Role of Three-Dimensional Convolution Neural Networks (3D- CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis
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Abstract
Three-dimensional convolutional neural networks (3D CNNs) have transformed oncology imaging, excelling in tumor detection, classification, segmentation, and prognosis prediction. Unlike traditional two-dimensional CNNs, 3D CNNs effectively analyze volumetric medical imaging data, enhancing spatial feature extraction and diagnostic accuracy across modalities, including CT, MRI, PET, and ultrasound.
This systematic review and meta-analysis evaluates the diagnostic performance and clinical utility of 3D CNNs across 22 studies, of which 11 were eligible for quantitative synthesis. Pooled sensitivity, specificity, and AUC were 0.72, 0.73, and 0.77, respectively, with a diagnostic odds ratio of 10.38, indicating favorable discriminative ability. Subgroup analyses demonstrated superior accuracy in lung cancer and CT-based models, with DenseNet and ResNet architectures outperforming traditional CNNs.
Technical innovations—including multi-modal fusion, spatial context integration, and explainable AI techniques—enhance model robustness and clinician trust. However, substantial heterogeneity (I² > 95%) across studies, attributable to differences in imaging protocols, dataset quality, and model design, underscores the need for standardized methodologies. Persistent challenges include computational demands, annotation variability, and generalization limitations.
Future directions should prioritize the integration of explainable AI, PACS-compatible user interfaces, and federated learning frameworks to bridge institutional gaps. This review highlights the considerable promise of 3D CNNs in advancing precision oncology, while also identifying the infrastructural and methodological refinements necessary for widespread clinical adoption.
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12. Meng L, Tian Y, Bu S. Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys. 2020;21(1):144-157.
13. Mzoughi H, Njeh I, Wali A, et al. Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification. J Digit Imaging. 2020;33(4):903-915.
14. Mlynarski P, Delingette H, Criminisi A, et al. 3D convolutional neural networks for tumor segmentation using long-range 2D context. Comput Med Imaging Graph. 2019;73:60-72.
15. Ilesanmi AE, Ilesanmi TO, Ajayi BO. Reviewing 3D convolutional neural network approaches for medical image segmentation. Heliyon. 2024;10(6):e27398.
16. Xu S, Liu C, Zong Y, et al. An early diagnosis of oral cancer based on three-dimensional convolutional neural networks. IEEE Access. 2019;7:158603-11.
17. Huang X, Sun Y, Tan M, et al. Three-dimensional convolutional neural network-based prediction of epidermal growth factor receptor expression status in patients with non-small cell lung cancer. Front Oncol. 2022;12:772770.
18. Perez G, Arbelaez P. Automated lung cancer diagnosis using three-dimensional convolutional neural networks. Med Biol Eng Comput. 2020;58(8):1803-1815.
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24. Hooijmans CR, Rovers MM, De Vries RB, et al. SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol. 2014; 14: 43.
25. Alakwaa W, Nassef M, Badr A. Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). Int. J. Adv. Comput. Sci. Appl.2017;8(8).
26. Zhang Z, Chen L, Wang Y, et al. Label-Free Estimation of Therapeutic Efficacy on 3D Cancer Spheres Using Convolutional Neural Network Image Analysis. Anal Chem. 2019;91(21):14093-14100.
27. Wei R, Zhou F, Liu B, et al. Convolutional neural network (CNN) based three dimensional tumor localization using single X-ray projection. IEEE Access. 2019;7:37026-38.
28. Starke S, Leger S, Zwanenburg A, et al. 2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma. Sci Rep. 2020;10:15625.
29. Camalan S, Mahmood H, Binol H, et al. Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results. Cancers. 2021;13(6):1291.
30. Yanagawa M, Niioka H, Kusumoto M, et al. Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network. Eur Radiol. 2021;31(4):1978-1986.
31. Zhang Y, Lv X, Qiu J, et al. Deep Learning with 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma. J Magn Reson Imaging. 2021;54(1):134-143.
32. Tan X, Li K, Zhang J, et al. Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study. Cancer Cell Int. 2021;21(1):35.
33. Tao J, Liang C, Yin K, et al. 3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer. Diagn Interv Imaging. 2022;103(11):535-544.
34. Jung Y, Kim T, Han MR, et al. Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder. Sci Rep. 2022;12(1):17024.
35. Chithra PL, Bhavani P. A Novel 3D Multi-Layer Convolutional Neural Networks for Lung Cancer Segmentation in CT Images. Indian J Sci Technol. 2024;17(13):1368-80.
36. Tiwari S, Jain G, Shetty DK, et al. A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging. Eng Proc. 2023;59(1):3.
37. Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3d shape recognition. ICCV.2015;945-953.
38. Wang TW, Hong JS, Huang JW, et al. Systematic review and meta- analysis of deep learning applications in computed tomography lung cancer segmentation. Radiother Oncol. 2024:197:110344.
39. Jin T, Cui H, Zeng S, et al. Learning deep spatial lung features by 3D convolutional neural network for early cancer detection. DICTA. 2017:1-6.
40. Ji S, Yang M, Yu K. 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell. 2013;35(1):221-31.
41. Taddese AA, Tilahun BC, Awoke T, et al. Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis. Front Oncol. 2024;13:1216326.
42. Leung JH, Karmakar R, Mukundan A, et al. Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging. Bioengineering (Basel). 2024;11(11):1060.
43. Alabi RO, Bello IO, Youssef O, et al. Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review. Front Oral Health. 2021;2:686863.
44. Beristain-Colorado MDP, Castro-Gutiérrez MEM, Torres-Rosas R, et al Application of neural networks for the detection of oral cancer: A systematic review. Dent Med Probl. 2024;61(1):121-128.
45. Xu Q, Wang X, Jiang H. Convolutional neural network for breast cancer diagnosis using diffuse optical tomography. Vis Comput Ind Biomed Art. 2019;2(1):1.
46. Wang Y, Zhang H, Wang T, et al. Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging. Sci Rep. 2023;13(1):2770.
47. Ying H, Liu X, Zhang M, et al. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun. 2024;15(1):1131.
48. Jiang X, Hu Z, Wang S, et al. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers (Basel). 2023 Jul 13;15(14):3608.
49. Sadeghi MH, Sina S, Omidi H, et al. Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities. Pol J Radiol. 2024;89:e30-e48.
50. Thakur A, Gupta M, Sinha DK, et al. Transformative breast Cancer diagnosis using CNNs with optimized Reduce L R On Plateau and Early stopping Enhancements. Int J Comput Intell Sys. 2024;17(1):14.
2. Trivizakis E, Manikis GC, Nikiforaki K, et al. Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation. IEEE J Biomed Health Inform. 2019;23(3):923-930.
3. Cui H, Wang KY, Li WY, et al. CT images-based 3D convolutional neural network to predict early recurrence of solitary hepatocellular carcinoma after radical hepatectomy. Diagn Interv Radiol. 2022;28(6):524-531.
4. Muñoz-Aseguinolaza U, Fernandez-Iriondo I, Rodríguez-Moreno I, et al. Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach. Heliyon. 2023;9(11):e21203.
5. Mishra S, Chaudhary NK, Asthana P, et al. Deep 3d convolutional neural network for automated lung cancer diagnosis. In Computing and Network Sustainability: Proceedings of IRSCNS 2018. Springer Singapore, India, 2019, pp 157-165.
6. Ahmed T, Parvin MS, Haque MR, et al. Lung cancer detection using CT image based on 3D convolutional neural network. J Comput Commun. 2020;8(03):35-42.
7. Jafari Z, Karami E. Breast cancer detection in mammography images: a CNN-based approach with feature selection. Inf. 2023;14(7):410.
8. Neal Joshua ES, Bhattacharyya D, Chakkravarthy M, et al. 3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation. J Healthc Eng. 2021;2021:6695518.
9. Yu J, Yang B, Wang J, et al. 2D CNN versus 3D CNN for false- positive reduction in lung cancer screening. J Med Imaging (Bellingham). 2020;7(5):051202.
10. Alotaibi M, Aljouie A, Alluhaidan N, et al. Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images. Heliyon. 2023;9(11):e22406.
11. Zhou J, Luo LY, Dou Q, et al. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. J Magn Reson Imaging. 2019 Oct;50(4):1144-1151.
12. Meng L, Tian Y, Bu S. Liver tumor segmentation based on 3D convolutional neural network with dual scale. J Appl Clin Med Phys. 2020;21(1):144-157.
13. Mzoughi H, Njeh I, Wali A, et al. Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification. J Digit Imaging. 2020;33(4):903-915.
14. Mlynarski P, Delingette H, Criminisi A, et al. 3D convolutional neural networks for tumor segmentation using long-range 2D context. Comput Med Imaging Graph. 2019;73:60-72.
15. Ilesanmi AE, Ilesanmi TO, Ajayi BO. Reviewing 3D convolutional neural network approaches for medical image segmentation. Heliyon. 2024;10(6):e27398.
16. Xu S, Liu C, Zong Y, et al. An early diagnosis of oral cancer based on three-dimensional convolutional neural networks. IEEE Access. 2019;7:158603-11.
17. Huang X, Sun Y, Tan M, et al. Three-dimensional convolutional neural network-based prediction of epidermal growth factor receptor expression status in patients with non-small cell lung cancer. Front Oncol. 2022;12:772770.
18. Perez G, Arbelaez P. Automated lung cancer diagnosis using three-dimensional convolutional neural networks. Med Biol Eng Comput. 2020;58(8):1803-1815.
19. Ding Y, Chen Z, Wang Z, et al. Three-dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images. J Appl Clin Med Phys. 2022;23(4):e13566.
20. Zhou C, Ding C, Wang X, et al. One-pass multi-task networks with cross-task guided attention for brain tumor segmentation. IEEE Trans Image Process. 2020. Online ahead of print.
21. Abinaya K, Sivakumar B . A Deep Learning-Based Approach for Cervical Cancer Classification Using 3D CNN and Vision Transformer. J Imaging Inform Med. 2024;37(1):280-296.
22. Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.
23. https://www.ohri.ca/programs/ clinical_epidemiology/ oxford.asp
24. Hooijmans CR, Rovers MM, De Vries RB, et al. SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol. 2014; 14: 43.
25. Alakwaa W, Nassef M, Badr A. Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). Int. J. Adv. Comput. Sci. Appl.2017;8(8).
26. Zhang Z, Chen L, Wang Y, et al. Label-Free Estimation of Therapeutic Efficacy on 3D Cancer Spheres Using Convolutional Neural Network Image Analysis. Anal Chem. 2019;91(21):14093-14100.
27. Wei R, Zhou F, Liu B, et al. Convolutional neural network (CNN) based three dimensional tumor localization using single X-ray projection. IEEE Access. 2019;7:37026-38.
28. Starke S, Leger S, Zwanenburg A, et al. 2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma. Sci Rep. 2020;10:15625.
29. Camalan S, Mahmood H, Binol H, et al. Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results. Cancers. 2021;13(6):1291.
30. Yanagawa M, Niioka H, Kusumoto M, et al. Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network. Eur Radiol. 2021;31(4):1978-1986.
31. Zhang Y, Lv X, Qiu J, et al. Deep Learning with 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma. J Magn Reson Imaging. 2021;54(1):134-143.
32. Tan X, Li K, Zhang J, et al. Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study. Cancer Cell Int. 2021;21(1):35.
33. Tao J, Liang C, Yin K, et al. 3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer. Diagn Interv Imaging. 2022;103(11):535-544.
34. Jung Y, Kim T, Han MR, et al. Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder. Sci Rep. 2022;12(1):17024.
35. Chithra PL, Bhavani P. A Novel 3D Multi-Layer Convolutional Neural Networks for Lung Cancer Segmentation in CT Images. Indian J Sci Technol. 2024;17(13):1368-80.
36. Tiwari S, Jain G, Shetty DK, et al. A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging. Eng Proc. 2023;59(1):3.
37. Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3d shape recognition. ICCV.2015;945-953.
38. Wang TW, Hong JS, Huang JW, et al. Systematic review and meta- analysis of deep learning applications in computed tomography lung cancer segmentation. Radiother Oncol. 2024:197:110344.
39. Jin T, Cui H, Zeng S, et al. Learning deep spatial lung features by 3D convolutional neural network for early cancer detection. DICTA. 2017:1-6.
40. Ji S, Yang M, Yu K. 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell. 2013;35(1):221-31.
41. Taddese AA, Tilahun BC, Awoke T, et al. Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis. Front Oncol. 2024;13:1216326.
42. Leung JH, Karmakar R, Mukundan A, et al. Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging. Bioengineering (Basel). 2024;11(11):1060.
43. Alabi RO, Bello IO, Youssef O, et al. Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review. Front Oral Health. 2021;2:686863.
44. Beristain-Colorado MDP, Castro-Gutiérrez MEM, Torres-Rosas R, et al Application of neural networks for the detection of oral cancer: A systematic review. Dent Med Probl. 2024;61(1):121-128.
45. Xu Q, Wang X, Jiang H. Convolutional neural network for breast cancer diagnosis using diffuse optical tomography. Vis Comput Ind Biomed Art. 2019;2(1):1.
46. Wang Y, Zhang H, Wang T, et al. Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging. Sci Rep. 2023;13(1):2770.
47. Ying H, Liu X, Zhang M, et al. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun. 2024;15(1):1131.
48. Jiang X, Hu Z, Wang S, et al. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers (Basel). 2023 Jul 13;15(14):3608.
49. Sadeghi MH, Sina S, Omidi H, et al. Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities. Pol J Radiol. 2024;89:e30-e48.
50. Thakur A, Gupta M, Sinha DK, et al. Transformative breast Cancer diagnosis using CNNs with optimized Reduce L R On Plateau and Early stopping Enhancements. Int J Comput Intell Sys. 2024;17(1):14.
| Files | ||
| Issue | Vol 20 No 2 (2026) | |
| Section | Review Article(s) | |
| Keywords | ||
| Three-dimensional convolutional neural networks; Oncology imaging; Cancer diagnostics; Tumor segmentation; Tumor classification | ||
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How to Cite
1.
Jain DS, Afaq S, Sharma S. Role of Three-Dimensional Convolution Neural Networks (3D- CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis. Int J Hematol Oncol Stem Cell Res. 2026;20(2):195-220.
