Machine Learning in MRI-Based Cancer Characterization: Enhancing Precision and Early Detection

This study examines how MRI and ML can improve cancer detection and early detection. Better cancer diagnostics and earlier detection are the goals. Test and evaluate machine learning methods for different cancers. These include CNNs, SVMs, RFs, and ensemble approaches. Public datasets were analyzed. The AUC-ROC, sensitivity, specificity, and detection rate were used to evaluate the results. Using radiomics features with deep learning architectures to improve diagnostic skills is also considered. Ensemble systems that use many machine learning (ML) methods outperform solo algorithms with an average precision of 92.7 percent. A list of the primary issues that must be addressed for practical translation is also provided. This requires larger and more diverse datasets and improved understanding and application of learned information. Multimodal integration and federated learning approaches may be studied in the future to solve current issues.

Machine Learning, Deep Learning, MRI, Cancer Detection, Computer-Aided Diagnosis, Radiomics, Convolutional Neural Networks

1. He, M.; Cao, Y.; Chi, C.; Yang, X.; Ramin, R.; Wang, S.; Hu, K. Research progress on deep learning in magnetic resonance imaging–based diagnosis and treatment of prostate cancer: A review on the current status and perspectives. Front. Oncol. 2023, 13, 1189370. [Google Scholar] [Crossref]

2. Li, H.; Lee, C.H.; Chia, D.; Lin, Z.; Huang, W.; Tan, C.H. Machine learning in prostate MRI for prostate cancer: Current status and future opportunities. Diagnostics 2022, 12, 289. [Google Scholar] [Crossref]

3. American Cancer Society. Cancer Facts & Figures 2024. 2024. Available online: [Google Scholar] [Crossref]

4. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2024cancer-facts-figures.html (accessed on 17 January 2024). [Google Scholar] [Crossref]

5. Fernandes, M.C.; Yildirim, O.; Woo, S.; Vargas, H.A.; Hricak, H. The role of MRI in prostate cancer: Current and future directions. Magn. Reson. Mater. Phys. Biol. Med. 2022, 35, 503–521. [Google Scholar] [Crossref]

6. Suarez-Ibarrola, R.; Sigle, A.; Eklund, M.; Eberli, D.; Miernik, A.; Benndorf, M.; Gratzke, C. Artificial intelligence in magnetic resonance imaging–based prostate cancer diagnosis: Where do we stand in 2021? Eur. Urol. Focus 2022, 8, 409–417. [Google Scholar] [Crossref]

7. Kryvenko, O.N.; Epstein, J.I. Prostate cancer grading: A decade after the 2005 modified Gleason grading system. Arch. Pathol. Lab. Med. 2016, 140, 1140–1152. [Google Scholar] [Crossref]

8. Heijmink, S.W.; van Moerkerk, H.; Kiemeney, L.A.; Witjes, J.A.; Frauscher, F.; Barentsz, J.O. A comparison of the diagnostic performance of systematic versus ultrasound-guided biopsies of prostate cancer. Eur. Radiol. 2006, 16, 927–938. [Google Scholar] [Crossref]

9. Sieren, J.C.; Ohno, Y.; Koyama, H.; Sugimura, K.; McLennan, G. Recent technological and application developments in computed tomography and magnetic resonance imaging for improved pulmonary nodule detection and lung cancer staging. J. Magn. Reson. Imaging 2010, 32, 1353–1369. [Google Scholar] [Crossref]

10. Li, B.; Du, Y.; Yang, H.; Huang, Y.; Meng, J.; Xiao, D. Magnetic resonance imaging for prostate cancer clinical application. Chin. J. Cancer Res. 2013, 25, 240. [Google Scholar] [Crossref]

11. Turkbey, B.; Brown, A.M.; Sankineni, S.; Wood, B.J.; Pinto, P.A.; Choyke, P.L. Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer. CA Cancer J. Clin. 2016, 66, 326–336. [Google Scholar] [Crossref]

12. Barbosa, F.D.G.; Queiroz, M.A.; Nunes, R.F.; Marin, J.F.G.; Buchpiguel, C.A.; Cerri, G.G. Clinical perspectives of PSMA PET/MRI for prostate cancer. Clinics 2018, 73 (Suppl. 1), e586s. [Google Scholar] [Crossref]

13. Manafi-Farid, R.; Ranjbar, S.; Jamshidi Araghi, Z.; Pilz, J.; Schweighofer-Zwink, G.; Pirich, C.; Beheshti, M. Molecular imaging in primary staging of prostate cancer patients: Current aspects and future trends. Cancers 2021, 13, 5360. [Google Scholar] [Crossref]

14. Zhu, L.; Pan, J.; Mou, W.; Deng, L.; Zhu, Y.; Wang, Y.; Xue, W. Harnessing artificial intelligence for prostate cancer management. Cell Rep. Med. 2024, 5, 101506. [Google Scholar] [Crossref]

15. Fischer, S.; Tahoun, M.; Klaan, B.; Thierfelder, K.M.; Weber, M.A.; Krause, B.J.; Hamed, M. A radiogenomic approach for decoding molecular mechanisms underlying tumor progression in prostate cancer. Cancers 2019, 11, 1293. [Google Scholar] [Crossref]

16. Sadeghi, Z.; Alizadehsani, R.; CIFCI, M.A.; Kausar, S.; Rehman, R.; Mahanta, P.; Bora, P.K.; Almasri, A.; Alkhawaldeh, R.S.; Hussain, S.; et al. A review of Explainable Artificial Intelligence in healthcare. Comput. Electr. Eng. 2024, 118, 109370. [Google Scholar] [Crossref]

17. Mahmood, T.; Rehman, A.; Saba, T.; Nadeem, L.; Bahaj, S.A.O. Recent advancements and future prospects in active deep learning for medical image segmentation and classification. IEEE Access 2023, 11, 26143–26159. [Google Scholar] [Crossref]

18. Cifci, M.A. A deep learning-based framework for uncertainty quantification in medical imaging using the DropWeak technique: An empirical study with baresnet. Diagnostics 2023, 13, 800. [Google Scholar] [Crossref]

19. Alkadi, R.; Taher, F.; El-Baz, A.; Werghi, N. A deep learning-based approach for the detection and localization of prostate cancer in T2 magnetic resonance images. J. Digit. [Google Scholar] [Crossref]

20. Imaging 2019, 32, 793–807. [Google Scholar] [Crossref]

21. Salvi, M.; De Santi, B.; Pop, B.; Bosco, M.; Giannini, V.; Regge, D.; Meiburger, K.M. Integration of deep learning and active shape models for more accurate prostate segmentation in 3D MR images. J. Imaging 2022, 8, 133. [Google Scholar] [Crossref]

22. Almestad, E. Exploring Explainable AI Adoption in Medical Diagnosis and the Empowering Potential of Collaboration. Master’s Thesis, NTNU, Trondheim, Norway, 2023. [Google Scholar] [Crossref]

• "Towards GDP to GEP-Centric Model: A Proposed GEP Index Framework and Its Application in Haryana"
• Physicochemical Analysis of Petroleum Products in Selected Depots in Calabar Metropolis Cross River State and The Effects on Motor Engine
• Impact of Employee Welfare Measures on Workforce Well-Being in SBI: Insights from the Public Banking Sector
• Problem of Small-Scale Farmers in Agricutlure Sector in Tirunelveli Taluk
• Globalization, Economic Development, and Ecological Footprint in Tunisia: A QARDL Approach