Razavi Journal of Medicine

Razavi Journal of Medicine

Artificial Intelligence and Radiomics for Detection, Segmentation, and Biomarker Prediction in Gastrointestinal Cancers

Articles in Press

Document Type : Review Article/ Systematic Review Article/ Meta Analysis

Authors
Niloofar Hooshmand 1
Mobin Ahmadi 1
Ramin Raoufinia 2, 3
Aida Yavari Kondori 1
Ibrahim Saeed Gataa 4
Ali Gholami 2, 3
Amir Avan 1
Hamid Naderi 1
1 Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
2 Healthy Ageing Research Centre, Neyshabur University of Medical Sciences, Neyshabur, Iran
3 Noncommunicable Diseases Research Center, Neyshabur University of Medical Sciences, Neyshabur, Iran.
4 College of Medicine, University of Warith Al-Anbiyaa, Karbala 56001, Iraq.
10.30483/rjm.2026.254841.1579
Abstract
Background: Early diagnosis of gastrointestinal (GI) cancers remains challenging because of the heterogeneity of imaging modalities and clinical manifestations and the absence of specific symptoms. Artificial intelligence (AI), deep learning, and radiomics can enhance image interpretation, automate tumor segmentation, and identify clinically relevant imaging biomarkers.

Objectives: This narrative review aimed to summarize recent advances in AI- and radiomics-based imaging of colorectal, gastric, liver, and pancreatic cancers, focusing on tumor detection, segmentation, staging, histological classification, molecular biomarker prediction, and prognostic evaluation.

Materials and Methods: A structured, topic-focused search of PubMed/MEDLINE and Google Scholar was conducted without a lower publication-date limit and was last updated on 30 July 2026. English-language human studies evaluating AI- or radiomics-based computed tomography (CT), magnetic resonance imaging, or positron emission tomography applications were considered. Studies with external validation, multicenter designs, or larger cohorts, as well as systematic reviews and meta-analyses, were prioritized, and methodological robustness was appraised qualitatively.

Results: In colorectal cancer, AI-based methods have been developed for preoperative staging, prediction of Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation and microsatellite instability, lesion segmentation, and survival estimation. In gastric cancer, deep learning and radiomics have been applied to cancer detection on noncontrast CT, cross-center three-dimensional tumor segmentation, and preoperative histological type prediction. Liver cancer studies have mostly focused on automated lesion segmentation and radiomics-based prediction of clinically relevant biomarkers such as vessels encapsulating tumor clusters. In pancreatic cancer, multimodal and attention-based models have been used for three-dimensional and small-tumor segmentation and for prediagnostic detection of pancreatic ductal adenocarcinoma. Although several models achieved high area-under-the-curve or Dice values, direct comparisons are limited by heterogeneity in populations, imaging protocols, model architectures, performance metrics, and validation strategies. Much of the evidence remains retrospective and is often based on single-center or selected datasets with limited external validation.

Conclusions: AI and radiomics show potential as decision-support tools in GI cancer imaging. Before clinical application, prospective multicenter evaluation, standardized imaging and reporting practices, and improved accessibility and integration into routine radiology workflows are required.
Keywords
Gastrointestinal Cancer
Artificial Intelligence
Radiomics
Deep Learning
Early Detection
Oncologic Imaging
Subjects

Funding: This work was supported by the National Institute for Medical Research Development (Grant No. 4002286 to Amir Avan).

 

Data availability: Not applicable.


Conflicts of Interest: The authors have no relevant financial or non-financial interests to disclose.


Ethics Approval: Not applicable.



Consent for Publication: Not applicable.


Author Contributions Statement: N.H., M.A., and R.R. contributed to Writing – Original Draft and Writing – Review & Editing. N.H., M.A., and A.Y.K. were responsible for the conceptualization, literature search, and drafting of the manuscript. I.S.G. contributed to data curation and critical revision of the manuscript. A.G. was involved in methodological design and provided important intellectual content. A.A. and H.N. supervised the study, contributed to the study design, and performed critical revision of the manuscript for important intellectual content. All authors read and approved the final manuscript.


Declaration of AI-assisted technologies: During the preparation of this manuscript, ChatGPT (OpenAI) was used solely to improve grammar, language quality, clarity, readability, and section organization. All scientific content, numerical data, references, interpretations, and conclusions were independently reviewed and verified by the authors. The authors take full responsibility for the scientific integrity and final content of the manuscript.

Open Access Policy: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

1. Organization WH. ICD-10: international statistical classification of diseases and related health problems: tenth revision: World Health Organization; 2004.
 
2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2021;71(3):209-49.
https://doi.org/10.3322/caac.21660  
 
3. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2024;74(3):229-63.
https://doi.org/10.3322/caac.21834  
 
4. Van Cutsem E, Nordlinger B, Cervantes A, Group EGW. Advanced colorectal cancer: ESMO Clinical Practice Guidelines for treatment. Annals of oncology. 2010;21:v93-v7.
https://doi.org/10.1093/annonc/mdq222  
 
5. Siegel RL, Miller KD, Fedewa SA, Ahnen DJ, Meester RG, Barzi A, et al. Colorectal cancer statistics, 2017. CA: a cancer journal for clinicians. 2017;67(3):177-93.
https://doi.org/10.3322/caac.21395  
 
6. O'Connell JB, Maggard MA, Ko CY. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging. Journal of the National Cancer Institute. 2004;96(19):1420-5.
https://doi.org/10.1093/jnci/djh275  
 
7. Arnold M, Park JY, Camargo MC, Lunet N, Forman D, Soerjomataram I. Is gastric cancer becoming a rare disease? A global assessment of predicted incidence trends to 2035. Gut. 2020;69(5):823-9.
https://doi.org/10.1136/gutjnl-2019-320234  
 
8. Lin J-L, Lin J-X, Lin G-T, Huang C-M, Zheng C-H, Xie J-W, et al. Global incidence and mortality trends of gastric cancer and predicted mortality of gastric cancer by 2035. BMC Public Health. 2024;24(1):1763.
https://doi.org/10.1186/s12889-024-19104-6  
 
9. Massarweh NN, El-Serag HB. Epidemiology of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Cancer control. 2017;24(3):1073274817729245.
https://doi.org/10.1177/1073274817729245  
 
10. Delghandi S, Raoufinia R, Shahtahmasbi S, Meshkat Z, Gouklani H, Gholoobi A. An overview of occult hepatitis B infection (OBI) with emphasis on HBV vaccination. Heliyon. 2024;10(17).
https://doi.org/10.1016/j.heliyon.2024.e37097  
 
11. Raoufinia R, Arabnezhad A, Keyhanvar N, Abdyazdani N, Saburi E, Naseri N, et al. Leveraging stem cells to combat hepatitis: a comprehensive review of recent studies. Mol Biol Rep. 2024;51(1):459.
https://doi.org/10.1007/s11033-024-09391-y  
 
12. Marrero JA, Kulik LM, Sirlin CB, Zhu AX, Finn RS, Abecassis MM, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases. Hepatology. 2018;68(2):723-50.
https://doi.org/10.1002/hep.29913  
 
13. Farges O, Fuks D, Le Treut YP, Azoulay D, Laurent A, Bachellier P, et al. AJCC 7th edition of TNM staging accurately discriminates outcomes of patients with resectable intrahepatic cholangiocarcinoma: By the AFC-IHCC-2009 study group. Cancer. 2011;117(10):2170-7.
https://doi.org/10.1002/cncr.25712  
 
14. Ayuso J-R, Balyaniskova S, Beets-Tan RGH, Blazic I, Blomqvist L, Caruso D, et al. MRI to guide clinical management of rectal cancer: updated consensus recommendations from the European Society of Gastrointestinal and Abdominal Radiology (ESGAR)-PART I primary staging. European radiology. 2026;36(6):4592-607.
https://doi.org/10.1007/s00330-025-12274-w  
 
15. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88.
https://doi.org/10.1016/j.media.2017.07.005  
 
16. Raoufinia R, Alimoradi M, Karimoddini M, Najafizadeh F, Taromi N, Bijari R, et al. Advancements in Personalized Medicine for Leukemia: Integrating Genetic, Transcriptomic, and Artificial Intelligence Insights. Curr Pharm Des. 2025.
https://doi.org/10.2174/0113816128402252251005215721  
 
17. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563-77.
https://doi.org/10.1148/radiol.2015151169  
 
18. Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, et al. Introduction to Radiomics. J Nucl Med. 2020;61(4):488-95.
https://doi.org/10.2967/jnumed.118.222893  
 
19. Capello Ingold G, Martins da Fonseca J, Kolenda Zloić S, Verdan Moreira S, Kago Marole K, Finnegan E, et al. Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis. Abdom Radiol (NY). 2025;50(11):5148-61.
https://doi.org/10.1007/s00261-025-04981-1  
 
20. Jiang Y, Yuan H, Li Z, Ji X, Shen Q, Tuo J, et al. Global pattern and trends of colorectal cancer survival: a systematic review of population-based registration data. Cancer Biology & Medicine. 2022;19(2):175-86.  
 
21. Doubeni CA, Corley DA, Quinn VP, Jensen CD, Zauber AG, Goodman M, et al. Effectiveness of screening colonoscopy in reducing the risk of death from right and left colon cancer: a large community-based study. Gut. 2018;67(2):291-8.
https://doi.org/10.1136/gutjnl-2016-312712  
 
22. Al Zaabi A, Al Maashri A, Bourdoucen H, A. Al-Busafi S. Emerging Endorobotic and AI Technologies in Colorectal Cancer Screening: A Review of Design, Validation, and Translational Pathways. Diagnostics. 2026;16(3):421.
https://doi.org/10.3390/diagnostics16030421  
 
23. Atkin W, Dadswell E, Wooldrage K, Kralj-Hans I, von Wagner C, Edwards R, et al. SIGGAR investigators. Computed tomographic colonography versus colonoscopy for investigation of patients with symptoms suggestive of colorectal cancer (SIGGAR): a multicentre randomised trial. Lancet. 2013;381(9873):1194-202.
https://doi.org/10.1016/S0140-6736(12)62186-2  
 
24. Graser A, Stieber P, Nagel D, Schaefer C, Horst D, Becker CR, et al. Comparison of CT colonography, colonoscopy, sigmoidoscopy and faecal occult blood tests for the detection of advanced adenoma in an average risk population. Gut. 2009;58(2):241-8.
https://doi.org/10.1136/gut.2008.156448  
 
25. Johnson CD, Chen M-H, Toledano AY, Heiken JP, Dachman A, Kuo MD, et al. Accuracy of CT colonography for detection of large adenomas and cancers. New England Journal of Medicine. 2008;359(12):1207-17.
https://doi.org/10.1056/NEJMoa0800996  
 
26. Kim DH, Pickhardt PJ, Taylor AJ, Leung WK, Winter TC, Hinshaw JL, et al. CT colonography versus colonoscopy for the detection of advanced neoplasia. New England journal of medicine. 2007;357(14):1403-12.
https://doi.org/10.1056/NEJMoa070543  
 
27. Pickhardt PJ, Choi JR, Hwang I, Butler JA, Puckett ML, Hildebrandt HA, et al. Computed tomographic virtual colonoscopy to screen for colorectal neoplasia in asymptomatic adults. N Engl J Med. 2003;349(23):2191-200.
https://doi.org/10.1056/NEJMoa031618  
 
28. Näppi J, Yoshida H. Fully automated three-dimensional detection of polyps in fecal-tagging CT colonography. Academic radiology. 2007;14(3):287-300.
https://doi.org/10.1016/j.acra.2006.11.007  
 
29. Yoshida H, Dachman AH. Computer-aided diagnosis for CT colonography. Semin Ultrasound CT MR. 2004;25(5):419-31.
https://doi.org/10.1053/j.sult.2004.07.002  
 
30. Song K, Zhao Z, Ma Y, Wang J, Wu W, Qiang Y, et al. A multitask dual-stream attention network for the identification of KRAS mutation in colorectal cancer. Med Phys. 2022;49(1):254-70.
https://doi.org/10.1002/mp.15361  
 
31. Gheybi E, Hosseinzadeh P, Raoufinia R, Rostami M, Mahmoudi A, Soukhtanloo M. Contribution of Different Cell Signaling Pathways in Tumorigenesis. Iranian Journal of Blood and Cancer. 2026;18(2):37-46.  
 
32. Yang J, Cai D, Liu J, Zhuang Z, Zhao Y, Wang FA, et al. CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self-Supervised Learning. Adv Sci (Weinh). 2025;12(41):e07339.
https://doi.org/10.1002/advs.202407339  
 
33. Zhao Z, Wang H, Wu D, Zhu Q, Tan X, Hu S, et al. PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention. Med Biol Eng Comput. 2025;63(6):1627-47.
https://doi.org/10.1007/s11517-025-03292-3  
 
34. Jia LL, Zhao JX, Zhao LP, Tian JH, Huang G. Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis. Eur J Radiol. 2023;158:110640.
https://doi.org/10.1016/j.ejrad.2022.110640  
 
35. Zhang G, Guo Y, Yang Y, Li T, Gong B, Lou J, et al. Radiogenomic landscape of the hallmarks of cancer. Biomark Res. 2026;14(1).
https://doi.org/10.1186/s40364-026-00920-4  
 
36. Zhong J, Liu X, Lu J, Yang J, Zhang G, Mao S, et al. Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes. European radiology. 2025;35(3):1146-56.
https://doi.org/10.1007/s00330-024-11331-0  
 
37. Pickhardt PJ, Summers RM, Garrett JW, Krishnaraj A, Agarwal S, Dreyer KJ, et al. Opportunistic Screening: Radiology Scientific Expert Panel. Radiology. 2023;307(5):e222044.
https://doi.org/10.1148/radiol.222044  
 
38. Hu C, Xia Y, Zheng Z, Cao M, Zheng G, Chen S, et al. AI-based large-scale screening of gastric cancer from noncontrast CT imaging. Nat Med. 2025;31(9):3011-9.
https://doi.org/10.1038/s41591-025-03785-6  
 
39. Giandola T, Maino C, Marrapodi G, Ratti M, Ragusi M, Bigiogera V, et al. Imaging in Gastric Cancer: Current Practice and Future Perspectives. Diagnostics. 2023;13(7):1276.
https://doi.org/10.3390/diagnostics13071276  
 
40. Wang Z, Mo T-M, Tian L, Chen J-Q. Gastrin-17 combined with CEA, CA12-5 and CA19-9 improves the sensitivity for the diagnosis of gastric cancer. International Journal of General Medicine. 2021:8087-95.
https://doi.org/10.2147/IJGM.S335985  
 
41. Cai Q, Zhu C, Yuan Y, Feng Q, Feng Y, Hao Y, et al. Development and validation of a prediction rule for estimating gastric cancer risk in the Chinese high-risk population: a nationwide multicentre study. Gut. 2019;68(9):1576-87.
https://doi.org/10.1136/gutjnl-2018-317556  
 
42. Yuan N, Zhang Y, Lv K, Liu Y, Yang A, Hu P, et al. HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images. Cancer Imaging. 2024;24(1):63.
https://doi.org/10.1186/s40644-024-00711-w  
 
43. Huang H, Xu F, Chen Q, Hu H, Qi F, Zhao J. The value of CT-based radiomics nomogram in differential diagnosis of different histological types of gastric cancer. Phys Eng Sci Med. 2022;45(4):1063-71.
https://doi.org/10.1007/s13246-022-01170-y  
 
44. Grazzini G, Cozzi D, Flammia F, Grassi R, Agostini A, Belfiore MP, et al. Hepatic tumors: pitfall in diagnostic imaging. Acta Biomed. 2020;91(8-s):9-17.  
 
45. Song L, Wang H, Wang ZJ. Bridging the gap between 2D and 3D contexts in CT volume for liver and tumor segmentation. IEEE journal of biomedical and health informatics. 2021;25(9):3450-9.
https://doi.org/10.1109/JBHI.2021.3075752  
 
46. Di S, Zhao YQ, Liao M, Zhang F, Li X. TD-Net: A Hybrid End-to-End Network for Automatic Liver Tumor Segmentation From CT Images. IEEE J Biomed Health Inform. 2023;27(3):1163-72.
https://doi.org/10.1109/JBHI.2022.3181974  
 
47. Jiang J, Peng Y, Hou Q, Wang J. MDCF_Net: A Multi-dimensional hybrid network for liver and tumor segmentation from CT. Biocybernetics and Biomedical Engineering. 2023;43(2):494-506.
https://doi.org/10.1016/j.bbe.2023.04.004  
 
48. Xie J, Zhou J, Yang M, Xu L, Li T, Jia H, et al. Lesion segmentation method for multiple types of liver cancer based on balanced dice loss. Med Phys. 2025;52(5):3059-74.
https://doi.org/10.1002/mp.17624  
 
49. Singh R, Gupta S, Almogren A, Rehman AU, Bharany S, Altameem A, et al. FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17. Sci Rep. 2025;15(1):17697.
https://doi.org/10.1038/s41598-025-98427-9  
 
50. Wu J, Zhou S, Zuo S, Chen Y, Sun W, Luo J, et al. U-Net combined with multi-scale attention mechanism for liver segmentation in CT images. BMC Med Inform Decis Mak. 2021;21(1):283.
https://doi.org/10.1186/s12911-021-01649-w  
 
51. Hua X, Fu R, Yin Z. Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis. J Med Internet Res. 2026;28:e90931.
https://doi.org/10.2196/90931  
 
52. Yu Y, Cao L, Shen B, Du M, Gu W, Gu C, et al. Deep Learning Radiopathomics Models Based on Contrast-enhanced MRI and Pathologic Imaging for Predicting Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma. Radiol Imaging Cancer. 2025;7(2):e240213.
https://doi.org/10.1148/rycan.240213  
 
53. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: a cancer journal for clinicians. 2022;72(1):7-33.
https://doi.org/10.3322/caac.21708  
 
54. Walter FM, Mills K, Mendonça SC, Abel GA, Basu B, Carroll N, et al. Symptoms and patient factors associated with diagnostic intervals for pancreatic cancer (SYMPTOM pancreatic study): a prospective cohort study. The lancet Gastroenterology & hepatology. 2016;1(4):298-306.
https://doi.org/10.1016/S2468-1253(16)30079-6  
 
55. Khalaf N, El-Serag HB, Abrams HR, Thrift AP. Burden of pancreatic cancer: from epidemiology to practice. Clinical Gastroenterology and Hepatology. 2021;19(5):876-84.
https://doi.org/10.1016/j.cgh.2020.02.054  
 
56. Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nature reviews Gastroenterology & hepatology. 2021;18(7):493-502.
https://doi.org/10.1038/s41575-021-00457-x  
 
57. Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, et al. Artificial intelligence in pancreatic cancer. Theranostics. 2022;12(16):6931-54.
https://doi.org/10.7150/thno.77949  
 
58. Shi Z, Zhang R, Wei X, Yu C, Xie H, Hu Z, et al. LUNETR: Language-Infused UNETR for precise pancreatic tumor segmentation in 3D medical image. Neural Netw. 2025;187:107414.
https://doi.org/10.1016/j.neunet.2025.107414  
 
59. Du Y, Zuo X, Liu S, Cheng D, Li J, Sun M, et al. Segmentation of pancreatic tumors based on multi-scale convolution and channel attention mechanism in the encoder-decoder scheme. Med Phys. 2023;50(12):7764-78.
https://doi.org/10.1002/mp.16561  
 
60. Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Rajamohan N, Suman G, et al. Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis. Gastroenterology. 2022;163(5):1435-46.e3.
https://doi.org/10.1053/j.gastro.2022.06.066  
 
61. Thanya T, Jeslin T. DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging. Abdom Radiol (NY). 2025;50(9):4181-211.
https://doi.org/10.1007/s00261-025-04860-9  
 
62. Tejani AS, Klontzas ME, Gatti AA, Mongan JT, Moy L, Park SH, et al. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update. Radiol Artif Intell. 2024;6(4):e240300.  
 
63. Azarfar G, Naimimohasses S, Rambhatla S, Komorowski M, Ferro D, Lewis PR, et al. Responsible adoption of multimodal artificial intelligence in health care: promises and challenges. Lancet Digit Health. 2025;7(12):100917.
https://doi.org/10.1016/j.landig.2025.100917  
 
64. Verma S, Magazzù G, Eftekhari N, Lou T, Gilhespy A, Occhipinti A, et al. Cross-attention enables deep learning on limited omics-imaging-clinical data of 130 lung cancer patients. Cell Rep Methods. 2024;4(7):100817.
https://doi.org/10.1016/j.crmeth.2024.100817  
 
65. Jing HH, Hao D, Liu XJ, Cui MJ, Xue KJ, Wang DS, et al. Development and validation of a radiopathomics model for predicting liver metastases of colorectal cancer. European radiology. 2025;35(6):3409-17.
https://doi.org/10.1007/s00330-024-11198-1  
 
66. Santinha J, Pinto Dos Santos D, Laqua F, Visser JJ, Groot Lipman KBW, Dietzel M, et al. ESR Essentials: radiomics-practice recommendations by the European Society of Medical Imaging Informatics. European radiology. 2025;35(3):1122-32.
https://doi.org/10.1007/s00330-024-11093-9  
 
67. Whybra P, Zwanenburg A, Andrearczyk V, Schaer R, Apte AP, Ayotte A, et al. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology. 2024;310(2):e231319.
https://doi.org/10.1148/radiol.231319  
 
68. Jia W, Li H, Ali R, Shanbhogue KP, Masch WR, Aslam A, et al. Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data. J Imaging Inform Med. 2025;38(2):1016-27.
https://doi.org/10.1007/s10278-024-01253-0  
 
69. Haberl D, Spielvogel CP, Jiang Z, Orlhac F, Iommi D, Carrió I, et al. Multicenter PET image harmonization using generative adversarial networks. Eur J Nucl Med Mol Imaging. 2024;51(9):2532-46.
https://doi.org/10.1007/s00259-024-06708-8  
 
70. Fotopoulos D, Ladakis I, Filos D, Moreno-Sánchez PA, van Gils M, Chouvarda I. Explainable AI in Cancer Imaging: Scoping Review of Methods, Modalities, and Clinical Integration. J Med Internet Res. 2026;28:e80645.
https://doi.org/10.2196/80645  
 
71. Sivakumar R, Lue B, Kundu S. FDA Approval of Artificial Intelligence and Machine Learning Devices in Radiology: A Systematic Review. JAMA Netw Open. 2025;8(11):e2542338.
https://doi.org/10.1001/jamanetworkopen.2025.42338  
 
72. Zhang S, Li Y, Liu W, Chu Q, Wang S, Li J, et al. A decade of review in global regulation and research of artificial intelligence medical devices (2015-2025). Front Med (Lausanne). 2025;12:1630408.
https://doi.org/10.3389/fmed.2025.1630408  
 
73. Song J, Gao Y, Liu W, Sun X, Chen C, Wu IX. Effectiveness of Artificial Intelligence-Assisted Examination for Cancer Detection in Medical Imaging: A Systematic Review and Meta-Analysis. J Am Coll Radiol. 2026;23(4):586-98.
https://doi.org/10.1016/j.jacr.2025.11.003  
 
74. Priya S, Dhruba DD, Sorensen E, Aher PY, Narayanasamy S, Nagpal P, et al. ComBat Harmonization of Myocardial Radiomic Features Sensitive to Cardiac MRI Acquisition Parameters. Radiol Cardiothorac Imaging. 2023;5(4):e220312.
https://doi.org/10.1148/ryct.220312  
 
 
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