elgharib MA, Tavares T, Jessri M, Basile JR. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. Journal of Oral Pathology & Medicine. 2020;49(9):849-56.

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Published

2024-09-30

Issue

Vol. 7 No. 3 (2024): July-September 2024

Section

Research Article

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