Revolutionizing Oncology: Harnessing Artificial Intelligence for Precision Tumor Detection and Personalized Treatment with Microbiological Insight

Authors

  • Muhammad Faizan Institute of Microbiology, University of Veterinary & Animal Sciences - UVAS, Lahore, Pakistan
  • Ahram Hussain Superior University Lahore, Pakistan
  • Zainab Nadeem Institute of Microbiology, University of Veterinary & Animal Sciences - UVAS, Lahore, Pakistan
  • Amna Tariq Institute of Microbiology, University of Veterinary & Animal Sciences - UVAS, Lahore, Pakistan
  • Muhammad Irfan Arif Institute of Biotechnology, University of Veterinary & Animal Sciences - UVAS, Lahore, Pakistan
  • Muhammad Umer Chorahi Institute of Biotechnology, University of Veterinary & Animal Sciences - UVAS, Lahore, Pakistan
  • Lubna Shabbir Department of Biological Sciences, Superior University, Lahore, Pakistan
  • Samavia Mustafa Department of Biology, University of Okara, Renala Khurd, Pakistan
  • Maliha Ghaffar Department of Biology, University of Okara, Renala Khurd, Pakistan

DOI:

https://doi.org/10.70749/ijbr.v3i6.1710

Keywords:

Tumor, AI Diagnostics, Oncology, Immunotherapy, Multiomics

Abstract

The inclusion of artificial intelligence (AI) in oncology has transformed the identification and treatment of malignancies, giving remarkable accuracy and individualization. Using microbiological techniques like metagenomic sequencing, 16S rRNA sequencing, and microbial biomarker analysis, this paper explores the new uses of artificial intelligence (AI) in enhancing diagnostic accuracy, fine-tuning treatment regimens, and predicting outcomes in certain malignancies. Advanced imaging, deep learning (DL), and machine learning (ML) may improve early identification, tumor characterization, and treatment planning. The study combines recent advances, offers creative AI-based solutions, and addresses ethical concerns, model interpretation, and data quality. Two novel visualizations a bar chart and a line graph, depict the benefits of AI on diagnostic sensitivity and microbial biomarker prediction accuracy, integrating microbiological data. Directions for the future stress multi-omics and microbiome integration with big language models to assist individualized cancer treatment, enabling fair access to AI-enabled solutions.

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References

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

https://doi.org/10.1016/j.canlet.2019.12.007

2. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.

https://doi.org/10.1038/s41591-018-0300-7

3. Litjens, G., & Kooi, T. (2017). Deep learning in medical image analysis: A review. Medical Image Analysis, 39, 1–13.

https://doi.org/10.1016/j.media.2017.07.005

4. Esteva, A., & Kuprel, B. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.

https://doi.org/10.1038/nature21056

5. Sepich-Poore, G. D., & Knight, R. (2021). Intratumoral microbiome and cancer: New horizons for AI-driven diagnostics. Nature Reviews Cancer, 21(9), 581–592.

https://doi.org/10.1016/j.ccell.2021.04.008

6. Hosny, A., & Parmar, C. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500–510.

https://doi.org/10.1038/s41568-018-0016-5

7. Le, E. P. V., & Wang, Y. (2019). Artificial intelligence in radiology: A review. Journal of Medical Imaging, 6(2), 021001.

https://doi.org/10.1016/j.crad.2019.02.006

8. Harmon, S. A., & Turkbey, B. (2022). Artificial intelligence in cancer imaging: Improving detection and diagnosis. Cancer Imaging Journal, 12(3), 45–56.

9. McKinney, S. M., & Sieniek, M. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94.

https://doi.org/10.1038/s41586-019-1799-6

10. Liu, Y., & Kohlberger, T. (2018). Artificial intelligence-based breast cancer nodal metastasis detection. Archives of Pathology & Laboratory Medicine, 142(10), 1234–1242.

https://doi.org/10.5858/arpa.2018-0147-oa

11. Gillies, R. J., & Kinahan, P. E. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563–577.

12. Koh, D. M., & Papanikolaou, N. (2022). Artificial intelligence in cancer imaging: Opportunities and barriers. Communications Medicine, 2(1), 1–10.

13. Ternifi, R., Wang, Y., & Gu, J. (2024). Ultrasound microvasculature imaging with AI for breast cancer detection. European Radiology, 34(11), 7448–7462.

https://doi.org/10.1007/s00330-022-08815-2

14. Le, E. P. V., & Wang, Y. (2019). Artificial intelligence in radiology: A review. Journal of Medical Imaging, 6(2), 021001.

15. Obermeyer, Z., & Powers, B. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

16. Vamathevan, J., & Clark, D. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477.

https://doi.org/10.1038/s41573-019-0024-5

17. Subramanian, I., & Verma, S. (2020). Multi-omics data integration for cancer research. Nature Reviews Genetics, 21(12), 747–762.

18. Ao, Y., Xu, L., & Zhang, Q. (2023). Predicting immunotherapy response using AI-driven genomic analysis. Journal of Clinical Oncology, 41(7), 1234–1245.

19. Parikh, R. B., & Teeple, S. (2021). Machine learning for predicting cancer outcomes. JAMA Oncology, 7(11), 1683–1690.

20. Yang, W., & Soares, J. (2020). CDRscan: A deep learning-based platform for cancer drug response prediction. Bioinformatics, 36(6), 1845–1852.

21. Hollon, T. C., & Pandian, B. (2020). Near real-time intraoperative brain tumor diagnosis using AI. Nature Medicine, 26(1), 52–58.

https://doi.org/10.1093/neuros/nyz310_634

22. Shickel, B., & Tighe, P. J. (2018). A review of clinical decision support systems in healthcare. Journal of Healthcare Informatics Research, 2(3), 234–257.

23. Nejman, D., & Livyatan, I. (2020). The human tumor microbiome is associated with cancer progression. Cell, 180(6), 1204–1218.

24. Thomas, A. M., & Segata, N. (2019). Non-invasive cancer detection using microbial biomarkers. Nature Reviews Microbiology, 17(12), 753–764.

25. Poore, G. D., & Kopylova, E. (2023). Machine learning for tumor microbiome analysis. Nature Biotechnology, 41(4), 512–520.

26. Zitvogel, L., & Ma, Y. (2017). The microbiome in cancer immunotherapy: Diagnostic tools and therapeutic strategies. Science, 359(6382), 1366–1370.

https://doi.org/10.1126/science.aar6918

27. Routy, B., & Le Chatelier, E. (2018). Gut microbiome influences efficacy of PD-1-based immunotherapy. Science, 359(6371), 91–97.

28. Riquelme, E., & Zhang, Y. (2024). Microbial biomarkers for pancreatic cancer prognosis using AI. Cancer Research, 84(5), 678–689.

29. Gopalakrishnan, V., & Spencer, C. N. (2018). Gut microbiome modulates response to anti-PD-1 immunotherapy. Science, 359(6371), 97–103.

30. Helmink, B. A., & Khan, M. A. W. (2020). The microbiome, cancer, and immunotherapy. Nature Reviews Immunology, 20(2), 121–136.

31. Schloss, P. D., & Handelsman, J. (2005). Introducing 16S rRNA sequencing for microbial community analysis. Microbiology and Molecular Biology Reviews, 69(4), 686–699.

32. Gopalakrishnan, V., & Helmink, B. A. (2022). Microbial diversity and immunotherapy response in melanoma. Journal of Immunotherapy, 45(3), 123–134.

33. Viaud, S., & Saccheri, F. (2014). The intestinal microbiota modulates chemotherapy efficacy. Science, 345(6203), 1491–1495.

https://doi.org/10.1126/science.1240537

34. Koh, A., & Bäckhed, F. (2020). Microbial metabolomics in cancer research. Nature Reviews Microbiology, 18(3), 171–183.

35. Helmink, B. A., & Wargo, J. A. (2023). Microbial metabolites and chemotherapy response in colorectal cancer. Cancer Discovery, 13(4), 789–801.

36. Donia, M. S., & Fischbach, M. A. (2015). Metabolomics in microbiome research. Nature Chemical Biology, 11(12), 885–893.

37. Boehm, K. M., & Khosravi, P. (2021). Harnessing multimodal data for cancer prognosis. Nature Medicine, 27(12), 2072–2082.

https://doi.org/10.1038/s41568-021-00408-3

38. Zhang, Z., & Sejdic, E. (2020). Multi-omics integration with AI for precision medicine. IEEE Reviews in Biomedical Engineering, 13, 256–268.

39. Weinstein, J. N., & Collisson, E. A. (2015). The Cancer Genome Atlas: A resource for cancer research. Nature Reviews Genetics, 16(9), 520–532.

40. Yu, K.-H., & Blavatnik Institute. (2024). CHIEF: A versatile AI model for cancer diagnosis and prognosis. Nature, 631(8020), 789–799.

41. Brown, T., & Mann, B. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.

42. Lee, P., & Bubeck, S. (2023). Large language models in medical research: Opportunities and challenges. Nature, 620(7972), 245–253.

43. Goodfellow, I., & Pouget-Abadie, J. (2014). Generative adversarial nets for synthetic data generation. Advances in Neural Information Processing Systems, 27, 2672–2680.

44. Char, D. S., & Shah, N. H. (2019). The ethics of artificial intelligence in healthcare. New England Journal of Medicine, 381(25), 2471–2473.

45. Rajkomar, A., & Hardt, M. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866–872.

https://doi.org/10.7326/m18-1990

46. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions. Nature Machine Intelligence, 1(5), 206–215.

47. Panch, T., & Szolovits, P. (2018). Artificial intelligence and global health. The Lancet, 392(10153), 1927–1928.

48. FDA. (2021). Artificial intelligence and machine learning in software as a medical device. U.S. Food and Drug Administration.

49. Cabitza, F., & Rasoini, R. (2017). The role of interpretability in healthcare AI. Artificial Intelligence in Medicine, 81, 1–12.

50. Jha, S., & Topol, E. J. (2016). Adapting to artificial intelligence: Radiologists and pathologists as information specialists. JAMA, 316(22), 2353–2354.

https://doi.org/10.1001/jama.2016.17438

51. Rieke, N., & Hancox, J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 1–7.

https://doi.org/10.1038/s41746-020-00323-1

52. Hashimoto, D. A., & Rosman, G. (2018). Artificial intelligence in surgery: Promises and perils. Annals of Surgery, 268(1), 70–76.

https://doi.org/10.1097/sla.0000000000002693

53. Devlin, J., & Chang, M.-W. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

54. NCI. (2023). MOSSAIC: Multimodal AI for cancer research. National Cancer Institute.

55. Liu, Z., & Zhang, X. (2022). AI-driven precision oncology: Opportunities and challenges. Frontiers in Oncology, 12, 987654.

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Published

2025-06-30

Issue

Vol. 3 No. 6 (2025): Indus Journal of Bioscience Research

Section

Review Article

License

Copyright (c) 2025 Indus Journal of Bioscience Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Faizan, M., Hussain, A., Nadeem, Z., Tariq, A., Arif, M. I., Chorahi, M. U., Shabbir, L., Mustafa, S., & Ghaffar, M. (2025). Revolutionizing Oncology: Harnessing Artificial Intelligence for Precision Tumor Detection and Personalized Treatment with Microbiological Insight. Indus Journal of Bioscience Research, 3(6), 592-597. https://doi.org/10.70749/ijbr.v3i6.1710