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Jajroudi, Mahdie, Afrash, Mohammad Reza, Yadegari, Fateme, Najafi Fadafen, Seyyed Ali, Movassagh, Ali Akbar, Alnattah, Ashraf, Geramifar, Parham, Eslami, Saied. (1405). Deep Learning-Based Object Detection in Oncology: A Systematic Scoping Review of CT, MRI, and PET/CT Applications. سامانه مدیریت نشریات علمی, 13(3), 166-183. doi: 10.30476/jhmi.2026.110527.1354
Mahdie Jajroudi; Mohammad Reza Afrash; Fateme Yadegari; Seyyed Ali Najafi Fadafen; Ali Akbar Movassagh; Ashraf Alnattah; Parham Geramifar; Saied Eslami. "Deep Learning-Based Object Detection in Oncology: A Systematic Scoping Review of CT, MRI, and PET/CT Applications". سامانه مدیریت نشریات علمی, 13, 3, 1405, 166-183. doi: 10.30476/jhmi.2026.110527.1354
Jajroudi, Mahdie, Afrash, Mohammad Reza, Yadegari, Fateme, Najafi Fadafen, Seyyed Ali, Movassagh, Ali Akbar, Alnattah, Ashraf, Geramifar, Parham, Eslami, Saied. (1405). 'Deep Learning-Based Object Detection in Oncology: A Systematic Scoping Review of CT, MRI, and PET/CT Applications', سامانه مدیریت نشریات علمی, 13(3), pp. 166-183. doi: 10.30476/jhmi.2026.110527.1354
Jajroudi, Mahdie, Afrash, Mohammad Reza, Yadegari, Fateme, Najafi Fadafen, Seyyed Ali, Movassagh, Ali Akbar, Alnattah, Ashraf, Geramifar, Parham, Eslami, Saied. Deep Learning-Based Object Detection in Oncology: A Systematic Scoping Review of CT, MRI, and PET/CT Applications. سامانه مدیریت نشریات علمی, 1405; 13(3): 166-183. doi: 10.30476/jhmi.2026.110527.1354

Deep Learning-Based Object Detection in Oncology: A Systematic Scoping Review of CT, MRI, and PET/CT Applications

Health Management & Information Science
دوره 13، شماره 3، مهر 2026، صفحه 166-183    اصل مقاله (935.43 K)
نوع مقاله: Review
شناسه دیجیتال (DOI): 10.30476/jhmi.2026.110527.1354
نویسندگان
Mahdie Jajroudi1؛ Mohammad Reza Afrash2؛ Fateme Yadegari1؛ Seyyed Ali Najafi Fadafen3؛ Ali Akbar Movassagh4؛ Ashraf Alnattah5؛ Parham Geramifar* 6؛ Saied Eslami* 1
1Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
2Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran
3Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
4Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
5Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
6Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
چکیده
Introduction: The global rise in cancer incidence highlights the urgent need for more effective diagnostic tools to support early detection and optimize treatment planning. Deep learning-based object detection (OD) has emerged as a major advance in medical imaging, automating lesion identification and classification. By improving the analysis of CT, MRI, and PET/CT scans, OD reduces interpretation time, enhances diagnostic accuracy, and promotes more consistent clinical decision-making in oncology. This review maps the clinical applications of deep learning-based OD in cancer imaging, evaluates the diagnostic performance of various architectures, and identifies barriers to routine clinical adoption.
Methods: Following the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, PubMed and Scopus were systematically searched for studies published between 2014 and January 2026. Peer-reviewed studies applying deep learning-based OD models to cancer imaging using CT, MRI, or PET/CT were included. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.
Results: Of the 46 included studies, 27 utilized one-stage detectors, 11 used two-stage detectors, and 5 employed both approaches. One-stage models were favored for their efficiency, while two-stage models demonstrated superior detection of small or low-contrast lesions. Applications included early cancer detection, lesion classification, and evaluation of treatment response.
Conclusion: Deep learning-based OD demonstrates high diagnostic accuracy and potential to optimize oncologic imaging workflows. However, challenges such as validation, generalizability, and integration into clinical practice limit widespread adoption. Addressing these barriers is critical to realizing the full potential of OD in improving cancer management.

تازه های تحقیق

Mahdie Jajroudi: Google Scholar

Parham Geramifar: Google Scholar

Saeid Eslami: Google Scholar

کلیدواژه‌ها
Object Detection؛ Deep Learning؛ Cancer؛ Magnetic Resonance Imaging؛ Computed Tomography؛ Positron Emission Tomography - Computed Tomography
مراجع
  1. 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. 10.3322/caac.21834.
  2. Ibrahim A, Mohamed HK, Maher A, Zhang B. A Survey on Human Cancer Categorization Based on Deep Learning. Front Artif Intell. 2022;5:884749. 10.3389/frai.2022.884749.
  3. Chen MM, Terzic A, Becker AS, Johnson JM, Wu CC, Wintermark M, et al. Artificial intelligence in oncologic imaging. Eur J Radiol Open. 2022;9:100441. 10.1016/j.ejro.2022.100441.
  4. 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). 10.3390/cancers15092573.
  5. Ahmad I, Alqurashi F. Early cancer detection using deep learning and medical imaging: A survey. Crit Rev Oncol Hematol. 2024;204:104528. 10.1016/j.critrevonc.2024.104528.
  6. Shamshad F, Khan S, Zamir SW, Khan MH, Hayat M, Khan FS, et al. Transformers in medical imaging: A survey. Medical image analysis. 2023;88:102802.
  7. Jiang X, Hu Z, Wang S, Zhang Y. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers (Basel). 2023;15(14). 10.3390/cancers15143608.
  8. Sobek J, Medina Inojosa JR, Medina Inojosa BJ, Rassoulinejad-Mousavi SM, Conte GM, Lopez-Jimenez F, et al. MedYOLO: A Medical Image Object Detection Framework. J Imaging Inform Med. 2024;37(6):3208-16. 10.1007/s10278-024-01138-2.
  9. Picarra C, Winzeck S, Monteiro M, Mathieu F, Newcombe VFJ, Menon PDK, et al. Automatic localisation and per-region quantification of traumatic brain injury on head CT using atlas mapping. Eur J Radiol Open. 2023;10:100491. 10.1016/j.ejro.2023.100491.
  10. Suganyadevi S, Seethalakshmi V, Balasamy K. A review on deep learning in medical image analysis. Int J Multimed Inf Retr. 2022;11(1):19-38. 10.1007/s13735-021-00218-1.
  11. Abdusalomov A, Rakhimov M, Karimberdiyev J, Belalova G, Cho YI. Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments. Bioengineering (Basel). 2024;11(6). 10.3390/bioengineering11060627.
  12. Kim JS, Cho Y, Lim TH. Prediction of locations in medical images using orthogonal neural networks. Eur J Radiol Open. 2021;8:100388. 10.1016/j.ejro.2021.100388.
  13. Sakha MA, Ameri A. Detection and Classification of Lesions in Mammograms using One-Stage Models. Journal of Biomedical Physics and Engineering. 2025.
  14. Liu Y, Li C, Li F, Lin R, Zhang D, Lian Y. Advances in computer vision and deep learning-facilitated early detection of melanoma. Briefings in Functional Genomics. 2025;24:elaf002.
  15. Paudyal R, Grkovski M, Oh JH, Schöder H, Nunez DA, Hatzoglou V, et al. Application of community detection algorithm to investigate the correlation between imaging biomarkers of tumor metabolism, hypoxia, cellularity, and perfusion for precision radiotherapy in head and neck squamous cell carcinomas. Cancers. 2021;13(15). 10.3390/cancers13153908.
  16. Paudyal R, Grkovski M, Oh JH, Schöder H, Nunez DA, Hatzoglou V, et al. Application of Community Detection Algorithm to Investigate the Correlation between Imaging Biomarkers of Tumor Metabolism, Hypoxia, Cellularity, and Perfusion for Precision Radiotherapy in Head and Neck Squamous Cell Carcinomas. Cancers (Basel). 2021;13(15). 10.3390/cancers13153908.
  17. Qasem A, Zhou Z. Automated tumor localization and segmentation through hybrid neural network in head and neck cancer. Medical Dosimetry. 2025;50(1):80-90. 10.1016/j.meddos.2024.09.001.
  18. Yang H, Yang M, Chen J, Yao G, Zou Q, Jia L. Multimodal deep learning approaches for precision oncology: a comprehensive review. Briefings in Bioinformatics. 2025;26(1):bbae699.
  19. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, et al., editors. Ssd: Single shot multibox detector. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14; 2016: Springer.
  20. Redmon J, Divvala S, Girshick R, Farhadi A, editors. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016.
  21. Ren S, He K, Girshick R, Sun J. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. 2015;28.
  22. Tang C, Zhou F, Sun J, Zhang Y. Lung-YOLO: Multiscale feature fusion attention and cross-layer aggregation for lung nodule detection. Biomedical Signal Processing and Control. 2025;99. 10.1016/j.bspc.2024.106815.
  23. Ahsan R, Shahzadi I, Najeeb F, Omer H. Brain tumor detection and segmentation using deep learning. Magnetic Resonance Materials in Physics, Biology and Medicine. 2025;38(1):13-22. 10.1007/s10334-024-01203-5.
  24. Zhang M, Sun L, Li X, Zhu W, Yi Y, Yan F. A multi-scale self-calibrating lung nodule detection based on SPC-UNet. Biomedical Signal Processing and Control. 2025;104. 10.1016/j.bspc.2025.107515.
  25. Anari PY, Obiezu F, Lay N, Firouzabadi FD, Chaurasia A, Golagha M, et al. Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging. ArXiv. 2024.
  26. Liu Y, Ao Y. Deformable attention mechanism-based YOLOv7 structure for lung nodule detection. Journal of Supercomputing. 2024;80(17):25450-69. 10.1007/s11227-024-06381-6.
  27. Mehralivand S, Yang D, Harmon SA, Xu D, Xu Z, Roth H, et al. A Cascaded Deep Learning–Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging. Academic Radiology. 2022;29(8):1159-68. 10.1016/j.acra.2021.08.019.
  28. Aishwarya KV, Asuntha A, Murugan J. Optimizing pulmonary nodule segmentation in CT imaging: A comparative study of anchor-based and anchor-free detectors using attention mechanism. Multimedia Tools and Applications. 2025. 10.1007/s11042-025-20752-y.
  29. Qasem A, Zhou Z. Automated tumor localization and segmentation through hybrid neural network in head and neck cancer. Med Dosim. 2025;50(1):80-90. 10.1016/j.meddos.2024.09.001.
  30. Muksimova S, Umirzakova S, Mardieva S, Iskhakova N, Sultanov M, Cho YI. A lightweight attention-driven YOLOv5m model for improved brain tumor detection. Computers in Biology and Medicine. 2025;188. 10.1016/j.compbiomed.2025.109893.
  31. Jiang C, Li Y, Luo H, Zhang C, Du H. KansNet: Kolmogorov–Arnold Networks and multi slice partition channel priority attention in convolutional neural network for lung nodule detection. Biomedical Signal Processing and Control. 2025;103. 10.1016/j.bspc.2024.107358.
  32. Xu D, Ma M, Cao M, Kishan AU, Nickols NG, Scalzo F, et al. Mask R-CNN assisted 2.5D object detection pipeline of 68Ga-PSMA-11 PET/CT-positive metastatic pelvic lymph node after radical prostatectomy from solely CT imaging. Scientific Reports. 2023;13(1). 10.1038/s41598-023-28669-y.
  33. Zhang S, Miao Y, Chen J, Zhang X, Han L, Huang Z, et al. CCS-Net: Cascade Detection Network With the Convolution Kernel Switch Block and Statistics Optimal Anchors Block in Hypopharyngeal Cancer MRI. IEEE Journal of Biomedical and Health Informatics. 2023;27(1):433-44. 10.1109/JBHI.2022.3217174.
  34. Du L, Zhang R, Wang X, editors. Overview of two-stage object detection algorithms. Journal of Physics: Conference Series; 2020: IOP Publishing.
  35. Hikmah NF, Hajjanto AD, Surbakti AFA, Prakosa NA, Asmaria T, Sardjono TA. Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels. International Journal of Electrical and Computer Engineering. 2024;14(4):3995-4004. 10.11591/ijece.v14i4.pp3995-4004.
  36. Zamanidoost Y, Ould-Bachir T, Martel S. OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN. IEEE Journal of Biomedical and Health Informatics. 2025;29(3):2148-60. 10.1109/JBHI.2024.3507360.
  37. Dai J, He C, Jin L, Chen C, Wu J, Bian Y. A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture. Journal of X-ray science and technology. 2025;33(2):461-71. 10.1177/08953996251313719.
  38. Son JW, Hong JY, Kim Y, Kim WJ, Shin DY, Choi HS, et al. How Many Private Data are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study. Cancers. 2022;14(13). 10.3390/cancers14133174.
  39. Ito S, Nakashima H, Segi N, Ouchida J, Oda M, Yamauchi I, et al. Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging. Journal of Clinical Medicine. 2023;12(15). 10.3390/jcm12155075.
  40. Terzi R. An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI. Diagnostics (Basel). 2023;13(8). 10.3390/diagnostics13081494.
  41. Chaudhary A, Mittal K, Tejpal B, Thakur K, Varughese DA, Sridevi S. ENHANCING CNN PERFORMANCE WITH RIGHT-NEIGHBOR DEVIATION (RND) POOLING: A FOCUS ON MULTI-CLASS BRAIN TUMOR CLASSIFICATION IN MRI IMAGES. Biomedical Engineering - Applications, Basis and Communications. 2025;37(2). 10.4015/S1016237224500467.
  42. Abdusalomov A, Rakhimov M, Karimberdiyev J, Belalova G, Cho YI. Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments. Bioengineering. 2024;11(6). 10.3390/bioengineering11060627.
  43. Chai YF, Zhang XX. Enhanced Chest CT Detection of Pulmonary Nodules Based on YOLOv8. Engineering Letters. 2024;32(12):2221-31.
  44. Wang H, Tang N, Zhang C, Hao Y, Meng X, Li J. Practice toward standardized performance testing of computer-aided detection algorithms for pulmonary nodule. Frontiers in Public Health. 2022;10. 10.3389/fpubh.2022.1071673.
  45. Song W, Tang F, Marshall H, Fong KM, Liu F. A multiscale 3D network for lung nodule detection using flexible nodule modeling. Medical Physics. 2024;51(10):7356-68. 10.1002/mp.17283.
  46. Swinburne NC, Yadav V, Kim J, Choi YR, Gutman DC, Yang JT, et al. Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations. Radiology. 2022;303(1):80-9. 10.1148/RADIOL.210817.
  47. Gui H, Su T, Jiang X, Li L, Xiong L, Zhou J, et al. FS-YOLOv9: A Frequency and Spatial Feature-Based YOLOv9 for Real-time Breast Cancer Detection. Academic Radiology. 2025;32(3):1228-40. 10.1016/j.acra.2024.09.048.
  48. Saidani T. Efficient Tumor Detection in Medical Imaging Using Advanced Object Detection Model: A Deep Learning Approach. International Journal of Advanced Computer Science and Applications. 2025;16(1):1139-45. 10.14569/IJACSA.2025.01601109.
  49. Ahmadyar Y, Kamali-Asl A, Arabi H, Samimi R, Zaidi H. Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier. Radiological Physics and Technology. 2024;17(1):124-34. 10.1007/s12194-023-00756-9.
  50. Manokaran J, Mittal R, Ukwatta E. Pulmonary nodule detection in low dose computed tomography using a medical-to-medical transfer learning approach. Journal of Medical Imaging. 2024;11(4):44502. 10.1117/1.JMI.11.4.044502.
  51. Pandey SK, Bhandari AK. YOLOv7 for brain tumour detection using morphological transfer learning model. Neural Computing and Applications. 2024;36(32):20321-40. 10.1007/s00521-024-10246-7.
  52. Piao Z, Meng M, Yang H, Xue T, Jia Z, Liu W. Distinguishing between aldosterone-producing adenomas and non-functional adrenocortical adenomas using the YOLOv5 network. Acta Radiologica. 2024;65(8):1007-14. 10.1177/02841851241251446.
  53. Suryawanshi S, Patil SB. Brain Tumor Detection Using YoloV5 and Faster RCNN. International Journal of Intelligent Systems and Applications in Engineering. 2024;12(4s):335-42.
  54. Randar S, Shah V, Kulkarni H, Suryawanshi Y, Joshi A, Sawant S. YOLOv8-Based Frameworks for Liver and Tumor Segmentation Task on LiTS. SN Computer Science. 2024;5(6). 10.1007/s42979-024-03097-5.
  55. Gao Z, Guo Y, Wang G, Chen X, Cao X, Zhang C, et al. Robust deep learning from incomplete annotation for accurate lung nodule detection. Comput Biol Med. 2024;173:108361. 10.1016/j.compbiomed.2024.108361.
  56. Terzi DS, Azginoglu N. In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI. Diagnostics. 2023;13(12). 10.3390/diagnostics13122110.
  57. Xie F, Ju J, Zhang T, Wang H, Liu J, Wang J, et al. A Small Intestinal Stromal Tumor Detection Method Based on an Attention Balance Feature Pyramid. Sensors. 2023;23(24). 10.3390/s23249723.
  58. Oh JH, Lee KM, Kim HG, Yoon JT, Kim EJ. Deep learning-based detection algorithm for brain metastases on black blood imaging. Scientific Reports. 2022;12(1). 10.1038/s41598-022-23687-8.
  59. Shelatkar T, Urvashi D, Shorfuzzaman M, Alsufyani A, Lakshmanna K. Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach. Computational and Mathematical Methods in Medicine. 2022;2022. 10.1155/2022/2858845.
  60. Xu J, Ren H, Cai S, Zhang X. An improved faster R-CNN algorithm for assisted detection of lung nodules. Computers in Biology and Medicine. 2023;153. 10.1016/j.compbiomed.2022.106470.
  61. Chegraoui H, Philippe C, Dangouloff-Ros V, Grigis A, Calmon R, Boddaert N, et al. Object detection improves tumour segmentation in mr images of rare brain tumours. Cancers. 2021;13(23). 10.3390/cancers13236113.
  62. Gunasekara SR, Kaldera HNTK, Dissanayake MB. A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring. Journal of Healthcare Engineering. 2021;2021. 10.1155/2021/6695108.
  63. Ito S, Ando K, Kobayashi K, Nakashima H, Oda M, MacHino M, et al. Automated Detection of Spinal Schwannomas Utilizing Deep Learning Based on Object Detection From Magnetic Resonance Imaging. Spine. 2021;46(2):95-100. 10.1097/BRS.0000000000003749.
  64. Peng X, Yang X. Liver tumor detection based on objects as points. Physics in Medicine and Biology. 2021;66(23). 10.1088/1361-6560/ac35c7.
  65. Takao H, Amemiya S, Kato S, Yamashita H, Sakamoto N, Abe O. Deep-learning single-shot detector for automatic detection of brain metastases with the combined use of contrast-enhanced and non-enhanced computed tomography images. European Journal of Radiology. 2021;144. 10.1016/j.ejrad.2021.110015.
  66. Adachi M, Fujioka T, Mori M, Kubota K, Kikuchi Y, Xiaotong W, et al. Detection and diagnosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images. Diagnostics. 2020;10(5). 10.3390/diagnostics10050330.
  67. Su Y, Li D, Chen X. Lung Nodule Detection based on Faster R-CNN Framework. Computer Methods and Programs in Biomedicine. 2021;200. 10.1016/j.cmpb.2020.105866.
  68. Zhao Z, Ye C, Hu Y, Li C, Li X. Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT. Comput Intell Neurosci. 2019;2019:7401235. 10.1155/2019/7401235.
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