Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Systematic Review Article

Artificial Intelligence in Breast Cancer: A Systematic Review on PET Imaging Clinical Applications

Author(s): Pierpaolo Alongi*, Guido Rovera, Federica Stracuzzi, Cristina Elena Popescu, Fabio Minutoli, Gaspare Arnone, Sergio Baldari, Désirée Deandreis and Federico Caobelli

Volume 19, Issue 8, 2023

Published on: 22 February, 2023

Article ID: e260123213158 Pages: 12

DOI: 10.2174/1573405619666230126093806

Price: $65

Abstract

Background: 18F-FDG PET/CT imaging represents the most important functional imaging method in oncology. European Society of Medical Oncology and the National Comprehensive Cancer Network guidelines defined a crucial role of 18F-FDG PET/CT imaging for local/locally advanced breast cancer. The application of artificial intelligence on PET images might potentially contributes in the field of precision medicine.

Objective: This review aims to summarize the clinical indications and limitations of PET imaging for comprehensive artificial intelligence in relation to breast cancer subtype, hormone receptor status, proliferation rate, and lymphonodal (LN)/distant metastatic spread, based on recent literature.

Methods: A literature search of the Pubmed/Scopus/Google Scholar/Cochrane/EMBASE databases was carried out, searching for articles on the use of artificial intelligence and PET in breast tumors. The search was updated from January 2010 to October 2021 and was limited to original articles published in English and about humans. A combination of the search terms "artificial intelligence", “breast cancer”, “breast tumor”, “PET”, “Positron emission tomography”, “PET/CT”, “PET/MRI”, “radiomic”," texture analysis", “machine learning”, “deep learning” was used.

Results: Twenty-three articles were selected following the PRISMA criteria from 139 records obtained from the Pubmed/Scopus/Google Scholar/Cochrane/EMBASE databases according to our research strategy. The QUADAS of 30 full-text articles assessed reported seven articles that were excluded for not being relevant to population and outcomes and/or for lower level of evidence. The majority of papers were at low risk of bias and applicability. The articles were divided per topic, such as the value of PET in the staging and re-staging of breast cancer patients, including new radiopharmaceuticals and simultaneous PET/MRI.

Conclusion: Despite the current role of AI in this field remains still undefined, several applications for PET/CT imaging are under development, with some preliminary interesting results particularly focused on the staging phase that might be clinically translated after further validation studies.

Keywords: Breast cancer, radiomics, artificial intelligence, positron emission tomography, nuclear medicine, cancer patients.

Graphical Abstract

[1]
Ou X, Zhang J, Wang J, et al. Radiomics based on 18 F‐FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine‐learning approach: A preliminary study. Cancer Med 2020; 9(2): 496-506.
[http://dx.doi.org/10.1002/cam4.2711] [PMID: 31769230]
[2]
Acar E, Turgut B. Yiğit S, Kaya G. Comparison of the volumetric and radiomics findings of 18F-FDG PET/CT images with immunohistochemical prognostic factors in local/locally advanced breast cancer. Nucl Med Commun 2019; 40(7): 764-72.
[http://dx.doi.org/10.1097/MNM.0000000000001019] [PMID: 30925542]
[3]
Krajnc D, Papp L, Nakuz TS, et al. Breast tumor characterization using [18F]FDG-PET/CT imaging combined with data preprocessing and radiomics. Cancers (Basel) 2021; 13(6): 1249.
[http://dx.doi.org/10.3390/cancers13061249] [PMID: 33809057]
[4]
Aide N, Salomon T, Blanc-Fournier C, Grellard JM, Levy C, Lasnon C. Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics. EJNMMI Res 2018; 8(1): 114.
[http://dx.doi.org/10.1186/s13550-018-0466-5] [PMID: 30594961]
[5]
Li Z, Kitajima K, Hirata K, et al. Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer. EJNMMI Res 2021; 11(1): 10.
[http://dx.doi.org/10.1186/s13550-021-00751-4] [PMID: 33492478]
[6]
Umutlu L, Kirchner J, Bruckmann NM, et al. Multiparametric integrated 18F-FDG PET/MRI-based radiomics for breast cancer phenotyping and tumor decoding. Cancers (Basel) 2021; 13(12): 2928.
[http://dx.doi.org/10.3390/cancers13122928] [PMID: 34208197]
[7]
Li P, Wang X, Xu C, et al. 18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients. Eur J Nucl Med Mol Imaging 2020; 47(5): 1116-26.
[http://dx.doi.org/10.1007/s00259-020-04684-3] [PMID: 31982990]
[8]
Shamseer L, Moher D, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 2015; 349(jan02 1): g7647.
[http://dx.doi.org/10.1136/bmj.g7647] [PMID: 25555855]
[9]
Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155(8): 529-36.
[http://dx.doi.org/10.7326/0003-4819-155-8-201110180-00009] [PMID: 22007046]
[10]
Liu J, Bian H, Zhang Y, et al. Molecular subtype classification of breast cancer using established radiomic signature models based on 18F-FDG PET/CT images. Frontiers in Bioscience-Landmark 2021; 26(9): 475-84.
[http://dx.doi.org/10.52586/4960] [PMID: 34590460]
[11]
Antunovic L, Gallivanone F, Sollini M, et al. [18F]FDG PET/CT features for the molecular characterization of primary breast tumors. Eur J Nucl Med Mol Imaging 2017; 44(12): 1945-54.
[http://dx.doi.org/10.1007/s00259-017-3770-9] [PMID: 28711994]
[12]
Boughdad S, Nioche C, Orlhac F, Jehl L, Champion L, Buvat I. Influence of age on radiomic features in 18F-FDG PET in normal breast tissue and in breast cancer tumors. Oncotarget 2018; 9(56): 30855-68.
[http://dx.doi.org/10.18632/oncotarget.25762] [PMID: 30112113]
[13]
Ou X, Wang J, Zhou R, et al. Ability of 18 F-FDG PET/CT radiomic features to distinguish breast carcinoma from breast lymphoma. Contrast Media Mol Imaging 2019; 2019: 1-9.
[http://dx.doi.org/10.1155/2019/4507694] [PMID: 30930700]
[14]
Schiano C, Franzese M, Pane K, et al. Hybrid 18F-FDG-PET/MRI measurement of standardized uptake value coupled with Yin Yang 1 signature in metastatic breast cancer. A Preliminary Study. Cancers (Basel) 2019; 11(10): 1444.
[http://dx.doi.org/10.3390/cancers11101444] [PMID: 31561604]
[15]
Song BI. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer 2021; 28(3): 664-71.
[http://dx.doi.org/10.1007/s12282-020-01202-z] [PMID: 33454875]
[16]
Ha S, Park S, Bang JI, Kim EK, Lee HY. Metabolic Radiomics for Pretreatment 18F-FDG PET/CT to Characterize Locally Advanced Breast Cancer: Histopathologic Characteristics, Response to Neoadjuvant Chemotherapy, and Prognosis. Sci Rep 2017; 7(1): 1556.
[http://dx.doi.org/10.1038/s41598-017-01524-7] [PMID: 28484211]
[17]
Lemarignier C, Martineau A, Teixeira L, et al. Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed with 18F-FDG PET in a large cohort of oestrogen receptor-positive breast cancer patients. Eur J Nucl Med Mol Imaging 2017; 44(7): 1145-54.
[http://dx.doi.org/10.1007/s00259-017-3641-4] [PMID: 28188325]
[18]
Weber M, Kersting D, Umutlu L, et al. 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 2021; 48(10): 3141-50.
[http://dx.doi.org/10.1007/s00259-021-05270-x] [PMID: 33674891]
[19]
Romeo V, Clauser P, Rasul S, et al. AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging 2022; 49(2): 596-608.
[http://dx.doi.org/10.1007/s00259-021-05492-z] [PMID: 34374796]
[20]
Huang S, Franc BL, Harnish RJ, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ Breast Cancer 2018; 4(1): 24.
[http://dx.doi.org/10.1038/s41523-018-0078-2] [PMID: 30131973]
[21]
Orlhac F, Boughdad S, Philippe C, et al. A postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med 2018; 59(8): 1321-8.
[http://dx.doi.org/10.2967/jnumed.117.199935] [PMID: 29301932]
[22]
Yoon HJ, Kim Y, Chung J, Kim BS. Predicting neo‐adjuvant chemotherapy response and progression‐free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F‐18 FDG PET/CT and diffusion‐weighted MR imaging. Breast J 2019; 25(3): 373-80.
[http://dx.doi.org/10.1111/tbj.13032] [PMID: 29602210]
[23]
Antunovic L, De Sanctis R, Cozzi L, et al. PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 2019; 46(7): 1468-77.
[http://dx.doi.org/10.1007/s00259-019-04313-8] [PMID: 30915523]
[24]
Choi JH, Kim HA, Kim W, et al. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Sci Rep 2020; 10(1): 21149.
[http://dx.doi.org/10.1038/s41598-020-77875-5] [PMID: 33273490]
[25]
Fantini L, Belli ML, Azzali I, et al. Exploratory Analysis of 18F-3′-deoxy-3′-fluorothymidine (18F-FLT) PET/CT-Based Radiomics for the Early Evaluation of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer. Front Oncol 2021; 11: 601053.
[http://dx.doi.org/10.3389/fonc.2021.601053] [PMID: 34249671]
[26]
Caobelli F. Artificial intelligence in medical imaging: Game over for radiologists? Eur J Radiol 2020; 126: 108940.
[http://dx.doi.org/10.1016/j.ejrad.2020.108940] [PMID: 32171909]
[27]
Popescu C, Laudicella R, Baldari S, et al. PET-based artificial intelligence applications in cardiac nuclear medicine. Swiss Med Wkly 2021; 151(3-4): w30123.
[http://dx.doi.org/10.4414/smw.2022.w30123] [PMID: 35098599]
[28]
Urso L, Quartuccio N, Caracciolo M, et al. Impact on the long-term prognosis of FDG PET/CT in luminal-A and luminal-B breast cancer. Nucl Med Commun 2022; 43(2): 212-9.
[http://dx.doi.org/10.1097/MNM.0000000000001500] [PMID: 35022378]
[29]
Evangelista L, Cervino AR, Ghiotto C, et al. Could semiquantitative FDG analysis add information to the prognosis in patients with stage II/III breast cancer undergoing neoadjuvant treatment? Eur J Nucl Med Mol Imaging 2015; 42(11): 1648-55.
[http://dx.doi.org/10.1007/s00259-015-3088-4] [PMID: 26025244]
[30]
Uribe CF, Mathotaarachchi S, Gaudet V, et al. Machine learning in nuclear medicine: Part 1-introduction. J Nucl Med 2019; 60(4): 451-8.
[http://dx.doi.org/10.2967/jnumed.118.223495] [PMID: 30733322]
[31]
Chen Y, Wang Z, Yin G, et al. Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning. Ann Nucl Med 2022; 36(2): 172-82.
[http://dx.doi.org/10.1007/s12149-021-01688-3] [PMID: 34716873]
[32]
Fang YHD, Lin CY, Shih MJ, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. BioMed Res Int 2014; 2014: 1-9.
[http://dx.doi.org/10.1155/2014/248505] [PMID: 24757667]
[33]
Cheng NM, Fang YHD, Lee L, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging 2015; 42(3): 419-28.
[http://dx.doi.org/10.1007/s00259-014-2933-1] [PMID: 25339524]
[34]
Soussan M, Orlhac F, Boubaya M, et al. Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. PLoS One 2014; 9(4): e94017.
[http://dx.doi.org/10.1371/journal.pone.0094017] [PMID: 24722644]
[35]
Xu F, Zhu C, Tang W, et al. Predicting axillary lymph node metastasis in early breast cancer using deep learning on primary tumor biopsy slides. Front Oncol 2021; 11: 759007.
[http://dx.doi.org/10.3389/fonc.2021.759007] [PMID: 34722313]

Rights & Permissions Print Cite
Article Metrics
39
2
© 2024 Bentham Science Publishers | Privacy Policy