Recent advances in computed tomography and magnetic resonance imaging techniques for cancer detection with artificial intelligence and machine learning integration

Mohd Faraz; Zeeshan Akram; Saqib Zameer; Urooj Fatima; Kasib Zafar; Mohd Fazil Ansari; Afsha; Mudassir Alam

doi:10.18203/2320-6012.ijrms20253652

Authors

Mohd Faraz Department of Radiodiagnosis, J. N. Medical College, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Zeeshan Akram Department of Radiodiagnosis, J. N. Medical College, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Saqib Zameer Department of Microbiology, J. N. Medical College, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Urooj Fatima Department of Radiodiagnosis, J. N. Medical College, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Kasib Zafar Department of Radiodiagnosis, J. N. Medical College, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Mohd Fazil Ansari Department of Surgery and Anaesthesiology, J. N. Medical College, Paramedical College, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
Afsha Department of Radiodiagnosis, Arogyam Institute of Paramedical and Allied Science, Roorkee, Uttarakhand, India
Mudassir Alam Department of Biological Sciences, Indian Biological Sciences and Research Institute (IBRI), Noida, Uttar Pradesh, India

DOI:

https://doi.org/10.18203/2320-6012.ijrms20253652

Keywords:

Artificial intelligence, Deep learning, Diagnostic imaging, Radiomics, Tumor detection

Abstract

Cancer is one of the leading causes of death all over the world. The International Agency for Research on Cancer (IARC) estimated 20 million new cancer cases in 2022. However, detecting cancer is difficult because it shows symptoms in the final stage. Diagnostic imaging plays a vital role in the early detection of cancer. Advanced imaging technology includes X-ray mammography, ultra-low-dose computed tomography (ULDCT), and dual-source CT, which uses two X-ray sources and improves the resolution of images. Radiomics is another advanced approach that converts medical images into quantitative data and deep learning methods (DL) using Artificial intelligence (AI), which tremendously increases the accuracy of tumor detection. Several automated and semi-automated methods are proposed to detect pulmonary nodules using DL methods such as Conventional neural network (CNNs), which include ResNet-50, VGG16, and InceptionV3. In addition to techniques that involves ionizing radiation, this review also explains the magnetic resonance imaging (MRI) techniques for cancer detection. MRI uses radio-frequency (RF) pulses to form images with high spatial resolution. Diffusion-weighted imaging (DWI), functional magnetic resonance imaging (fMRI), and MR spectroscopy (MRS) are the MRI sequences generally used for cancer detection. The aim of this paper is to provide an overview of modern CT and MR imaging techniques integrating with AI and machine learning for cancer detection.

Metrics

Metrics Loading ...

References

Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-63. DOI: https://doi.org/10.3322/caac.21834

Bhat A S, Ahmed M, Abbas K, Mustafa M, Alam M, Salem M A.S, et al. Cancer Initiation and Progression: A Comprehensive Review of Carcinogenic Substances, Anti-Cancer Therapies, and Regulatory Frameworks. Asian J Res Biochem. 2024;14(4):111-25. DOI: https://doi.org/10.9734/ajrb/2024/v14i4300

Mustafa M, Ahmad R, Tantry IQ, Ahmad W, Siddiqui S, Alam M, et al. Apoptosis: A Comprehensive Overview of Signaling Pathways, Morphological Changes, and Physiological Significance and Therapeutic Implications. Cells. 2024;13(22):1838. DOI: https://doi.org/10.3390/cells13221838

Hussain S, Mubeen I, Ullah N, Shah SSUD, Khan BA, Zahoor M, et al. Modern Diagnostic Imaging Technique Applications and Risk Factors in the Medical Field: A Review. Biomed Res Int. 2022;2022:5164970. DOI: https://doi.org/10.1155/2022/5164970

van Beek EJ, Mirsadraee S, Murchison JT. Lung cancer screening: Computed tomography or chest radiographs? World J Radiol. 2015;7(8):189-93. DOI: https://doi.org/10.4329/wjr.v7.i8.189

Sharma U, Jagannathan NR. Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism. Metabolites. 2022;12(4):295. DOI: https://doi.org/10.3390/metabo12040295

Wibmer AG, Hricak H, Ulaner GA, Weber W. Trends in oncologic hybrid imaging. Eur J Hybrid Imaging. 2018;2(1):1. DOI: https://doi.org/10.1186/s41824-017-0019-6

Jiang X, Hu Z, Wang S, Zhang Y. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers. 2023;15(14):3608. DOI: https://doi.org/10.3390/cancers15143608

Ger RB, Wei L, Naqa IE, Wang J. The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation. Semin Radiat Oncol. 2023;33(3):252-61. DOI: https://doi.org/10.1016/j.semradonc.2023.03.003

Stieb S, McDonald B, Gronberg M, Engeseth GM, He R, Fuller CD. Imaging for Target Delineation and Treatment Planning in Radiation Oncology: Current and Emerging Techniques. Hematol Oncol Clin North Am. 2019;33(6):963-75. DOI: https://doi.org/10.1016/j.hoc.2019.08.008

Gierada DS, Black WC, Chiles C, Pinsky PF, Yankelevitz DF. Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study. Radiol Imaging Cancer. 2020;2(2):e190058. DOI: https://doi.org/10.1148/rycan.2020190058

Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, et al. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020 ;93(1108):20190948. DOI: https://doi.org/10.1259/bjr.20190948

Zheng S, Cui X, Ye Z. Integrating artificial intelligence into radiological cancer imaging: from diagnosis and treatment response to prognosis. Cancer Biol Med. 2025;22(1):6-13. DOI: https://doi.org/10.20892/j.issn.2095-3941.2024.0422

Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019;9(5):1303-22. DOI: https://doi.org/10.7150/thno.30309

Bruno F, Granata V, Cobianchi Bellisari F, Sgalambro F, Tommasino E, Palumbo P, et al. Advanced Magnetic Resonance Imaging (MRI) Techniques: Technical Principles and Applications in Nanomedicine. Cancers (Basel). 2022;14(7):1626. DOI: https://doi.org/10.3390/cancers14071626

Islam MN, Azam MS, Islam MS, Kanchan MH, Parvez AHMS, Islam MM. An improved deep learning-based hybrid model with ensemble techniques for brain tumor detection from MRI image. Inform Med Unlocked. 2024;47:101483. DOI: https://doi.org/10.1016/j.imu.2024.101483

Najjar R. Clinical applications, safety profiles, and future developments of contrast agents in modern radiology: A comprehensive review. iRadiology. 2024;2(5):430-68. DOI: https://doi.org/10.1002/ird3.95

Akram Z, Faraz Md, Nigar A, Parveen R, Khan N, Anwer MR, et al. Clinical Significance of CT and MR Perfusion Imaging in Cancer Diagnosis: Techniques, Parameters, and Diagnostic Implications. J Cancer Tumor Int. 2025;15(3):43-60. DOI: https://doi.org/10.9734/jcti/2025/v15i3301

Lancaster HL, Heuvelmans MA, Oudkerk M. Low-dose computed tomography lung cancer screening: Clinical evidence and implementation research. J Intern Med. 2022;292(1):68-80. DOI: https://doi.org/10.1111/joim.13480

National Lung Screening Trial Research Team; Aberle DR, Berg CD, Black WC, Church TR, Fagerstrom RM, Galen B, et al. The National Lung Screening Trial: overview and study design. Radiology. 2011;258(1):243-53. DOI: https://doi.org/10.1148/radiol.10091808

Goo HW, Goo JM. Dual-Energy CT: New Horizon in Medical Imaging. Korean J Radiol. 2017;18(4):555-69. DOI: https://doi.org/10.3348/kjr.2017.18.4.555

Wang X, Shen H, Zhang J, Liu D, Tao J, Luo L, et al. Dual-energy CT: A new frontier in oncology imaging. Meta-Radiol. 2023;1(3):100044. DOI: https://doi.org/10.1016/j.metrad.2023.100044

Bansal A, Dhamija E, Chandrashekhara SH, Sahoo RK. Role of CT in the detection and management of cancer related complications: a study of 599 patients. E Cancer Med Sci. 2023;17:1529. DOI: https://doi.org/10.3332/ecancer.2023.1529

Bianconi F, Fravolini ML, Pizzoli S, Palumbo I, Minestrini M, Rondini M, et al. Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT. Quant Imaging Med Surg. 2021;11(7):3286-305. DOI: https://doi.org/10.21037/qims-20-1356

Zhao L, He C. Gaussian highpass guided image filtering. Digit Signal Process. 2025;165:105344. DOI: https://doi.org/10.1016/j.dsp.2025.105344

Parida P, Bhoi N. 2-D Gabor filter based transition region extraction and morphological operation for image segmentation. Computers Electrical Engineering. 2017;62:119-34. DOI: https://doi.org/10.1016/j.compeleceng.2016.10.019

Gao Y, Chen X, Yang Q, Lasso A, Kolesov I, Piper S et al. An effective and open source interactive 3D medical image segmentation solution. Sci Rep. 2024;14(1):29878. DOI: https://doi.org/10.1038/s41598-024-80206-7

Khehrah N, Farid MS, Bilal S, Khan MH. Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features. J Imaging. 2020;6(2):6. DOI: https://doi.org/10.3390/jimaging6020006

Falk Delgado A, Van Westen D, Nilsson M, Knutsson L, Sundgren PC, Larsson EM, et al. Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview. Insights Imaging. 2019;10(1):84. DOI: https://doi.org/10.1186/s13244-019-0771-1

Low RN, Barone RM, Duggan B, Bahador A, Daniels C, Veerapong J. Detection of Mesenteric Tumor Using Dynamic Contrast Enhanced MRI. Ann Surg Oncol. 2020;27(7):2525-536. DOI: https://doi.org/10.1245/s10434-020-08308-w

Fortugno AP, Bakke JR, Babajani-Feremi A, Newman J, Patel TS. Functional Magnetic Resonance Imaging and Applications in Dermatology. JID Innov. 2021;1:3. DOI: https://doi.org/10.1016/j.xjidi.2021.100015

Jeon JY, Chung HW, Lee MH, Lee SH, Shin MJ. Usefulness of diffusion-weighted MR imaging for differentiating between benign and malignant superficial soft tissue tumours and tumour-like lesions. Br J Radiol. 2016;89(1060):20150929. DOI: https://doi.org/10.1259/bjr.20150929

Laino ME, Young R, Beal K, Haque S, Mazaheri Y, Corrias G. Magnetic resonance spectroscopic imaging in gliomas: clinical diagnosis and radiotherapy planning. BJR Open. 2020;2(1):20190026. DOI: https://doi.org/10.1259/bjro.20190026

Weinberg BD, Kuruva M, Shim H, Mullins ME. Clinical Applications of Magnetic Resonance Spectroscopy in Brain Tumors: From Diagnosis to Treatment. Radiol Clin North Am. 2021;59(3):349-62. DOI: https://doi.org/10.1016/j.rcl.2021.01.004

Grover VPB, Tognarelli JM, Crossey MME, Cox IJ, Taylor-Robinson SD, McPhail MJW. Magnetic Resonance Imaging: Principles and Techniques: Lessons for Clinicians. J Clin Exp Hepatol. 2015;5(3):246-55. DOI: https://doi.org/10.1016/j.jceh.2015.08.001

Read GH, Bailleul J, Vlashi E, Kesarwala AH. Metabolic response to radiation therapy in cancer. Mol Carcinog. 2022;61(2):200-24. DOI: https://doi.org/10.1002/mc.23379

Zameer S, Akram Z, Daniyal A, Daniyal A, Fatima U, Faraz M, et al. Apoptosis Mechanisms: Role of Anti-apoptotic Proteins, Cancer Hallmarks and Tumor Microenvironment in Cancer Cell Survival. Biotechnol J Int. 2025;29(2):27-36. DOI: https://doi.org/10.9734/bji/2025/v29i2769

Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med. 2022;146:105580. DOI: https://doi.org/10.1016/j.compbiomed.2022.105580

Liao Y, Liu H, Spasić I. Deep learning approaches to automatic radiology report generation: A systematic review. Informat Med Unlocked. 2023;39:101273. DOI: https://doi.org/10.1016/j.imu.2023.101273

Alaca Y, Akmeşe ÖF. Pancreatic Tumor Detection From CT Images Converted to Graphs Using Whale Optimization and Classification Algorithms With Transfer Learning. Int J Imaging Syst Technol. 2025;35(2):e70040. DOI: https://doi.org/10.1002/ima.70040

Mall PK, Singh PK, Srivastav S, Narayan V, Paprzycki M, Jaworska T, et al. A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthc Anal. 2023;4:100216. DOI: https://doi.org/10.1016/j.health.2023.100216

Ilesanmi AE, Ilesanmi TO, Ajayi BO. Reviewing 3D convolutional neural network approaches for medical image segmentation. Heliyon. 2024;10:6. DOI: https://doi.org/10.1016/j.heliyon.2024.e27398

Yeasmin MN, Al Amin M, Joti TJ, Aung Z, Azim MA. Advances of AI in image-based computer-aided diagnosis: A review. Array. 2024;23:100357. DOI: https://doi.org/10.1016/j.array.2024.100357

Xiao X, Zhao J, Li S. Task relevance driven adversarial learning for simultaneous detection, size grading, and quantification of hepatocellular carcinoma via integrating multi-modality MRI. Med Image Anal. 2022;81:102554. DOI: https://doi.org/10.1016/j.media.2022.102554

Lassau N, Bousaid I, Chouzenoux E, Lamarque JP, Charmettant B, Azoulay M, et al. Three artificial intelligence data challenges based on CT and MRI. Diagn Interv Imaging. 2020;101(12):783-8. DOI: https://doi.org/10.1016/j.diii.2020.03.006

Herm LV, Heinrich K, Wanner J, Janiesch C. Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. Int J Inf Manag. 2023;69:102538. DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102538

Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-10. DOI: https://doi.org/10.1038/s41568-018-0016-5

Chakraborty C, Bhattacharya M, Pal S, Lee SS. From machine learning to deep learning: Advances of the recent data-driven paradigm shift in medicine and healthcare. Curr Res Biotechnol. 2024;7:100164. DOI: https://doi.org/10.1016/j.crbiot.2023.100164

Lips M. ALARA in practice-4 decades of radiological protection at Goesgen NPP. J Radiol Prot Off J Soc Radiol Prot. 2021;41:4. DOI: https://doi.org/10.1088/1361-6498/ac1a82

Do KH. General Principles of Radiation Protection in Fields of Diagnostic Medical Exposure. J Korean Med Sci. 2016;31:S6-9. DOI: https://doi.org/10.3346/jkms.2016.31.S1.S6

Esquivel A, Ferrero A, Mileto A, Baffour F, Horst K, Rajiah PS, et al . Photon-Counting Detector CT: Key Points Radiologists Should Know. Korean J Radiol. 2022;23(9):854-65. DOI: https://doi.org/10.3348/kjr.2022.0377

Kroft LJM, van der Velden L, Girón IH, Roelofs JJH, de Roos A, Geleijns J. Added Value of Ultra–low-dose Computed Tomography, Dose Equivalent to Chest X-Ray Radiography, for Diagnosing Chest Pathology. J Thorac Imaging. 2019;34(3):179. DOI: https://doi.org/10.1097/RTI.0000000000000404

Paudyal R, Shah AD, Akin O, Do RKG, Konar AS, Hatzoglou V, et al. Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers (Basel). 2023;15(9):2573. DOI: https://doi.org/10.3390/cancers15092573

Platt T, Ladd ME, Paech D. 7 Tesla and Beyond: Advanced Methods and Clinical Applications in Magnetic Resonance Imaging. Invest Radiol. 2021;56(11):705. DOI: https://doi.org/10.1097/RLI.0000000000000820

van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022;79:102470. DOI: https://doi.org/10.1016/j.media.2022.102470