Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging
Abstract
1. Introduction
2. Imaging Studies for Lung Cancer
2.1. Computed Tomography
2.2. Positron Emission Tomography
3. Lung Cancer Treatment Evaluation Methods
3.1. Criteria to Assess Morphological Changes
3.2. Criteria Based on Metabolic Changes
4. Deep Learning Networks (DLNs)
4.1. Convolutional Neural Network
U-Net
4.2. Recurrent Neural Network
4.3. Recursive Neural Network
4.4. Deep Generative Networks
4.4.1. Variational AutoEncoder
4.4.2. Generative Adversarial Network
4.5. Activation Functions for Neural Networks
4.6. Evaluation Metrics to Assess Deep Learning Models Performance in Image Processing
4.7. Image Preprocessing Techniques
Filters
5. The Potential of Deep Learning in NSCLC Treatment Evaluation
5.1. Underscoring the Role of Deep Learning in Measuring Morphological Changes
Reference | Model Architecture | Dimensional Approach | Evaluation Score |
---|---|---|---|
[82] | CNN (U-Net) | 3D | 86.6% (ACC) |
[83] | Mask R-CNN | 2D | 79.65% (ACC) |
[79] | CNN (U-Net) | 2D | 72%(ACC), 75%(DC), 82%(SEN) |
[84] | CNN (U-Net) | 3D | 82.8%(DC) |
[61] | Mask R-CNN | 2D | 89.96%(ACC), 76.81%(DC), 87.72%(SEN), 86.7%(SPE) |
[85] | CNN (MSDS-UNet ) | 3D | 69.1%(DC), 74.4%(SEN) |
[86] | GANs | 2D | 98.5%(ACC) |
[87] | CNN (U-net) | 3D | 78% (DC) |
[88] | CNN (SquExUNet) | 3D | 80%(DC) |
[89] | CNN (SegNet) | 2D | 92.5%(ACC), 95.11%(DC), 98.33%(SEN), 86.67%(SPE) |
[64] | CNN (ResNet50,U-Net) | 2D | 98.43%(ACC), 98.86%(DC), 98.99%(SEN) |
[90] | CNN (U-Net) | 3D | 82%(DC) |
[91] | CNN (GUNET3++) | 2D | 96% (DC) |
[92] | CNN (SegChaNet) | 3D | 98.48% (DC) |
[93] | CNN (RRc-Unet) | 3D | 87.77% (DC) |
[94] | CNN (Unet) | 2D | 82%(ACC), 62%(DC) |
[95] | CNN (RAD-UNet) | 2D | 88.13%(DC), 92.17%(SEN), 94.75%(SPE) |
[67] | CNN and Transformer | 2D | 92%(DC) |
Reference | Dataset | Image Resolution | Convolutional Layers | Deconvolutional and Max Pooling Layers |
---|---|---|---|---|
[82] | 978 training | 512 × 512 | 4 conv 3 × 3 × 32 | 3 maxpool |
419 validation | 4 conv 3 × 3 × 80 | 3 deconv | ||
4 conv 3 × 3 × 160 | ||||
2 conv 3 × 3 × 320 | ||||
1 conv 3 × 3 × 1 | ||||
[83] | 24,000 training | 512 × 512 | – | — |
8000 validation | ||||
[84] | 3000 training | 512 × 512 | 2 conv 3 × 3 × 32 | 3 maxpool |
2 conv 3 × 3 × 64 | 3 deconv | |||
2 conv 3 × 3 × 128 | ||||
2 conv 3 × 3 × 512 | ||||
1 conv 3 × 3 × 1 | ||||
[79] | 57,793 training | 256 × 256 and | – | – |
160 × 160 | ||||
[61] | 1265 training | 512 × 512 | – | – |
[85] | 15,553 training | 512 × 512 | 1 conv 3 × 3 × 64 | 4 maxpool |
3196 validation | 9 conv 3 × 3 × 1 | 4 deconv | ||
4 conv 3 × 3 × 2 | ||||
1 conv 1 × 1 × 1 | ||||
[86] | 23,400 training | 512 × 512 | – | – |
5200 validation | ||||
[87] | 3072 training | 96 × 96 | 10 conv 3 × 3 | 4 maxpool |
768 validation | 4 conv 3 × 3 × 2 | 4 deconv | ||
1 conv 1 × 1 × 1 | ||||
[88] | 820 training | 512 × 512 | ||
180 validation | ||||
[89] | 7400 training | – | 28 conv | 5 maxpool |
2600 validation | 5 deconv | |||
[91] | 32,606 training | 512 × 512 | – | 4 maxpool |
10 deconv | ||||
[92] | 35,688 training | 128 × 128 | – | 3 maxpool |
1750 validation | 3 deconv | |||
[93] | 395 training | 256 × 256 | – | 4 maxpool |
98 validation | 4 deconv | |||
[94] | 12,000 training | 512 × 512 | – | – |
3000 validation | ||||
[95] | 7725 training | 512 × 512 | 2 conv 3 × 3 × 64 | 4 maxpool |
1931 validation | 2 conv 3 × 3 × 128 | 4 deconv | ||
2 conv 3 × 3 × 256 | ||||
2 conv 3 × 3 × 512 | ||||
2 conv 3 × 3 × 1024 | ||||
[67] | 563 training | 512 × 512 | – | – |
113 validation |
5.2. Underscoring the Role of Deep Learning in Measuring Metabolic Changes
6. Practical Issues for Clinical Deployment of AI Technology
6.1. Regulation and Legislation Initiatives for AI Devices in Radiology
6.2. Validation Protocols
6.3. Challenges and Future Directions, Enabling the Introduction of DL Methods into Current Clinical Practices and Workflows
7. Research on AI Technologies in the Healthcare Sector
- 1.
- Screening, which involves testing to detect cancer in individuals who do not exhibit symptoms.
- 2.
- Detection, which refers to identifying cancerous cells, often through imaging techniques or biomarker tests.
- 3.
- Diagnosis, which confirms the presence of the cancer, and defines the specific type, its stage, and other critical factors to formulate a treatment plan.
- 4.
- Other purposes encompass treatment assistance, such as planning and evaluating the response to treatment using standardized criteria. Prognosis and predictions, such as a mutation, are also contemplated.
Review Content | Authors |
---|---|
The study introduces ML problems and types. It outlines research on DL architectures and processing medical images, including image registration, object localization, classification algorithms, detection, training methods for classification, detection, and segmentation, clustering, dimensionality reduction, and Q-Learning. It settles trends, challenges and future directions in the field. | Suganyadevi et al. [130] |
The paper introduces concepts and basic methods of AI, ML, and DL. It describes the learning types and the AI-based medical imaging analysis workflow. The paper gathers research on feature selection and extraction, models for regression and classification, and their main architectures. It states trends and future lines of research in the field. | Barragán-Montero et al. [131] |
The reviewed image processing techniques and their advances include classification, object detection, segmentation, image generation (using GAN to create realistic synthetic images) to increase classification or segmentation accuracy, or to enable anomaly detection. It discusses open questions and future directions in the field. | Kim et al. [132] |
The paper introduces learning types and DL methods, as well as their issues. The applications are for image registration, anatomical/cell structures detection, tissue segmentation, DL for computer-aided detection (CADe), computer-aided disease diagnosis, and prognosis. It identifies issues and future directions in the field. | Shen et al. [133] |
Review Content | Authors |
---|---|
The survey outlines DL techniques, models, platforms, and resources for image processing and assessment. It explains research on transfer learning and fine-tuning of DL models, types of learning, and data augmentation for training sets. Also, data annotation through mining text reports and active learning. The tasks considered are image preprocessing, lesion segmentation, organ detection, tissue or lesion characterization, and model training. The medical applications involve: Diagnosis, prognosis and staging, measurement, treatment planning, and response assessment. The examined challenges to develop DL methods for medical image evaluation were linked to robustness (limited dataset size, availability of a large number of properly annotated cases, oversizing), repeatability, statistical estimation of performance, and interpretability. | Sahiner et al. [129] |
The review describes advancements in DL models and their varied architectures. It outlines DL applications in healthcare and their optimization for segmentation and classification of images. It addresses topics such as data privacy, legal use, and standards. | Razzak et al. [134] |
Review Content | Authors |
---|---|
The research questions of the review address the following issues related to lung cancer detection by chest radiographs and CTs: | Nguyen [135] |
| |
A comparison of studies for lung cancer detection was given, based on CT and CR, with existing surveys with respect to transferring data (feature extraction). The review considered CNN architectures for 2D and 2D-3D analyses. Comparison of DL models describing their architecture, and distinguishing between application (including mainly detection, diagnosis, early stage detection, classification, prediction of mutation ), database, pros and cons, performance metrics, mainly using ACC: Accuracy, AUC: Area Under the Curve, SPC: Specificity, SEN: Sensitivity or Recall, PPV: Precision or Positive Predictive Value, CPM: competition performance metric. Discussed databases and future directions. | |
A systematic review methodology solves the research question on the relevance of lung cancer, with a focus on the diagnosis based on DL learning. It analyzes DL tools, classification algorithms, CNN architectures, datasets, evaluation criteria, and accuracy estimates of the performance for the resulting developments. Finally, it identifies universities active in the field. | Hosseini et al. [136] |
The review provides a description and classification of lung cancer. It characterizes cancer therapies, cancer progression in stages, and analysis of lung cancer. The focus is on Imaging techniques for lung cancer detection. The work is presented as a systematic literature review with the following research concerns: | Javed et al. [137] |
| |
An emphasis is placed on the literature sources. Their quality assessment was scored based on the proposed solution, contribution, future work, and results. Further information is related to imaging techniques, datasets, model architectures, feature extraction techniques, and image preprocessing methods. The survey identifies the DL methodologies for lung cancer detection and the evaluation performance criteria consistently used. | |
A systematic review and meta-analysis with a research question concerning the lung cancer diagnostic performance of DL algorithms, based on specific criteria, such as pooled sensitivity and specificity. | Forte et al. [138] |
Review Content | Authors |
---|---|
The review describes imaging techniques for screening and diagnosing lung cancer, as well as DL-based techniques to aid these tasks. The research outlines applications, the DL approach, and its advantages and disadvantages. The focus is on classification and segmentation methodologies. A discussion is provided on gaps, challenges, limitations, and future directions. | Thanoon et al. [139] |
The survey presents imaging techniques for lung cancer and pulmonary nodule detection. It studies DL-based techniques, DNN architectures, and performance metrics for DL algorithms, mainly with CNNs. The study outlines advances in image segmentation, nodule detection, and classification approaches for CT images. Il also discusses challenges and future research on DL techniques. | Wang [11] |
8. Discussion
9. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Xu, Y.; Hosny, A.; Zeleznik, R.; Parmar, C.; Coroller, T.; Franco, I.; Mak, R.H.; Aerts, H.J. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. Clin. Cancer Res. 2019, 25, 3266–3275. [Google Scholar] [CrossRef] [PubMed]
- Ferlay, J.; Ervik, M.; Lam, F.; Laversanne, M.; Colombet, M.; Mery, L.; Piñeros, M.; Znaor, A.; Soerjomataram, I.; Bray, F. Global Cancer Observatory: Cancer Today; International Agency for Research on Cancer: Lyon, France, 2024. [Google Scholar]
- Bianconi, F.; Palumbo, I.; Spanu, A.; Nuvoli, S.; Fravolini, M.L.; Palumbo, B. PET/CT Radiomics in Lung Cancer: An Overview. Appl. Sci. 2020, 10, 1718. [Google Scholar] [CrossRef]
- Shang, J.; Ling, X.; Zhang, L.; Tang, Y.; Xiao, Z.; Cheng, Y.; Guo, B.; Gong, J.; Huang, L.; Xu, H. Comparison of RECIST, EORTC criteria and PERCIST for evaluation of early response to chemotherapy in patients with non-small-cell lung cancer. Eur. J. Nucl. Med. Mol. Imaging 2016, 43, 1945–1953. [Google Scholar] [CrossRef] [PubMed]
- Lambin, P.; Leijenaar, R.T.; Deist, T.M.; Peerlings, J.; de Jongand Janita van Timmeren, E.E.; Sanduleanu, S.; Larue, R.T.; Even, A.J.; Jochems, A.; van Wijk, Y.; et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017, 14, 749–762. [Google Scholar] [CrossRef]
- Ambrosini, V.; Nicolini, S.; Caroli, P.; Nanni, C.; Massaro, A.; Marzola, M.C.; Rubello, D.; Fanti, S. PET/CT imaging in different types of lung cancer: An overview. Eur. J. Radiol. 2012, 81, 988–1001. [Google Scholar] [CrossRef]
- Shukla, S.; Malhotra, K.; Husain, N.; Gupta, A.; Anand, N. The utility of cytology in the diagnosis of adenocarcinoma lung: A tertiary care center study. J. Cytol. 2015, 32, 159. [Google Scholar] [CrossRef]
- Nooreldeen, R.; Bach, H. Current and Future Development in Lung Cancer Diagnosis. Int. J. Mol. Sci. 2021, 22, 8661. [Google Scholar] [CrossRef]
- Ranu, H.; Wilde, M.; Madden, B. Pulmonary Function Tests. Ulst. Med. J. 2011, 80, 84–90. [Google Scholar]
- Greillier, L.; Thomas, P.; Loundou, A.; Doddoli, C.; Badier, M.; Auquier, P.; Barlési, F. Pulmonary Function Tests as a Predictor of Quantitative and Qualitative Outcomes After Thoracic Surgery for Lung Cancer. Clin. Lung Cancer 2007, 8, 554–561. [Google Scholar] [CrossRef]
- Wang, L. Deep Learning Techniques to Diagnose Lung Cancer. Cancers 2022, 14, 5569. [Google Scholar] [CrossRef]
- Moon, J.W.; Yang, E.; Kim, J.H.; Kwon, O.J.; Park, M.; Yi, C.A. Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI. Diagnostics 2023, 13, 2555. [Google Scholar] [CrossRef] [PubMed]
- Fan, X.; Zhang, X.; Zhang, Z.; Jiang, Y. Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis. Contrast Media Mol. Imaging 2021, 2021, 1–9. [Google Scholar] [CrossRef]
- Shimazaki, A.; Ueda, D.; Choppin, A.; Yamamoto, A.; Honjo, T.; Shimahara, Y.; Miki, Y. Deep learning-based algorithm for lung cancer detection on chest radiographs using the segmentation method. Sci. Rep. 2022, 12, 727. [Google Scholar] [CrossRef]
- Lu, M.T.; Raghu, V.K.; Mayrhofer, T.; Aerts, H.J.; Hoffmann, U. Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model. Ann. Intern. Med. 2020, 173, 704–713. [Google Scholar] [CrossRef]
- Rajasekar, V.; Vaishnnave, M.; Premkumar, S.; Sarveshwaran, V.; Rangaraaj, V. Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques. Results Eng. 2023, 18, 101111. [Google Scholar] [CrossRef]
- Liu, L.; Li, C. Comparative study of deep learning models on the images of biopsy specimens for diagnosis of lung cancer treatment. J. Radiat. Res. Appl. Sci. 2023, 16, 100555. [Google Scholar] [CrossRef]
- Li, Z.; Zhang, J.; Tan, T.; Teng, X.; Sun, X.; Zhao, H.; Liu, L.; Xiao, Y.; Lee, B.; Li, Y.; et al. Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019. IEEE J. Biomed. Health Informatics 2021, 25, 429–440. [Google Scholar] [CrossRef]
- Hatuwal, B.K.; Thapa, H.C. Lung Cancer Detection Using Convolutional Neural Network on Histopathological Images. Int. J. Comput. Trends Technol. 2020, 68, 21–24. [Google Scholar] [CrossRef]
- Zhong, Y.; Cai, C.; Chen, T.; Gui, H.; Deng, J.; Yang, M.; Yu, B.; Song, Y.; Wang, T.; Sun, X.; et al. PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer. Nat Commun 2023, 14, 7513. [Google Scholar] [CrossRef]
- Calzado, A.; Geleijns, J. Tomografía computarizada. Evolución, principios técnicos y aplicaciones. Rev. FíSica MéDica 2010, 11, 163–180. [Google Scholar]
- Yanza, E.G.G.; Mora, M.C.M.; Mora, M.R.R.; Avilés, M.E.I. Avances en Tomografía por Emisión de Positrones (PET) y Tomografía Computarizada (CT): Aplicaciones clínicas y futuras perspectivas en imagenología médica. Reciamuc 2024, 8, 826–835. [Google Scholar] [CrossRef]
- Werner-Wasik, M.; Xiao, Y.; Pequignot, E.; Curran, W.J.; Hauck, W. Assessment of lung cancer response after nonoperative therapy: Tumor diameter, bidimensional product, and volume. A serial ct scan-based study. Int. J. Radiat. Oncol. Biol. Phys. 2001, 51, 56–61. [Google Scholar] [CrossRef] [PubMed]
- Park, J.O. Measuring Response in Solid Tumors: Comparison of RECIST and WHO Response Criteria. Jpn. J. Clin. Oncol. 2003, 33, 533–537. [Google Scholar] [CrossRef]
- Zhao, B.; Tan, Y.; Bell, D.J.; Marley, S.E.; Guo, P.; Mann, H.; Scott, M.L.; Schwartz, L.H.; Ghiorghiu, D.C. Exploring intra- and inter-reader variability in uni-dimensional, bi-dimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals. Eur. J. Radiol. 2013, 82, 959–968. [Google Scholar] [CrossRef]
- Petrick, N.; Kim, H.J.G.; Clunie, D.; Borradaile, K.; Ford, R.; Zeng, R.; Gavrielides, M.A.; McNitt-Gray, M.F.; Lu, Z.Q.J.; Fenimore, C.; et al. Comparison of 1D, 2D, and 3D Nodule Sizing Methods by Radiologists for Spherical and Complex Nodules on Thoracic CT Phantom Images. Acad. Radiol. 2014, 21, 30–40. [Google Scholar] [CrossRef]
- Hayes, S.; Pietanza, M.; O’Driscoll, D.; Zheng, J.; Moskowitz, C.; Kris, M.; Ginsberg, M. Comparison of CT volumetric measurement with RECIST response in patients with lung cancer. Eur. J. Radiol. 2016, 85, 524–533. [Google Scholar] [CrossRef]
- Litiére, S.; Isaac, G.; GE, E.D.V.; Bogaerts, J.; Chen, A.; Dancey, J.; Ford, R.; Gwyther, S.; Hoekstra, O.; Huang, E.; et al. RECIST 1.1 for response evaluation apply not only to chemotherapy-treated patients but also to targeted cancer agents: A pooled database analysis. J. Clin. Oncol. 2019, 37, 1102–1110. [Google Scholar] [CrossRef]
- Deval, J.C. RECIST y el radiólogo. Radiología 2014, 56, 193–205. [Google Scholar] [CrossRef]
- Eisenhauer, E.; Therasse, P.; Bogaerts, J.; Schwartz, L.; Sargent, D.; Ford, R.; Dancey, J.; Arbuck, S.; Gwyther, S.; Mooney, M.; et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer 2009, 45, 228–247. [Google Scholar] [CrossRef]
- Kim, J.H. Comparison of the EORTC criteria and PERCIST in solid tumors: A pooled analysis and review. Oncotarget 2016, 7, 58105–58110. [Google Scholar] [CrossRef]
- Skougaard, K.; Nielsen, D.; Jensen, B.V.; Hendel, H.W. Comparison of EORTC Criteria and PERCIST for PET/CT Response Evaluation of Patients with Metastatic Colorectal Cancer Treated with Irinotecan and Cetuximab. J. Nucl. Med. 2013, 54, 1026–1031. [Google Scholar] [CrossRef] [PubMed]
- Shahid, N.; Rappon, T.; Berta, W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS ONE 2019, 14, e0212356. [Google Scholar] [CrossRef]
- LeCun, Y.; Boser, B.; Denker, J.S.; Henderson, D.; Howard, R.E.; Hubbard, W.; Jackel, L.D. Backpropagation Applied to Handwritten Zip Code Recognition. Neural Comput. 1989, 1, 541–551. [Google Scholar] [CrossRef]
- Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25–29 October 2014. [Google Scholar] [CrossRef]
- Goller, C.; Kuchler, A. Learning task-dependent distributed representations by backpropagation through structure. In Proceedings of the International Conference on Neural Networks (ICNN’96), Washington, DC, USA, 3–6 June 1996; Volume 1, pp. 347–352. [Google Scholar] [CrossRef]
- Hinton, G. Deep belief networks. Scholarpedia 2009, 4, 5947. [Google Scholar] [CrossRef]
- Salakhutdinov, R.; Hinton, G. Deep Boltzmann Machines. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, Clearwater Beach, FL, USA, 16–18 April 2009; Volume 5, pp. 448–455. [Google Scholar]
- Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Ozair, S.; Courville, A.; Bengio, Y. Generative Adversarial Nets. In Advances in Neural Information Processing Systems; Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2014; Volume 27. [Google Scholar]
- Kingma, D.P. Auto-encoding variational bayes. arXiv 2013, arXiv:1312.6114. [Google Scholar] [CrossRef]
- Singh, A.; Ogunfunmi, T. An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications. Entropy 2021, 24, 55. [Google Scholar] [CrossRef]
- Esqueda Elizondo, J.J.; Palafox, L. Fundamentos de Procesamiento de Imágenes; Universidad Autónoma de Baja California: Mexicali, Baja California, Mexico, 2005. [Google Scholar]
- Manakitsa, N.; Maraslidis, G.S.; Moysis, L.; Fragulis, G.F. A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision. Technologies 2024, 12, 15. [Google Scholar] [CrossRef]
- Ronneberger, O.; Fischer, P.; Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Munich, Germany, 5–9 October 2015; Springer: Cham, Switzerland, 2015; pp. 234–241. [Google Scholar] [CrossRef]
- Abdulfatah, A.; Sheng, Z.; Tenawerk, Y.E. U-Net-Based Medical Image Segmentation: A Comprehensive Analysis and Performance Review. J. Electron. Res. Appl. 2025, 9, 202–208. [Google Scholar] [CrossRef]
- Yu, J.; de Antonio, A.; Villalba-Mora, E. Deep Learning (CNN, RNN) Applications for Smart Homes: A Systematic Review. Computers 2022, 11, 26. [Google Scholar] [CrossRef]
- Rajaram, S.; Gupta, P.; Andrassy, B.; Runkler, T. Neural Architectures for Relation Extraction Within and Across Sentence Boundaries in Natural Language Text. Master’s Thesis, Technische Universität München, München, Germany, 2018. [Google Scholar]
- Pouyanfar, S.; Sadiq, S.; Yan, Y.; Tian, H.; Tao, Y.; Shyu, M.P.R.M.L.; Chen, S.C.; Iyengar, S.S. A Survey on Deep Learning: Algorithms, Techniques, and Applications. ACM J. 2018, 51, 1–36. [Google Scholar] [CrossRef]
- Hosseini, M.P.; Lu, S.; Kamaraj, K.; Slowikowski, A.; Venkatesh, H.C. Deep Learning Architectures. In Deep Learning: Concepts and Architectures; Series Title: Studies in Computational Intelligence; Pedrycz, W., Chen, S.M., Eds.; Springer International Publishing: Cham, Switzerland, 2020; Volume 866, pp. 1–24. [Google Scholar] [CrossRef]
- Cemgil, T.; Ghaisas, S.; Dvijotham, K.; Gowal, S.; Kohli, P. The Autoencoding Variational Autoencoder. In Advances in Neural Information Processing Systems; Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2020; Volume 33, pp. 15077–15087. [Google Scholar]
- Berahmand, K.; Daneshfar, F.; Salehi, E.S.; Li, Y.; Xu, Y. Autoencoders and their applications in machine learning: A survey. Artif. Intell. Rev. 2024, 57, 28. [Google Scholar] [CrossRef]
- Aggarwal, A.; Mittal, M.; Battineni, G. Generative adversarial network: An overview of theory and applications. Int. J. Inf. Manag. Data Insights 2021, 1, 100004. [Google Scholar] [CrossRef]
- Sharma, S.; Sharma, S.; Athaiya, A. Activation functions in neural networks. Int. J. Eng. Appl. Sci. Technol. 2020, 4, 310–316. [Google Scholar] [CrossRef]
- Szandała, T. Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks. In Studies in Computational Intelligence, Bio-Inspired Neurocomputing; Springer: Singapore, 2020; pp. 203–224. [Google Scholar] [CrossRef]
- Rasamoelina, A.D.; Adjailia, F.; Sincak, P. A Review of Activation Function for Artificial Neural Network. In Proceedings of the 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Herlany, Slovakia, 23–25 January 2020. [Google Scholar] [CrossRef]
- Bingham, G.; Miikkulainen, R. Discovering Parametric Activation Functions. Neural Netw. 2022, 148, 48–65. [Google Scholar] [CrossRef]
- Glorot, X.; Bordes, A.; Bengio, Y. Deep Sparse Rectifier Neural Networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA, 11–13 April 2011; Gordon, G., Dunson, D., Dudík, M., Eds.; Volume 15, pp. 315–323. [Google Scholar]
- Clevert, D.A.; Unterthiner, T.; Hochreiter, S. Fast and accurate deep network learning by exponential linear units (ELUs). arXiv 2015, arXiv:1511.07289. [Google Scholar] [CrossRef]
- Müller, D.; Soto-Rey, I.; Kramer, F. Towards a guideline for evaluation metrics in medical image segmentation. BMC Res. Notes 2022, 15, 210. [Google Scholar] [CrossRef]
- Sánchez-Jiménez, E.; Hernández, Y.; Ortiz-Hernández, J.; Martínez-Rebollar, A.; Estrada-Esquivel, H. Configuración de hiperparámetros mediante algoritmos de optimización: Aplicación en la predicción de enfermedades cardiovasculares. Res. Comput. Sci. 2023, 152, 141–155. [Google Scholar]
- Hu, Q.; de F. Souza, L.F.; Holanda, G.B.; Alves, S.S.; dos S. Silva, F.H.; Han, T.; Filho, P.P.R. An effective approach for CT lung segmentation using mask region-based convolutional neural networks. Artif. Intell. Med. 2020, 103, 101792. [Google Scholar] [CrossRef]
- Makandar, A.; Halalli, B. Image Enhancement Techniques using Highpass and Lowpass Filters. Int. J. Comput. Appl. 2015, 109, 21–27. [Google Scholar] [CrossRef]
- Kumar, S.; Singh, M.; Shaw, D. Comparative Analysis of Various Edge Detection Techniques in Biometric Application. Int. J. Eng. Technol. 2016, 8, 2452–2459. [Google Scholar] [CrossRef]
- Salama, W.M.; Aly, M.H.; Elbagoury, A.M. Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach. J. Physics Conf. Ser. 2021, 2128, 012011. [Google Scholar] [CrossRef]
- Weyts, K.; Lequesne, J.; Johnson, A.; Curcio, H.; Parzy, A.; Coquan, E.; Lasnon, C. The impact of introducing deep learning based [18F]FDG PET denoising on EORTC and PERCIST therapeutic response assessments in digital PET/CT. EJNMMI Res. 2024, 14, 72. [Google Scholar] [CrossRef]
- Li, B.; Su, J.; Liu, K.; Hu, C. Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer. Eur. J. Radiol. Open 2024, 12, 100549. [Google Scholar] [CrossRef] [PubMed]
- Kunkyab, T.; Bahrami, Z.; Zhang, H.; Liu, Z.; Hyde, D. A deep learning-based framework (Co-ReTr) for auto-segmentation of non-small cell-lung cancer in computed tomography images. J. Appl. Clin. Med. Phys. 2024, 25, e14297. [Google Scholar] [CrossRef]
- Früh, M.; Fischer, M.; Schilling, A.; Gatidis, S.; Hepp, T. Weakly supervised segmentation of tumor lesions in PET-CT hybrid imaging. J. Med. Imaging 2021, 8, 054003. [Google Scholar] [CrossRef]
- Chang, R.; Qi, S.; Wu, Y.; Song, Q.; Yue, Y.; Zhang, X.; Guan, Y.; Qian, W. Deep multiple instance learning for predicting chemotherapy response in non-small cell lung cancer using pretreatment CT images. Sci. Rep. 2022, 12, 19829. [Google Scholar] [CrossRef]
- Javed, S.A.; Juyal, D.; Padigela, H.; Taylor-Weiner, A.; Yu, L.; Prakash, A. Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology. In Advances in Neural Information Processing Systems; Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A., Eds.; Curran Associates, Inc.: Red Hook, NY, USA, 2022; Volume 35, pp. 20689–20702. [Google Scholar]
- Waqas, M.; Ahmed, S.U.; Tahir, M.A.; Wu, J.; Qureshi, R. Exploring Multiple Instance Learning (MIL): A brief survey. Expert Syst. Appl. 2024, 250, 123893. [Google Scholar] [CrossRef]
- Ilse, M.; Tomczak, J.; Welling, M. Attention-based Deep Multiple Instance Learning. In Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, 10–15 July 2018; Dy, J., Krause, A., Eds.; Volume 80, pp. 2127–2136. [Google Scholar]
- Quellec, G.; Cazuguel, G.; Cochener, B.; Lamard, M. Multiple-Instance Learning for Medical Image and Video Analysis. IEEE Rev. Biomed. Eng. 2017, 10, 213–234. [Google Scholar] [CrossRef]
- Holliday, A.; Dudek, G. Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation. In Proceedings of the 2020 17th Conference on Computer and Robot Vision (CRV), Ottawa, ON, Canada, 13–15 May 2020; pp. 78–84. [Google Scholar] [CrossRef]
- Arbour, K.C.; Luu, A.T.; Luo, J.; Rizvi, H.; Plodkowski, A.J.; Sakhi, M.; Huang, K.B.; Digumarthy, S.R.; Ginsberg, M.S.; Girshman, J.; et al. Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade. Cancer Discov. 2021, 11, 59–67. [Google Scholar] [CrossRef]
- Tang, Y.; Harrison, A.P.; Bagheri, M.; Xiao, J.; Summers, R.M. Semi-automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks. In Medical Image Computing and Computer Assisted Intervention—MICCAI 2018, Granada, Spain, 16–20 September 2018. Lecture Notes in Computer Science; Springer: Cham, Switzerland, 2018; Volume 11073, pp. 405–413. [Google Scholar] [CrossRef]
- Xie, C.; Cao, S.; Wei, D.; Zhou, H.; Ma, K.; Zhang, X.; Qian, B.; Wang, L.; Zheng, Y. Recist-Net: Lesion Detection Via Grouping Keypoints On Recist-Based Annotation. In Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 13–16 April 2021; pp. 921–924. [Google Scholar] [CrossRef]
- Woo, M.; Devane, A.M.; Lowe, S.C.; Lowther, E.L.; Gimbel, R.W. Deep learning for semi-automated unidirectional measurement of lung tumor size in CT. Cancer Imaging 2021, 21, 43. [Google Scholar] [CrossRef] [PubMed]
- Jiang, J.; Hu, Y.C.; Liu, C.J.; Halpenny, D.; Hellmann, M.D.; Deasy, J.O.; Mageras, G.; Veeraraghavan, H. Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images. IEEE Trans. Med Imaging 2019, 38, 134–144. [Google Scholar] [CrossRef] [PubMed]
- Chen, W.; Yang, F.; Zhang, X.; Xu, X.; Qiao, X. MAU-Net: Multiple Attention 3D U-Net for Lung Cancer Segmentation on CT Images. Procedia Comput. Sci. 2021, 192, 543–552. [Google Scholar] [CrossRef]
- Kidd, A.C.; Anderson, O.; Cowell, G.W.; Weir, A.J.; Voisey, J.P.; Evison, M.; Tsim, S.; Goatman, K.A.; Blyth, K.G. Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: Validation and comparison with modified RECIST response criteria. Thorax 2022, 77, 1251–1259. [Google Scholar] [CrossRef] [PubMed]
- 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, 409–417. [Google Scholar] [CrossRef]
- Liu, M.; Dong, J.; Dong, X.; Yu, H.; Qi, L. Segmentation of Lung Nodule in CT Images Based on Mask R-CNN. In Proceedings of the 2018 9th International Conference on Awareness Science and Technology (iCAST), Fukuoka, Japan, 19–21 September 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Baek, S.; He1, Y.; Allen, B.G.; Buatti, J.M.; Smith, B.J.; Tong, L.; Sun, Z.; Wu, J.; Diehn, M.; Loo, B.W.; et al. Deep segmentation networks predict survival of non-small cell lung cancer. Sci. Rep. 2019, 9, 17286. [Google Scholar] [CrossRef]
- Yang, J.; Wu, B.; Li, L.; Cao, P.; Zaiane, O. MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT. Comput. Med Imaging Graph. 2021, 92, 101957. [Google Scholar] [CrossRef]
- Tan, J.; Jing, L.; Huo, Y.; Li, L.; Akin, O.; Tian, Y. LGAN: Lung segmentation in CT scans using generative adversarial network. Comput. Med Imaging Graph. 2021, 87, 101817. [Google Scholar] [CrossRef]
- Gainey, J.; He, Y.; Zhu, R.; Kim, Y. The Predictive Power of a Deep-Learning Segmentation Based Prognostication (DESEP) Model in Non-Small Cell Lung Cancer. Int. J. Radiat. Oncol. 2021, 111, e111. [Google Scholar] [CrossRef]
- Dutande, P.; Baid, U.; Talbar, S. LNCDS: A 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation. Biomed. Signal Process. Control 2021, 67, 102527. [Google Scholar] [CrossRef]
- Chen, X.; Duan, Q.; Wu, R.; Yang, Z. Segmentation of lung computed tomography images based on SegNet in the diagnosis of lung cancer. J. Radiat. Res. Appl. Sci. 2021, 14, 396–403. [Google Scholar] [CrossRef]
- Primakov, S.P.; Ibrahim, A.; van Timmeren, J.E.; Wu, G.; Keek, S.A.; Beuque, M.; Granzier, R.W.; Lavrova, E.; Scrivener, M.; Sanduleanu, S.; et al. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nat. Commun. 2022, 13, 3423. [Google Scholar] [CrossRef] [PubMed]
- Aversano, L.; Bernardi, M.L.; Cimitile, M.; Iammarino, M.; Verdone, C. An enhanced UNet variant for Effective Lung Cancer Detection. In Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 18–23 July 2022; Volume 2013, pp. 1–8. [Google Scholar] [CrossRef]
- Cifci, M.A. SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans. Appl. Bionics Biomech. 2022, 2022, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Le, V.L.; Saut, O. RRc-UNet 3D for lung tumor segmentation from CT scans of Non-Small Cell Lung Cancer patients. In Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2–6 October 2023; Volume 47, pp. 2308–2317. [Google Scholar] [CrossRef]
- Akash, V.; Monish, S.R.; Amith, K.B.; Jayanthi, M.G.; Kannadaguli, P. Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep Learning. In Proceedings of the 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), Bengaluru, India, 1–2 September 2023; Volume 15, pp. 1–6. [Google Scholar] [CrossRef]
- Wu, Z.; Li, X.; Zuo, J. RAD-UNet: Research on an improved lung nodule semantic segmentation algorithm based on deep learning. Front. Oncol. 2023, 13, 1084096. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Faes, L.; Kale, A.U.; Wagner, S.K.; Fu, D.J.; Bruynseels, A.; Mahendiran, T.; Moraes, G.; Shamdas, M.; Kern, C.; et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digit. Health 2019, 1, e271–e297. [Google Scholar] [CrossRef]
- Chen, L.; Liu, K.; Shen, H.; Ye, H.; Liu, H.; Yu, L.; Li, J.; Zhao, K.; Zhu, W. Multimodality Attention-Guided 3-D Detection of Nonsmall Cell Lung Cancer in 18F-FDG PET/CT Images. IEEE Trans. Radiat. Plasma Med Sci. 2022, 6, 421–432. [Google Scholar] [CrossRef]
- Coleman, R.E. PET in Lung Cancer. J. Nucl. Med. 1999, 40, 814–820. [Google Scholar]
- Bunyaviroch, T.; Coleman, R.E. PET Evaluation of Lung Cancer. J. Nucl. Med. 2006, 47, 451–469. [Google Scholar]
- Zhang, R.; Cheng, C.; Zhao, X.; Li, X. Multiscale Mask R-CNN–Based Lung Tumor Detection Using PET Imaging. Mol. Imaging 2019, 18, 153601211986353. [Google Scholar] [CrossRef]
- He, K.; Gkioxari, G.; Dollár, P.; Girshick, R. Mask R-CNN. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22–29 October 2017; pp. 2980–2988. [Google Scholar] [CrossRef]
- Protonotarios, N.E.; Katsamenis, I.; Sykiotis, S.; Dikaios, N.; Kastis, G.A.; Chatziioannou, S.N.; Metaxas, M.; Doulamis, N.; Doulamis, A. A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging. Biomed. Phys. Eng. Express 2022, 8, 025019. [Google Scholar] [CrossRef]
- Theophraste, H.; Meyer, C.; Chevance, V.; Roblot, V.; Blanchet, E.; Arnould, V.; Grimon, G.; Chekroun, M.; Parent, F.; Seferian, A.; et al. Automated PET segmentation for lung tumors: Can deep learning reach optimized expert-based performance? J. Nucl. Med. 2018, 59, 322. [Google Scholar]
- Zhao, X.; Li, L.; Lu, W.; Tan, S. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Phys. Med. Biol. 2018, 64, 015011. [Google Scholar] [CrossRef] [PubMed]
- Leung, K.H.; Marashdeh, W.; Wray, R.; Ashrafinia, S.; Pomper, M.G.; Rahmim, A.; Jha, A.K. A physics-guided modular deep-learning based automated framework for tumor segmentation in PET. Phys. Med. Biol. 2020, 65, 245032. [Google Scholar] [CrossRef] [PubMed]
- Park, J.; Kang, S.K.; Hwang, D.; Choi, H.; Ha, S.; Seo, J.M.; Eo, J.S.; Lee, J.S. Automatic Lung Cancer Segmentation in [18F]FDG PET/CT Using a Two-Stage Deep Learning Approach. Nucl. Med. Mol. Imaging 2022, 57, 86–93. [Google Scholar] [CrossRef] [PubMed]
- Lei, Y.; Wang, T.; Jeong, J.J.; Janopaul-Naylor, J.; Kesarwala, A.H.; Roper, J.; Tian, S.; Bradley, J.D.; Liu, T.; Higgins, K.; et al. Automated lung tumor delineation on positron emission tomography/computed tomography via a hybrid regional network. Med. Phys. 2022, 50, 274–283. [Google Scholar] [CrossRef]
- Xia, X.; Zhang, R. A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model. IEEE Access 2023, 11, 34015–34031. [Google Scholar] [CrossRef]
- Yu, X.; He, L.; Wang, Y.; Dong, Y.; Song, Y.; Yuan, Z.; Yan, Z.; Wang, W. A deep learning approach for automatic tumor delineation in stereotactic radiotherapy for non-small cell lung cancer using diagnostic PET-CT and planning CT. Front. Oncol. 2023, 13, 1235461. [Google Scholar] [CrossRef]
- Amyar, A.; Modzelewski, R.; Vera, P.; Morard, V.; Ruan, S. Multi-task multi-scale learning for outcome prediction in 3D PET images. Comput. Biol. Med. 2022, 151, 106208. [Google Scholar] [CrossRef]
- Jaudet, C.; Weyts, K.; Lechervy, A.; Batalla, A.; Bardet, S.; Corroyer-Dulmont, A. The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics. Front. Oncol. 2021, 11, 1–9. [Google Scholar] [CrossRef]
- Weyts, K.; Lasnon, C.; Ciappuccini, R.; Lequesne, J.; Corroyer-Dulmont, A.; Quak, E.; Clarisse, B.; Roussel, L.; Bardet, S.; Jaudet, C. Artificial intelligence-based PET denoising could allow a two-fold reduction in [18F]FDG PET acquisition time in digital PET/CT. Eur. J. Nucl. Med. Mol. Imaging 2022, 49, 3750–3760. [Google Scholar] [CrossRef]
- Pesapane, F.; Volonté, C.; Codari, M.; Sardanelli, F. Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights Imaging 2018, 9, 745–753. [Google Scholar] [CrossRef]
- Van Kolfschooten, H.; Van Oirschot, J. The EU Artificial Intelligence Act (2024): Implications for healthcare. Health Policy 2024, 149, 105152. [Google Scholar] [CrossRef] [PubMed]
- Braun, M.; Vallery, A.; Benizri, I. Measures in Support of Innovation in the European Union’s AI Act—AI Regulatory Sandboxes. 2024. Available online: https://www.wilmerhale.com/en/insights/blogs/wilmerhale-privacy-and-cybersecurity-law/20241023-measures-in-support-of-innovation-in-the-european-unions-ai-act-ai-regulatory-sandboxes (accessed on 10 January 2025).
- EU Artificial Intelligence Act. Article 59: Further Processing of Personal Data for Developing Certain AI Systems in the Public Interest in the AI Regulatory Sandbox, 2024. Available online: https://artificialintelligenceact.eu/article/59/ (accessed on 10 January 2025).
- EUR-Lex. Regulation - EU - 2024/1689, 2024. Doc ID: 32024R1689 Doc Sector: 3 Doc Title: Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance) Doc Type: R Usr_lan: En. Available online: https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng (accessed on 10 January 2025).
- Pesapane, F.; Hauglid, M.K.; Fumagalli, M.; Petersson, L.; Parkar, A.P.; Cassano, E.; Horgan, D. The translation of in-house imaging AI research into a medical device ensuring ethical and regulatory integrity. Eur. J. Radiol. 2025, 182, 111852. [Google Scholar] [CrossRef] [PubMed]
- European Parliament. Panel for the Future of Science and Technology (STOA). Available online: https://www.europarl.europa.eu/stoa/en/home/highlights (accessed on 10 January 2025).
- Miotto, R.; Wang, F.; Wang, S.; Jiang, X.; Dudley, J.T. Deep learning for healthcare: Review, opportunities and challenges. Briefings Bioinform. 2018, 19, 1236–1246. [Google Scholar] [CrossRef]
- Sousa Santos, C.; Amorim-Lopes, M. Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: A scoping review. BMC Med Res. Methodol. 2025, 25, 45. [Google Scholar] [CrossRef]
- Elhaddad, M.; Hamam, S. AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential. Cureus 2024, 16, e57728. [Google Scholar] [CrossRef]
- Giger, M.L. Machine Learning in Medical Imaging. J. Am. Coll. Radiol. 2018, 15, 512–520. [Google Scholar] [CrossRef]
- Magrabi, F.; Ammenwerth, E.; McNair, J.B.; De Keizer, N.F.; Hyppönen, H.; Nykänen, P.; Rigby, M.; Scott, P.J.; Vehko, T.; Wong, Z.S.Y.; et al. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications: A Position Paper from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group and the EFMI Working Group for Assessment of Health Information Systems. Yearb. Med. Inform. 2019, 28, 128–134. [Google Scholar] [CrossRef]
- Jones, C.; Thornton, J.; Wyatt, J.C. Artificial intelligence and clinical decision support: Clinicians’ perspectives on trust, trustworthiness, and liability. Med. Law Rev. 2023, 31, 501–520. [Google Scholar] [CrossRef]
- Tajidini, F. A comprehensive review of deep learning in lung cancer, 2023. Version Number: 1. arXiv 2023, arXiv:2308.02528. [Google Scholar] [CrossRef]
- Zhou, S.K.; Greenspan, H.; Davatzikos, C.; Duncan, J.S.; Van Ginneken, B.; Madabhushi, A.; Prince, J.L.; Rueckert, D.; Summers, R.M. A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies with Progress Highlights, and Future Promises. Proc. IEEE 2021, 109, 820–838. [Google Scholar] [CrossRef]
- Litjens, G.; Kooi, T.; Bejnordi, B.E.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; Van Der Laak, J.A.; Van Ginneken, B.; Sánchez, C.I. A survey on deep learning in medical image analysis. Med. Image Anal. 2017, 42, 60–88. [Google Scholar] [CrossRef] [PubMed]
- Sahiner, B.; Pezeshk, A.; Hadjiiski, L.M.; Wang, X.; Drukker, K.; Cha, K.H.; Summers, R.M.; Giger, M.L. Deep learning in medical imaging and radiation therapy. Med. Phys. 2019, 46, e1–e36. [Google Scholar] [CrossRef]
- Suganyadevi, S.; Seethalakshmi, V.; Balasamy, K. A review on deep learning in medical image analysis. Int. J. Multimed. Inf. Retr. 2022, 11, 19–38. [Google Scholar] [CrossRef] [PubMed]
- Barragán-Montero, A.; Javaid, U.; Valdés, G.; Nguyen, D.; Desbordes, P.; Macq, B.; Willems, S.; Vandewinckele, L.; Holmström, M.; Löfman, F.; et al. Artificial intelligence and machine learning for medical imaging: A technology review. Phys. Medica 2021, 83, 242–256. [Google Scholar] [CrossRef]
- Kim, M.; Yun, J.; Cho, Y.; Shin, K.; Jang, R.; Bae, H.j.; Kim, N. Deep Learning in Medical Imaging. Neurospine 2019, 16, 657–668. [Google Scholar] [CrossRef]
- Shen, D.; Wu, G.; Suk, H.I. Deep Learning in Medical Image Analysis. Annu. Rev. Biomed. Eng. 2017, 19, 221–248. [Google Scholar] [CrossRef]
- Razzak, M.I.; Naz, S.; Zaib, A. Deep Learning for Medical Image Processing: Overview, Challenges and the Future. In Classification in BioApps; Series Title: Lecture Notes in Computational Vision and Biomechanics; Dey, N., Ashour, A.S., Borra, S., Eds.; Springer International Publishing: Cham, Switzerland, 2018; Volume 26, pp. 323–350. [Google Scholar] [CrossRef]
- Nguyen, M. A scoping review of deep learning approaches for lung cancer detection using chest radiographs and computed tomography scans. Biomed. Eng. Adv. 2025, 9, 100138. [Google Scholar] [CrossRef]
- Hosseini, S.H.; Monsefi, R.; Shadroo, S. Deep learning applications for lung cancer diagnosis: A systematic review. Multimed. Tools Appl. 2023, 83, 14305–14335. [Google Scholar] [CrossRef]
- Javed, R.; Abbas, T.; Khan, A.H.; Daud, A.; Bukhari, A.; Alharbey, R. Deep learning for lungs cancer detection: A review. Artif. Intell. Rev. 2024, 57, 197. [Google Scholar] [CrossRef]
- Forte, G.C.; Altmayer, S.; Silva, R.F.; Stefani, M.T.; Libermann, L.L.; Cavion, C.C.; Youssef, A.; Forghani, R.; King, J.; Mohamed, T.L.; et al. Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis. Cancers 2022, 14, 3856. [Google Scholar] [CrossRef]
- Thanoon, M.A.; Zulkifley, M.A.; Mohd Zainuri, M.A.A.; Abdani, S.R. A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images. Diagnostics 2023, 13, 2617. [Google Scholar] [CrossRef] [PubMed]
- Liu, X.; Wang, R.; Zhu, Z.; Wang, K.; Gao, Y.; Li, J.; Zhang, Y.; Wang, X.; Zhang, X.; Wang, X. Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: Assessment of tumor treatment response according to the RECIST 1.1 criteria. BMC Cancer 2022, 22, 1285. [Google Scholar] [CrossRef]
Main Object | Criteria | Measurement | Type of Cancer | Reference |
---|---|---|---|---|
Compare different measurement approaches. | RECIST | 1D, 2D and 3D | Lung | [23] |
Compare different criteria | WHO and RECIST | 1D and 2D | Gastric, lung, breast and liver | [24] |
Compare different slice intervals. | WHO and RECIST 1.1 | 1D, 2D and 3D | Lung | [25] |
Compare measurement approaches (3D spherical and ellipsoid vs. 1D). | RECIST 1.1 | 1D and 3D | Lung | [26] |
Examine correlations between 1D and 3D measurements. | RECIST 1.1 | 1D and 3D | Lung | [27] |
Analyze RECIST with various tumor types and different treatments. | RECIST 1.1 | 1D | Lung, breast and colon | [28] |
Type of Lesion | Response Criteria | Condition |
---|---|---|
Target lession | Complete response (CR) | Disappearance of all target lesions or any pathological lymph nodes. |
Partial response (PR) | At least a 30% decrease in the sum of the target lesion diameters. | |
Progressive disease (PD) | At least a 20% increase in the sum of target lesion diameters and demonstrate an absolute increase of at least 5 mm. The appearance of one or more new lesions is also considered progression. | |
Stable disease (SD) | No contraction or increase sufficient to qualify as a PR or SD. | |
Non-target lesion | Complete response (CR) | Disappearance of all non-target lesions. |
Progressive disease (PD) | Unequivocal progression of existing non-target lesions. The appearance of one or more new lesions is considered progression. | |
Stable disease (SD) | Persistence of one or more non-target lesions. |
Response Criterion | EORTC | PERCIST |
---|---|---|
Complete metabolic response (CMR) | Complete resolution of FDG uptake in all lesions. | Complete resolution of FDG uptake in all lesions. |
Partial metabolic response (PMR) | Greater than 25% reduction in the sum of SUVmax after more than one cycle of treatment. | A minimum of 30% reduction in the peak lean body mass SUV (SULpeak) and an absolute drop of 0.8 SULpeak units. |
Progressive metabolic disease (PMD) | More than 25% increase in the sum of SUVmax or appearance of new FDG avid lesions. | More than 30% increase in the SULpeak of the FDG uptake and an absolute increase of 0.8 SULpeak, or appearance of FDG-avid new lesions. |
Stable disease (SD) | Not qualify for CMR, PMR or PMD. | Not qualify for CMR, PMR or PMD. |
DL Network | Main Applications | Reference |
---|---|---|
CNN | Image processing (detection, modeling, segmentation) | [34] |
RNN | Time-Series Analysis, Language Processing (Modeling, analysis, translation) and Speech Recognition | [35] |
RvNN | Molecular structure analysis and language processing (statistical modeling, analysis) | [36] |
DBN | Image recognition, speech recognition and natural language processing | [37] |
DBM | Images recognition and speech recognition | [38] |
GAN | Generate images, complete missing information and generate 3D models from 2D data | [39] |
VAE | Generate new data | [40,41] |
(a) | (b) |
(c) | (d) |
Ground Truth | |||
---|---|---|---|
Functional Tissue Units (1) | Background (0) | ||
Prediction | Functional Tissue Units (1) | TP | FP |
Background (0) | FN | TN |
Type of Filter | Name | Reference |
---|---|---|
Noise removal | Wiener Filter | [64] |
Gaussian Filter | [65] | |
Wavelet Filter | [66] | |
Blurring | Gaussian Filter | [67] |
Attenuation corrected | — | [68] |
Category | Database Name | Reference |
---|---|---|
CT lung cancer scans with manual delineation | NSCLC-Radiomics: https://www.cancerimagingarchive.net/collection/nsclc-radiomics/ accessed on 10 January 2025 | [78] |
CT lung cancer scans treated with anti-PD1 | Anti-PD-1Lung: https://www.cancerimagingarchive.net/collection/anti-pd-1_lung/ accessed on 10 January 2025 | [79] |
CT scans with labeled nodules | Lung Nodule Analysis 2016 (LUNA16): https://luna16.grand-challenge.org/Data/ accessed on 10 January 2025 | [64,82,94] |
CT scans with labeled (lung nodules, livers tumors and enlarged lymph nodes) | DeepLesion: https://nihcc.app.box.com/v/DeepLesion accessed on 10 January 2025 | [76,77] |
CT scans with labeled lung cancer | LIDC-IDRI: https://www.cancerimagingarchive.net/collection/lidc-idri/ accessed on 10 January 2025 | [83,85,88,91,94,95] |
CT scans with labeled nodules | QIN-LungCT-Seg: https://www.cancerimagingarchive.net/analysis-result/qin-lungct-seg/ accessed on 10 January 2025 | [78,86] |
CT scans with labeled nodules | RIDER Lung CT: https://www.cancerimagingarchive.net/collection/lidc-idri/ accessed on 10 January 2025 | [78,92] |
Reference | Model Architecture | Dimensional Approach | Evaluation |
---|---|---|---|
[103] | CNN (U-Net) | 3D | 93%(DC) |
[104] | CNN (V-Net) | 3D | 83%(DC) |
[105] | CNN (mU-Net) | 3D | 87%(DC) |
[106] | CNN (U-Net) | 3D | 78%(DC) |
[107] | CNN (R-CNN) | 2D | 84%(DC) |
[108] | CNN | 2D | 86%(DC), 83%(SEN) |
[109] | CNN (U-Net) | 3D | 84.4%(DC), 83%(SEN), 84%(SPE) |
Reference | Dataset | Image Resolution | Convolutional Layers | Deconvolutional and Max Pooling Layers |
---|---|---|---|---|
3D | ||||
[102] | – | 256 × 256 | 1 conv 1 × 1 × 1 | 3 maxpool |
5 Conv 3 × 3 × 16 | 3 deconv | |||
6 Conv 3 × 3 ×32 | ||||
6 Conv 3 × 3 ×64 | ||||
3 Conv 3 × 3 ×128 | ||||
[103] | 50 training | – | – | – |
26 validation | ||||
[104] | 48 training | 512 × 512 | 2 conv 3 × 3 ×16 | 2 maxpool |
36 validation | 2 conv 3 × 3 × 64 | 2 deconv | ||
2 conv 3 × 3 × 256 | ||||
1 conv 3 × 3 × 1 | ||||
[105] | – | – | 1 conv 3 × 3 × 2 | 3 maxpool |
3 conv 3 × 3 × 16 | 3 deconv | |||
4 conv 3 × 3 × 32 | ||||
6 conv 3 × 3 × 64 | ||||
[106] | 730 training | 512 × 512 | 10 conv | 4 maxpool |
81 validation | 4 deconv | |||
[107] | 63 validation | 256 × 256 | – | – |
[108] | 88 training | 128 × 128 | – | – |
47 validation | ||||
[109] | – | 144 × 144 | 1 conv 3 × 3 ×3 | 4 maxpool |
4 conv 1 × 1 × 1 | 4 deconv | |||
21 conv 3 × 3 × 3 |
Category | Database Name | Reference |
---|---|---|
PET/CT scans labeled Lung Cancer Diagnosis | Lung-PET-CT-Dx: https://www.cancerimagingarchive.net/collection/lung-pet-ct-dx/ accessed on 10 January 2025 | [102,107,108] |
PET/CT pretrained | ImageNet | [66] |
PET or PET/CT scans labeled | Private | [68,97,100,103,104,105,106,109,110] |
Survey Target | Review Content | Authors |
---|---|---|
AI technologies CDSSs | The study delves into ML algorithms, natural language processing (NLP), and DL theory and applications. The survey includes advancements for diagnosis, treatment recommendations, risk prediction, early intervention, and clinical documentation. It briefly discusses usability, ethical and legal implications, challenges, and opportunities. | Elhaddad and Hamam [122] |
The study presents advances in ML, particularly computer-aided detection (CADe), computer-aided diagnosis (CADx), and decision support systems. | Giger [123] | |
Evaluation of CDSSs | The primary topic concerns the challenges in evaluating AI-based CDSSs. It encompasses the design, development, use, and continuous surveillance stages. | Magrabi et al. [124] |
CDSSs implementation issues | The study identifies clinician issues on “trust” and “trustworthiness” by developing the following main aspects: control in terms of norms of clinical practice, autonomy, medical errors, and legal responsibility. The analysis encompasses both the clinician and patient perspectives. | Jones et al. [125] |
Review Content | Authors |
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The review examines ML for medical imaging, including preprocessing, segmentation, and post-processing tasks. The review brings closer to DL methods and model architectures, mainly for detection and classification, used to estimate malignancy. Diseases investigated were skin, lung, brain, and breast cancer. | Tajidini [126] |
The review begins with a description of the medical image characteristics, clinical requirements, and applications. It provides advancements in DL technology, including architectures, annotation approaches (transfer learning, domain adaptation, self-supervised learning, semi-supervised learning). Also, weakly/partially supervised and unsupervised learning, embedded knowledge into learning (enhanced learning from hybrid imaging techniques, for example), and federated learning (with robust algorithmic models). It also covers advances in image reconstruction, pretreatment, segmentation, registration, detection (CADe), diagnosis (CADx), and classification for diagnosis. Medical cases presented concern: | Zhou et al. [127] |
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The study primarily concerns ML types and CNN models. | |
The survey describes several aspects of DL-based tasks undertaken in medical applications using image processing: | Litjens et al. [128] |
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The research gathered and described involves CNN, RNN, SAE, AE, DQN, DBN neural networks architectures. The referenced studies concern image processing of anatomical areas, including neuro, retinal, pulmonary, breast, cardiac, abdominal, and musculoskeletal, obtained from CT, MRI, X-ray, microscopy, ultrasound (US), or video cervigrams. |
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Guzmán Gómez, R.; Lopez Lopez, G.; Alvarado, V.M.; Lopez Lopez, F.; Esqueda Cisneros, E.; López Moreno, H. Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging. Tomography 2025, 11, 78. https://doi.org/10.3390/tomography11070078
Guzmán Gómez R, Lopez Lopez G, Alvarado VM, Lopez Lopez F, Esqueda Cisneros E, López Moreno H. Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging. Tomography. 2025; 11(7):78. https://doi.org/10.3390/tomography11070078
Chicago/Turabian StyleGuzmán Gómez, Randy, Guadalupe Lopez Lopez, Victor M. Alvarado, Froylan Lopez Lopez, Eréndira Esqueda Cisneros, and Hazel López Moreno. 2025. "Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging" Tomography 11, no. 7: 78. https://doi.org/10.3390/tomography11070078
APA StyleGuzmán Gómez, R., Lopez Lopez, G., Alvarado, V. M., Lopez Lopez, F., Esqueda Cisneros, E., & López Moreno, H. (2025). Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging. Tomography, 11(7), 78. https://doi.org/10.3390/tomography11070078