Advancements in Cancer Detection: An Artificial Intelligence-Based Approach Using PET/CT Datasets
DOI:
https://doi.org/10.29194/NJES.28030451Keywords:
Artificial Intelligence (AI), Deep Learning (DL), Machine Learning (ML), Precision Oncology, Positron Emission Tomography/Computed Tomography (PET/CT), RadiomicsAbstract
Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.
Downloads
References
B. Li, J. Su, K. Liu, and C. Hu, "Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer," Eur. J. Radiol. Open, vol. 12, p. 100549, 2024. DOI:10.1016/j.ejro.2024.100549 DOI: https://doi.org/10.1016/j.ejro.2024.100549
B. Koa, A. J. Borja, M. Aly, S. Padmanabhan, J. Tran, V. Zhang, C. Rojulpote, S. K. Pierson, M. A. Tamakloe, J. S. Khor, and T. J. Werner, "Emerging role of 18F-FDG PET/CT in Castleman disease: A review," Insights Imaging, vol. 12, p. 1, 2021. DOI:10.1186/s13244-021-00963-1 DOI: https://doi.org/10.1186/s13244-021-00963-1
J. Park, S. K. Kang, D. Hwang, H. Choi, S. Ha, J. M. Seo, J. S. Eo, and J. S. Lee, "Automatic lung cancer segmentation in [18F] FDG PET/CT using a two-stage deep learning approach," Nucl. Med. Mol. Imaging, vol. 57, no. 2, pp. 86-93, 2023. DOI:10.1007/s13139-022-00745-7 DOI: https://doi.org/10.1007/s13139-022-00745-7
H. Guo, K. Xu, G. Duan, L. Wen, and Y. He, "Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: Toward precision medicine," Ann. Nucl. Med., vol. 36, pp. 1-4, 2022. DOI:10.1007/s12149-021-01683-8 DOI: https://doi.org/10.1007/s12149-021-01683-8
S. Kukava and M. Baramia, "Place and role of PET/CT in the diagnosis and staging of lung cancer," in Advances in Radiation Oncology in Lung Cancer, Cham: Springer, 2022, pp. 85-111. DOI:10.1007/174_2022_303 DOI: https://doi.org/10.1007/174_2022_303
M. Saied, M. Raafat, S. Yehia, and M. M. Khalil, "Efficient pulmonary nodules classification using radiomics and different artificial intelligence strategies," Insights Imaging, vol. 14, no. 1, p. 91, 2023. DOI:10.1186/s13244-023-01441-6 DOI: https://doi.org/10.1186/s13244-023-01441-6
N. B. Khalaf, H. K. Aljobouri, M. S. Najim, and I. Çankaya, "Simplified convolutional neural network model for automatic classification of retinal diseases from optical coherence tomography images," Al-Nahrain J. Eng. Sci., vol. 26, no. 4, pp. 314-319, 2023. DOI:10.29194/NJES.26040314 DOI: https://doi.org/10.29194/NJES.26040314
B. C. Sweetline and C. Vijayakumaran, "A comprehensive survey on deep learning-based pulmonary nodule identification on CT images," in Advances in Data-Driven Computing and Intelligent Systems, vol. 1, pp. 99-112, 2023. DOI:10.1007/978-981-99-3250-4_8 DOI: https://doi.org/10.1007/978-981-99-3250-4_8
C. Jacobs, "Challenges and outlook in the management of pulmonary nodules detected on CT," Eur. Radiol., vol. 34, no. 1, pp. 247-249, 2024. DOI:10.1007/s00330-023-10065-9 DOI: https://doi.org/10.1007/s00330-023-10065-9
T. Emad Ali, F. Imad Ali, A. Hussein Morad, M. A. Abdala, and A. Dhulfiqar Zoltan, "Diabetic patient real-time monitoring system using machine learning," Int. J. Comput. Digit. Syst., vol. 16, no. 1, pp. 1123-1134, 2024. DOI:10.12785/ijcds/160169 DOI: https://doi.org/10.12785/ijcds/160182
F. Grisanti, J. Zulueta, J. J. Rosales, M. I. Morales, L. Sancho, M. D. Lozano, M. Mesa-Guzman, and M. J. Garcia-Velloso, "Diagnostic accuracy of visual analysis versus dual time-point imaging with 18F-FDG PET/CT for the characterization of indeterminate pulmonary nodules with low uptake," Rev. Esp. Med. Nucl. Imagen Mol., vol. 40, no. 3, pp. 155-160, 2021. https://doi.org/10.1016/j.remn.2020.10.001 DOI: https://doi.org/10.1016/j.remnie.2020.05.002
H. Alrubaie, H. K. Aljobouri, Z. J. AL-Jobawi, and I. Çankaya, "Convolutional neural network deep learning model for improved ultrasound breast tumor classification," Al-Nahrain J. Eng. Sci., vol. 26, no. 2, pp. 57-62, 2023. DOI:10.29194/NJES.26020057 DOI: https://doi.org/10.29194/NJES.26020057
J. W. Fletcher and P. E. Kinahan, "PET/CT standardized uptake values (SUVs) in clinical practice and assessing response to therapy," Semin. Ultrasound CT MR, vol. 31, no. 6, pp. 496-505, 2010. DOI:10.1053/j.sult.2010.10.001 DOI: https://doi.org/10.1053/j.sult.2010.10.001
M. Schwyzer, K. Martini, D. C. Benz, I. A. Burger, D. A. Ferraro, K. Kudura, V. Treyer, G. K. von Schulthess, P. A. Kaufmann, M. W. Huellner, and M. Messerli, "Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: Impact of image reconstructions on diagnostic performance," Eur. Radiol., vol. 30, pp. 2031-2040, 2020. DOI:10.1007/s00330-019-06498-w DOI: https://doi.org/10.1007/s00330-019-06498-w
K. Tang, L. Wang, J. Lin, X. Zheng, and Y. Wu, "The value of 18F-FDG PET/CT in the diagnosis of different size of solitary pulmonary nodules," Medicine, vol. 98, no. 11, p. e14813, 2019. DOI:10.1097/MD.0000000000014813 DOI: https://doi.org/10.1097/MD.0000000000014813
J. L. Espinoza and L. T. Dong, "Artificial intelligence tools for refining lung cancer screening," J. Clin. Med., vol. 9, no. 12, p. 3860, 2020. DOI:10.3390/jcm9123860 DOI: https://doi.org/10.3390/jcm9123860
L. Sibille, R. Seifert, N. Avramovic, T. Vehren, B. Spottiswoode, S. Zuehlsdorff, and M. Schäfers, "18F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks," Radiology, vol. 294, no. 2, pp. 445-452, 2020. DOI:10.1148/radiol.2019191114 DOI: https://doi.org/10.1148/radiol.2019191114
P. Borrelli, J. Ly, R. Kaboteh, J. Ulén, O. Enqvist, E. Trägårdh, and L. Edenbrandt, "AI-based detection of lung lesions in [18F] FDG PET-CT from lung cancer patients," EJNMMI Phys., vol. 8, p. 1, 2021. DOI:10.1186/s40658-021-00376-5 DOI: https://doi.org/10.1186/s40658-021-00376-5
M. M. Krarup, G. Krokos, M. Subesinghe, A. Nair, and B. M. Fischer, "Artificial intelligence for the characterization of pulmonary nodules, lung tumors and mediastinal nodes on PET/CT," Semin. Nucl. Med., vol. 51, no. 2, pp. 143-156, 2021. DOI:10.1053/j.semnuclmed.2020.09.001 DOI: https://doi.org/10.1053/j.semnuclmed.2020.09.001
X. Fu, L. Bi, A. Kumar, M. Fulham, and J. Kim, "Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation," IEEE J. Biomed. Health Inform., vol. 25, no. 9, pp. 3507-3516, 2021. DOI:10.1109/JBHI.2021.3059453 DOI: https://doi.org/10.1109/JBHI.2021.3059453
S. R. Jena, S. T. George, and D. N. Ponraj, "Lung cancer detection and classification with DGMM-RBCNN technique," Neural Comput. Appl., vol. 33, no. 22, pp. 15601-15617, 2021. DOI:10.1007/s00521-021-06182-5 DOI: https://doi.org/10.1007/s00521-021-06182-5
Y. J. Park, D. Choi, J. Y. Choi, and S. H. Hyun, "Performance evaluation of a deep learning system for differential diagnosis of lung cancer with conventional CT and FDG PET/CT using transfer learning and metadata," Clin. Nucl. Med., vol. 46, no. 8, pp. 635-640, 2021. DOI:10.1097/RLU.0000000000003763 DOI: https://doi.org/10.1097/RLU.0000000000003661
J. Dafni Rose, K. Jaspin, and K. Vijayakumar, "Lung cancer diagnosis based on image fusion and prediction using CT and PET image," in Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems, 2021, pp. 67-86. DOI:10.1007/978-981-15-6141-2_4 DOI: https://doi.org/10.1007/978-981-15-6141-2_4
S. Chen, X. Han, G. Tian, Y. Cao, X. Zheng, X. Li, and Y. Li, "Using stacked deep learning models based on PET/CT images and clinical data to predict EGFR mutations in lung cancer," Front. Med., vol. 9, p. 1041034, 2022. DOI:10.3389/fmed.2022.1041034 DOI: https://doi.org/10.3389/fmed.2022.1041034
N. E. Protonotarios, I. Katsamenis, S. Sykiotis, N. Dikaios, G. A. Kastis, S. N. Chatziioannou, M. Metaxas, N. Doulamis, and A. Doulamis, "A few-shot U-Net deep learning model for lung cancer lesion segmentation via PET/CT imaging," Biomed. Phys. Eng. Express, vol. 8, no. 2, p. 025019, 2022. DOI:10.1088/2057-1976/ac53bd DOI: https://doi.org/10.1088/2057-1976/ac53bd
H. Guo, K. Xu, G. Duan, L. Wen, and Y. He, "Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: Toward precision medicine," Ann. Nucl. Med., vol. 36, pp. 1-4, 2022. DOI:10.1007/s12149-021-01683-8 DOI: https://doi.org/10.1007/s12149-021-01683-8
K. P. Das and J. Chandra, "Multimodal classification on PET/CT image fusion for lung cancer: A comprehensive survey," ECS Trans., vol. 107, no. 1, p. 3649, 2022. DOI:10.1149/10701.3649ecst DOI: https://doi.org/10.1149/10701.3649ecst
U. Batra, S. Nathany, S. K. Nath, J. T. Jose, R. Sinha, P. P., T. Sharma, S. Pasricha, M. Sharma, A. Bansal, and K. Rawal, "AI in NSCLC: PET-CT & histology model," Unpublished conference abstract, 2022. DOI:10.1200/JCO.2022.40.16_suppl.e21044 DOI: https://doi.org/10.1200/JCO.2022.40.16_suppl.e21044
W. Huang, J. Wang, H. Wang, Y. Zhang, F. Zhao, K. Li, L. Su, F. Kang, and X. Cao, "PET/CT based EGFR mutation status classification of NSCLC using deep learning features and radiomics features," Front. Pharmacol., vol. 13, p. 898529, 2022. DOI:10.3389/fphar.2022.898529 DOI: https://doi.org/10.3389/fphar.2022.898529
K. Barbouchi, D. El Hamdi, I. Elouedi, T. B. Aïcha, A. K. Echi, and I. Slim, "A transformer‐based deep neural network for detection and classification of lung cancer via PET/CT images," Int. J. Imaging Syst. Technol., vol. 33, no. 4, pp. 1383-1395, 2023. DOI:10.1002/ima.22838 DOI: https://doi.org/10.1002/ima.22858
C. Owens, S. Hindocha, R. Lee, T. Millard, and B. Sharma, "The lung cancers: Staging and response, CT, 18F-FDG PET/CT, MRI, DWI: Review and new perspectives," Br. J. Radiol., vol. 96, no. 1148, p. 20220339, 2023. DOI:10.1259/bjr.20220339 DOI: https://doi.org/10.1259/bjr.20220339
H. Wang, Y. Li, J. Han, Q. Lin, L. Zhao, Q. Li, J. Zhao, H. Li, Y. Wang, and C. Hu, "A machine learning-based PET/CT model for automatic diagnosis of early-stage lung cancer," Front. Oncol., vol. 13, p. 1192908, 2023. DOI:10.3389/fonc.2023.1192908 DOI: https://doi.org/10.3389/fonc.2023.1192908
Z. Gandhi, P. Gurram, B. Amgai, S. P. Lekkala, A. Lokhandwala, S. Manne, A. Mohammed, H. Koshiya, N. Dewaswala, R. Desai, and H. Bhopalwala, "Artificial intelligence and lung cancer: Impact on improving patient outcomes," Cancers, vol. 15, no. 21, p. 5236, 2023. DOI:10.3390/cancers15215236 DOI: https://doi.org/10.3390/cancers15215236
Y. Onozato, T. Iwata, Y. Uematsu, D. Shimizu, T. Yamamoto, Y. Matsui, K. Ogawa, J. Kuyama, Y. Sakairi, E. Kawakami, and T. Iizasa, "Predicting pathological highly invasive lung cancer from preoperative [18F] FDG PET/CT with multiple machine learning models," Eur. J. Nucl. Med. Mol. Imaging, vol. 50, no. 3, pp. 715-726, 2023. DOI:10.1007/s00259-022-06038-7 DOI: https://doi.org/10.1007/s00259-022-06038-7
N. S. Reddy and V. Khanaa, "Intelligent deep learning algorithm for lung cancer detection and classification," Bull. Electr. Eng. Inform., vol. 12, no. 3, pp. 1747-1754, 2023. DOI:10.11591/eei.v12i3.4579 DOI: https://doi.org/10.11591/eei.v12i3.4579
R. Da-Ano, G. Andrade-Miranda, O. Tankyevych, D. Visvikis, P. H. Conze, and C. C. Rest, "Automated PD-L1 status prediction in lung cancer with multi-modal PET/CT fusion," Sci. Rep., vol. 14, no. 1, p. 16720, 2024. DOI:10.1038/s41598-024-66487-y DOI: https://doi.org/10.1038/s41598-024-66487-y
M. P. Rajendran, S. Pallaiyah, K. Ramaswamy, J. Govindaraj, V. Varadharajan, and S. Lakshmi, "An efficient image classification of lung nodule classification approach using CT and PET fused images," in AIP Conf. Proc., vol. 3042, no. 1, p. 040001, 2024. DOI:10.1063/5.0194202 DOI: https://doi.org/10.1063/5.0194202
A. H. M. Torbati, S. Pellegrino, R. Fonti, R. Morra, S. De Placido, and S. Del Vecchio, "Machine learning and texture analysis of [18F] FDG PET/CT images for the prediction of distant metastases in non-small-cell lung cancer patients," Biomedicines, vol. 12, no. 3, p. 472, 2024. DOI:10.3390/biomedicines12030472 DOI: https://doi.org/10.3390/biomedicines12030472
P. S. Bharathi and C. Shalini, "Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection," Med. Eng. Phys., vol. 126, p. 104138, 2024. DOI:10.1016/j.medengphy.2024.104138 DOI: https://doi.org/10.1016/j.medengphy.2024.104138
H. Zhao, Y. Su, Z. Lyu, L. Tian, P. Xu, L. Lin, W. Han, and P. Fu, "Non-invasively discriminating the pathological subtypes of non-small cell lung cancer with pretreatment 18F-FDG PET/CT using deep learning," Acad. Radiol., vol. 31, no. 1, pp. 35-45, 2024. DOI:10.1016/j.acra.2023.07.014 DOI: https://doi.org/10.1016/j.acra.2023.03.032
R. J. Kelly, G. D. Anderson, B. S. Joshi, and J. J. Donald, "Utility of FDG PET‐CT in CT Stage IA non‐small cell lung cancer: The New Zealand Te Whatu Ora Northern region experience," J. Med. Imaging Radiat. Oncol., Jun. 28, 2024. DOI:10.1111/1754-9485.13720 DOI: https://doi.org/10.1111/1754-9485.13720
L. Yuan, L. An, Y. Zhu, C. Duan, W. Kong, P. Jiang, and Q. Q. Yu, "Machine learning in diagnosis and prognosis of lung cancer by PET-CT," Cancer Manag. Res., vol. 16, pp. 361-375, Dec. 2024. DOI:10.2147/CMAR.S451871 DOI: https://doi.org/10.2147/CMAR.S451871
X. Shao, X. Ge, J. Gao, R. Niu, Y. Shi, X. Shao, Z. Jiang, R. Li, and Y. Wang, "Transfer learning-based PET/CT three-dimensional convolutional neural network fusion of image and clinical information for prediction of EGFR mutation in lung adenocarcinoma," BMC Med. Imaging, vol. 24, no. 1, p. 54, Mar. 2024. DOI:10.1186/s12880-024-01232-5 DOI: https://doi.org/10.1186/s12880-024-01232-5
B. Li, J. Su, K. Liu, and C. Hu, "Deep learning radiomics model based on PET/CT predicts PD-L1 expression in non-small cell lung cancer," Eur. J. Radiol. Open, vol. 12, p. 100549, Jun. 2024. DOI:10.1016/j.ejro.2024.100549 DOI: https://doi.org/10.1016/j.ejro.2024.100549
Z. Guo, N. Guo, K. Gong, and Q. Li, "Gross tumor volume segmentation for head and neck cancer radiotherapy using deep dense multi-modality network," Phys. Med. Biol., vol. 64, no. 20, p. 205015, Oct. 2019. DOI:10.1088/1361-6560/ab440d DOI: https://doi.org/10.1088/1361-6560/ab440d
W. Lv, S. Ashrafinia, J. Ma, L. Lu, and A. Rahmim, "Multi-level multi-modality fusion radiomics: Application to PET and CT imaging for prognostication of head and neck cancer," IEEE J. Biomed. Health Inform., vol. 24, no. 8, pp. 2268-2277, Dec. 2019. DOI:10.1109/JBHI.2019.2956354 DOI: https://doi.org/10.1109/JBHI.2019.2956354
M. A. Naser, L. V. van Dijk, R. He, K. A. Wahid, and C. D. Fuller, "Tumor segmentation in patients with head and neck cancers using deep learning based-on multi-modality PET/CT images," in 3D Head and Neck Tumor Segmentation in PET/CT Challenge, Cham: Springer, Oct. 2020, pp. 85-98. DOI:10.1007/978-3-030-67194-5_10 DOI: https://doi.org/10.1007/978-3-030-67194-5_10
V. Andrearczyk, V. Oreiller, M. Vallières, J. Castelli, H. Elhalawani, M. Jreige, S. Boughdad, J. O. Prior, and A. Depeursinge, "Automatic segmentation of head and neck tumors and nodal metastases in PET-CT scans," in Med. Imaging Deep Learn., PMLR, Sep. 2020, pp. 33-43.
K. Kawauchi, S. Furuya, K. Hirata, C. Katoh, O. Manabe, K. Kobayashi, S. Watanabe, and T. Shiga, "A convolutional neural network-based system to classify patients using FDG PET/CT examinations," BMC Cancer, vol. 20, p. 1000, Dec. 2020. DOI:10.1186/s12885-020-6694-x DOI: https://doi.org/10.1186/s12885-020-6694-x
A. R. Groendahl, I. S. Knudtsen, B. N. Huynh, M. Mulstad, Y. M. Moe, F. Knuth, O. Tomic, U. G. Indahl, T. Torheim, E. Dale, and E. Malinen, "A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers," Phys. Med. Biol., vol. 66, no. 6, p. 065012, Mar. 2021. DOI:10.1088/1361-6560/abe553 DOI: https://doi.org/10.1088/1361-6560/abe553
A. Qayyum, A. Benzinou, M. Mazher, M. Abdel-Nasser, and D. Puig, "Automatic segmentation of head and neck (H&N) primary tumors in PET and CT images using 3D-Inception-ResNet model," in 3D Head and Neck Tumor Segmentation in PET/CT Challenge, Cham: Springer, Sep. 2021, pp. 58-67. DOI:10.1007/978-3-030-98253-9_4 DOI: https://doi.org/10.1007/978-3-030-98253-9_4
S. N. Marschner, E. Lombardo, L. Minibek, A. Holzgreve, L. Kaiser, N. L. Albert, C. Kurz, M. Riboldi, R. Späth, P. Baumeister, and M. Niyazi, "Risk stratification using 18F-FDG PET/CT and artificial neural networks in head and neck cancer patients undergoing radiotherapy," Diagnostics, vol. 11, no. 9, p. 1581, Aug. 2021. DOI:10.3390/diagnostics11091581 DOI: https://doi.org/10.3390/diagnostics11091581
W. Lv, H. Feng, D. Du, J. Ma, and L. Lu, "Complementary value of intra-and peri-tumoral PET/CT radiomics for outcome prediction in head and neck cancer," IEEE Access, vol. 9, pp. 81818-81827, Jun. 2021. DOI:10.1109/ACCESS.2021.3085601 DOI: https://doi.org/10.1109/ACCESS.2021.3085601
Q. Zhang, K. Wang, Z. Zhou, G. Qin, L. Wang, P. Li, D. Sher, S. Jiang, and J. Wang, "Predicting local persistence/recurrence after radiation therapy for head and neck cancer from PET/CT using a multi-objective, multi-classifier radiomics model," Front. Oncol., vol. 12, p. 955712, Sep. 2022. DOI:10.3389/fonc.2022.955712 DOI: https://doi.org/10.3389/fonc.2022.955712
T. Pipikos, M. Vogiatzis, and V. Prasopoulos, "Artificial intelligence in head and neck cancer patients," in Artificial Intelligence in PET/CT Oncologic Imaging, Cham: Springer, Oct. 2022, pp. 33-38. DOI:10.1007/978-3-031-10090-1_4 DOI: https://doi.org/10.1007/978-3-031-10090-1_4
Y. Wang, E. Lombardo, M. Avanzo, S. Zschaek, J. Weingärtner, A. Holzgreve, N. L. Albert, S. Marschner, G. Fanetti, G. Franchin, and J. Stancanello, "Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis," Comput. Methods Programs Biomed., vol. 222, p. 106948, Jul. 2022. DOI:10.1016/j.cmpb.2022.106948 DOI: https://doi.org/10.1016/j.cmpb.2022.106948
M. R. Salmanpour, G. Hajianfar, M. Hosseinzadeh, S. M. Rezaeijo, M. M. Hosseini, E. Kalatehjari, A. Harimi, and A. Rahmim, "Deep learning and machine learning techniques for automated PET/CT segmentation and survival prediction in head and neck cancer," in 3D Head and Neck Tumor Segmentation in PET/CT Challenge, Cham: Springer, Sep. 2022, pp. 230-239. DOI:10.1007/978-3-031-27420-6_23 DOI: https://doi.org/10.1007/978-3-031-27420-6_23
P. Nikulin, S. Zschaeck, J. Maus, P. Cegla, E. Lombardo, C. Furth, J. Kaźmierska, J. M. Rogasch, A. Holzgreve, N. L. Albert, and K. Ferentinos, "A convolutional neural network with self-attention for fully automated metabolic tumor volume delineation of head and neck cancer in [18F] FDG PET/CT," Eur. J. Nucl. Med. Mol. Imaging, vol. 50, no. 9, pp. 2751-2766, Jul. 2023. DOI:10.1007/s00259-023-06197-1 DOI: https://doi.org/10.1007/s00259-023-06197-1
N. M. Rad, H. C. Woodruff, and P. Lambin, "HNT-AI: An automatic segmentation framework for head and neck primary tumors and lymph nodes in FDG-PET/CT images," in Head and Neck Tumor Segmentation and Outcome Prediction: Third Challenge, HECKTOR 2022, Cham: Springer, Mar. 2023, vol. 13626, p. 212. DOI:10.1007/978-3-031-27420-6_21 DOI: https://doi.org/10.1007/978-3-031-27420-6_21
H. Xu, N. Abdallah, J. M. Marion, P. Chauvet, C. Tauber, T. Carlier, L. Lu, and M. Hatt, "Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: Investigating ComBat strategies, sub-volume characterization, and automatic segmentation," Eur. J. Nucl. Med. Mol. Imaging, vol. 50, no. 6, pp. 1720-1734, May 2023. DOI:10.1007/s00259-023-06118-2 DOI: https://doi.org/10.1007/s00259-023-06118-2
L. Michelutti, A. Tel, M. Zeppieri, T. Ius, S. Sembronio, and M. Robiony, "The use of artificial intelligence algorithms in the prognosis and detection of lymph node involvement in head and neck cancer and possible impact in the development of personalized therapeutic strategy: A systematic review," J. Pers. Med., vol. 13, no. 12, p. 1626, Nov. 2023. DOI:10.3390/jpm13121626 DOI: https://doi.org/10.3390/jpm13121626
V. Andrearczyk, V. Oreiller, S. Boughdad, C. C. Le Rest, O. Tankyevych, H. Elhalawani, M. Jreige, J. O. Prior, M. Vallières, D. Visvikis, and M. Hatt, "Automatic head and neck tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge," Med. Image Anal., vol. 90, p. 102972, Dec. 2023. DOI:10.1016/j.media.2023.102972 DOI: https://doi.org/10.1016/j.media.2023.102972
M. Illimoottil and D. Ginat, "Recent advances in deep learning and medical imaging for head and neck cancer treatment: MRI, CT, and PET scans," Cancers, vol. 15, no. 13, p. 3267, Jun. 2023. DOI:10.3390/cancers15133267 DOI: https://doi.org/10.3390/cancers15133267
A. Toosi, I. Shiri, H. Zaidi, and A. Rahmim, "Segmentation-free outcome prediction from head and neck cancer PET/CT images: Deep learning-based feature extraction from multi-angle maximum intensity projections (MA-MIPs)," Cancers, vol. 16, no. 14, Jul. 2024. DOI:10.3390/cancers16142538 DOI: https://doi.org/10.3390/cancers16142538
I. Shiri, M. Amini, F. Yousefirizi, A. Vafaei Sadr, G. Hajianfar, Y. Salimi, Z. Mansouri, E. Jenabi, M. Maghsudi, I. Mainta, and M. Becker, "Information fusion for fully automated segmentation of head and neck tumors from PET and CT images," Med. Phys., vol. 51, no. 1, pp. 319-333, Jan. 2024. DOI:10.1002/mp.16556 DOI: https://doi.org/10.1002/mp.16615
D. G. Kovacs, C. N. Ladefoged, K. F. Andersen, J. M. Brittain, C. B. Christensen, D. Dejanovic, N. L. Hansen, A. Loft, J. H. Petersen, M. Reichkendler, and F. L. Andersen, "Clinical evaluation of deep learning for tumor delineation on 18F-FDG PET/CT of head and neck cancer," J. Nucl. Med., vol. 65, no. 4, pp. 623-629, Apr. 2024. DOI:10.2967/jnumed.123.266574 DOI: https://doi.org/10.2967/jnumed.123.266574
M. Sadik, E. Lind, E. Polymeri, O. Enqvist, J. Ulén, and E. Trägårdh, "Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG‐PET/CT in Hodgkin and non‐Hodgkin lymphomas," Clin. Physiol. Funct. Imaging, vol. 39, no. 1, pp. 78-84, Jan. 2019. DOI:10.1111/cpf.12530 DOI: https://doi.org/10.1111/cpf.12546
P. Borrelli, M. Larsson, J. Ulén, O. Enqvist, E. Trägårdh, M. H. Poulsen, M. A. Mortensen, H. Kjölhede, P. F. Høilund‐Carlsen, and L. Edenbrandt, "Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival," Clin. Physiol. Funct. Imaging, vol. 41, no. 1, pp. 62-67, Jan. 2021. DOI:10.1111/cpf.12696 DOI: https://doi.org/10.1111/cpf.12666
Y. Yang, B. Zheng, Y. Li, Y. Li, and X. Ma, "Computer‐aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT," Med. Phys., vol. 50, no. 1, pp. 152-162, Jan. 2023. DOI:10.1002/mp.16036 DOI:10.1002/mp.16036 DOI: https://doi.org/10.1002/mp.15901
E. M. Veziroglu, F. Farhadi, N. Hasani, M. Nikpanah, M. Roschewski, R. M. Summers, and B. Saboury, "Role of artificial intelligence in PET/CT imaging for management of lymphoma," in Semin. Nucl. Med., vol. 53, no. 3, pp. 426-448, May 2023. DOI:10.1053/j.semnuclmed.2023.02.004 DOI: https://doi.org/10.1053/j.semnuclmed.2022.11.003
S. Ellmann, L. Seyler, J. Evers, H. Heinen, A. Bozec, O. Prante, T. Kuwert, M. Uder, and T. Bäuerle, "Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a model-averaged neural network," Bone, vol. 120, pp. 254-261, Mar. 2019. DOI:10.1016/j.bone.2018.11.008 DOI: https://doi.org/10.1016/j.bone.2018.11.008
Y. Ming, N. Wu, T. Qian, X. Li, D. Q. Wan, C. Li, Y. Li, Z. Wu, X. Wang, J. Liu, and N. Wu, "Progress and future trends in PET/CT and PET/MRI molecular imaging approaches for breast cancer," Front. Oncol., vol. 10, p. 507927, 2020. DOI:10.3389/fonc.2020.01301 DOI: https://doi.org/10.3389/fonc.2020.01301
X. Ou, J. Zhang, J. Wang, F. Pang, Y. Wang, X. Wei, and X. Ma, "Radiomics based on 18F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study," Cancer Med., vol. 9, no. 2, pp. 496-506, Jan. 2020. DOI:10.1002/cam4.2703 DOI: https://doi.org/10.1002/cam4.2711
M. Weber, D. Kersting, L. Umutlu, M. Schäfers, C. Rischpler, W. P. Fendler, I. Buvat, K. Herrmann, and R. Seifert, "Just another 'Clever Hans'? Neural networks and FDG PET-CT to predict the outcome of patients with breast cancer," Eur. J. Nucl. Med. Mol. Imaging, Sep. 2021. DOI:10.1007/s00259-021-05270-x DOI: https://doi.org/10.1007/s00259-021-05270-x
Y. Han, Y. Ma, Z. Wu, F. Zhang, D. Zheng, X. Liu, L. Tao, Z. Liang, Z. Yang, X. Li, and J. Huang, "Histologic subtype classification of non-small cell lung cancer using PET/CT images," Eur. J. Nucl. Med. Mol. Imaging, vol. 48, pp. 350-360, Feb. 2021. DOI:10.1007/s00259-020-04771-5 DOI: https://doi.org/10.1007/s00259-020-04771-5
K. Takahashi, T. Fujioka, J. Oyama, M. Mori, E. Yamaga, Y. Yashima, T. Imokawa, A. Hayashi, Y. Kujiraoka, J. Tsuchiya, and G. Oda, "Deep learning using multiple degrees of maximum-intensity projection for PET/CT image classification in breast cancer," Tomography, vol. 8, no. 1, pp. 131-141, Jan. 2022. DOI:10.3390/tomography8010012 DOI: https://doi.org/10.3390/tomography8010011
Y. Chen, Z. Wang, G. Yin, C. Sui, Z. Liu, X. Li, and W. Chen, "Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning," Ann. Nucl. Med., Feb. 2022. DOI:10.1007/s12149-021-01688-3 DOI: https://doi.org/10.1007/s12149-021-01688-3
G. Bulut, H. I. Atilgan, G. Çınarer, K. Kılıç, D. Yıkar, and T. Parlar, "Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT," PLoS One, vol. 18, no. 9, p. e0290543, Sep. 2023. DOI:10.1371/journal.pone.0290543 DOI: https://doi.org/10.1371/journal.pone.0290543
L. Castorina, A. D. Comis, A. Prestifilippo, N. Quartuccio, S. Panareo, L. Filippi, S. Castorina, and D. Giuffrida, "Innovations in positron emission tomography and state of the art in the evaluation of breast cancer treatment response," J. Clin. Med., vol. 13, no. 1, p. 154, Dec. 2023. DOI:10.3390/jcm13010154 DOI: https://doi.org/10.3390/jcm13010154
D. de Jong, E. Desperito, K. A. Al Feghali, L. Dercle, R. D. Seban, J. P. Das, H. Ma, A. Sajan, B. Braumuller, C. Prendergast, and C. Liou, "Advances in PET/CT imaging for breast cancer," J. Clin. Med., vol. 12, no. 13, p. 4537, Jul. 2023. DOI:10.3390/jcm12134537 DOI: https://doi.org/10.3390/jcm12134537
K. Kawaji, M. Nakajo, Y. Shinden, M. Jinguji, A. Tani, D. Hirahara, I. Kitazono, T. Ohtsuka, and T. Yoshiura, "Application of machine learning analyses using clinical and [18F]-FDG-PET/CT radiomic characteristics to predict recurrence in patients with breast cancer," Mol. Imaging Biol., vol. 25, no. 5, pp. 923-934, Oct. 2023. DOI:10.1007/s11307-023-01806-9 DOI: https://doi.org/10.1007/s11307-023-01823-8
Z. Li, K. Kitajima, K. Hirata, R. Togo, J. Takenaka, Y. Miyoshi, K. Kudo, T. Ogawa, and M. Haseyama, "Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer," EJNMMI Res., vol. 11, p. 100, Dec. 2021. DOI:10.1186/s13550-021-00751-4 DOI: https://doi.org/10.1186/s13550-021-00751-4
K. Carrasco, L. Tomalá, E. Ramírez Meza, D. Meza Bolaños, and W. Ramírez Montalvan, "Computational techniques in PET/CT image processing for breast cancer: A systematic mapping review," ACM Comput. Surv., vol. 56, no. 8, pp. 1-38, Apr. 2024. DOI:10.1145/3648359 DOI: https://doi.org/10.1145/3648359
N. Robson and D. K. Thekkinkattil, "Current role and future prospects of positron emission tomography (PET)/computed tomography (CT) in the management of breast cancer," Medicina, vol. 60, no. 2, p. 321, Feb. 2024. DOI:10.3390/medicina60020321 DOI: https://doi.org/10.3390/medicina60020321
A. Hossain and S. I. Chowdhury, "Breast cancer subtype prediction model employing artificial neural network and 18F-fluorodeoxyglucose positron emission tomography/computed tomography," J. Med. Phys., vol. 49, no. 2, pp. 181-188, Apr. 2024. DOI:10.4103/jmp.jmp_181_23 DOI: https://doi.org/10.4103/jmp.jmp_181_23
A. Özdemir, M. Güven, S. Binici, S. Uygur, and O. Toktaş, "Impact of 18F-FDG PET/CT in the management decisions of breast cancer board on early-stage breast cancer," Clin. Transl. Oncol., vol. 26, no. 5, pp. 1139-1146, May 2024. DOI:10.1007/s12094-023-03331-1 DOI: https://doi.org/10.1007/s12094-023-03331-1
L. K. Shiyam Sundar, S. Gutschmayer, M. Maenle, and T. Beyer, "Extracting value from total-body PET/CT image data-the emerging role of artificial intelligence," Cancer Imaging, vol. 24, no. 1, p. 51, Apr. 2024. DOI:10.1186/s40644-024-00684-w DOI: https://doi.org/10.1186/s40644-024-00684-w
J. W. Froelich and A. Salavati, "Artificial intelligence in PET/CT is about to make whole-body tumor burden measurements a clinical reality," Radiology, vol. 294, no. 2, pp. 453-454, Feb. 2020. DOI:10.1148/radiol.2019192425 DOI: https://doi.org/10.1148/radiol.2019192425
I. Dirks, M. Keyaerts, B. Neyns, and J. Vandemeulebroucke, "Computer-aided detection and segmentation of malignant melanoma lesions on whole-body 18F-FDG PET/CT using an interpretable deep learning approach," Comput. Methods Programs Biomed., vol. 221, p. 106902, Jun. 2022. DOI:10.1016/j.cmpb.2022.106902 DOI: https://doi.org/10.1016/j.cmpb.2022.106902
Y. Zhang, C. Cheng, Z. Liu, G. Pan, G. Sun, X. Yang, and C. Zuo, "Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in 18F-FDG PET/CT," J. Biomed. Eng., vol. 36, no. 5, pp. 755-762, Oct. 2019.
W. C. Shen, S. W. Chen, K. C. Wu, T. C. Hsieh, J. A. Liang, Y. C. Hung, L. S. Yeh, W. C. Chang, W. C. Lin, K. Y. Yen, and C. H. Kao, "Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose PET/CT," Eur. Radiol., vol. 29, pp. 6741-6749, Dec. 2019. DOI:10.1007/s00330-019-06265-x DOI: https://doi.org/10.1007/s00330-019-06265-x
Y. Peng, L. Bi, Y. Guo, D. Feng, M. Fulham, and J. Kim, "Deep multi-modality collaborative learning for distant metastases prediction in PET-CT soft-tissue sarcoma studies," in Proc. 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Jul. 2019, pp. 3658-3688. DOI:10.1109/EMBC.2019.8857878 DOI: https://doi.org/10.1109/EMBC.2019.8857666
J. Wang, Y. Zhou, J. Zhou, H. Liu, and X. Li, "Preliminary study on the ability of the machine learning models based on 18F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma," Eur. J. Radiol., p. 111531, May 2024. DOI:10.1016/j.ejrad.2024.111531 DOI: https://doi.org/10.1016/j.ejrad.2024.111531
H. Patel, T. Zanos, and D. B. Hewitt, "Deep learning applications in pancreatic cancer," Cancers, vol. 16, no. 2, p. 436, Jan. 2024. DOI:10.3390/cancers16020436 DOI: https://doi.org/10.3390/cancers16020436
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Faten Imad Ali, Hadeel K. AlJobouri , Ali M. Hasan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors retain the copyright of their manuscript by submitting the work to this journal, and all open access articles are distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0), which permits use for any non-commercial purpose, distribution, and reproduction in any medium, provided that the original work is properly cited.
How to Cite
