Research Article
No access
Published Online: 8 April 2024

Noninvasive Assessment of HER2 Expression Status in Gastric Cancer Using 18F-FDG Positron Emission Tomography/Computed Tomography-Based Radiomics: A Pilot Study

Publication: Cancer Biotherapy & Radiopharmaceuticals
Volume 39, Issue Number 3

Abstract

Purpose: Immunohistochemistry (IHC) is the main method to detect human epidermal growth factor receptor 2 (HER2) expression levels. However, IHC is invasive and cannot reflect HER2 expression status in real time. The aim of this study was to construct and verify three types of radiomics models based on 18F-fuorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging and to evaluate the predictive ability of these radiomics models for the expression status of HER2 in patients with gastric cancer (GC).
Patients and Methods: A total of 118 patients with GC were enrolled in this study. 18F-FDG PET/CT imaging was performed prior to surgery. The LIFEx software package was applied to extract PET and CT radiomics features. The minimum absolute contraction and selection operator (least absolute shrinkage and selection operator [LASSO]) algorithm was used to select the best radiomics features. Three machine learning methods, logistic regression (LR), support vector machine (SVM), and random forest (RF) models, were constructed and verified. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address data imbalance.
Results: In the training and test sets, the area under the curve (AUC) values of the LR, SVM, and RF models were 0.809, 0.761, 0.861 and 0.628, 0.993, 0.717, respectively, and the Brier scores were 0.118, 0.214, and 0.143, respectively. Among the three models, the LR and RF models exhibited extremely good prediction performance. The AUC values of the three models significantly improved after SMOTE balanced the data.
Conclusions: 18F-FDG PET/CT-based radiomics models, especially LR and RF models, demonstrate good performance in predicting HER2 expression status in patients with GC and can be used to preselect patients who may benefit from HER2-targeted therapy.

Get full access to this article

View all available purchase options and get full access to this article.

References

1. Erratum: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2020;70(4):313;
2. Sun D, Cao M, Li H, et al. Cancer burden and trends in China: A review and comparison with Japan and South Korea. Chin J Cancer Res 2020;32(2):129–139;
3. Shitara K, Bang YJ, Iwasa S, et al. Trastuzumab deruxtecan in previously treated HER2-positive gastric cancer. N Engl J Med 2020;382(25):2419–2430;
4. Gravalos C, Jimeno A. HER2 in gastric cancer: A new prognostic factor and a novel therapeutic target. Ann Oncol 2008;19(9):1523–1529;
5. Joshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin 2021;71(3):264–279;
6. Orditura M, Galizia G, Sforza V, et al. Treatment of gastric cancer. World J Gastroenterol 2014;20(7):1635–1649;
7. Baretton G, Dietel M, Gaiser T, et al. [HER2 testing in gastric cancer: Results of a meeting of German experts]. Pathologe 2016;37(4):361–366;
8. Abrahao-Machado LF, Scapulatempo-Neto C. HER2 testing in gastric cancer: An update. World J Gastroenterol 2016;22(19):4619–4625;
9. Levy I, Gralnek IM. Complications of diagnostic colonoscopy, upper endoscopy, and enteroscopy. Best Pract Res Clin Gastroenterol 2016;30(5):705–718;
10. Le Berre C, Sandborn WJ, Aridhi S, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020;158(1):76.e2–94.e2;
11. Koh WJ, Abu-Rustum NR, Bean S, et al. Uterine neoplasms, version 1.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2018;16(2):170–199;
12. Wang FH, Zhang XT, Li YF, et al. The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2021. Cancer Commun (Lond) 2021;41(8):747–795;
13. Elemento O, Leslie C, Lundin J, et al. Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer 2021;21(12):747–752;
14. Jiang Y, Yuan Q, Lv W, et al. Radiomic signature of (18)F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics 2018;8(21):5915–5928;
15. Nioche C, Orlhac F, Boughdad S, et al. LIFEx: A freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 2018;78(16):4786–4789;
16. Han Y, Ma Y, Wu Z, et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images. Eur J Nucl Med Mol Imaging 2021;48(2):350–360;
17. Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 2020;295(2):328–338;
18. Nakamura M, Kajiwara Y, Otsuka A, et al. LVQ-SMOTE—Learning Vector Quantization based Synthetic Minority Over-sampling Technique for biomedical data. BioData Min 2013;6(1):16;
19. Yan Y, Lu L, Liu C, et al. HER2/neu over-expression predicts poor outcome in early gastric cancer without lymph node metastasis. Clin Res Hepatol Gastroenterol 2015;39(1):121–126;
20. Shinohara H, Morita S, Kawai M, et al. Expression of HER2 in human gastric cancer cells directly correlates with antitumor activity of a recombinant disulfide-stabilized anti-HER2 immunotoxin. J Surg Res 2002;102(2):169–177;
21. Ciesielski M, Szajewski M, Peksa R, et al. The relationship between HER2 overexpression and angiogenesis in gastric cancer. Medicine (Baltimore) 2018;97(42):e12854;
22. Pagni F, Zannella S, Ronchi S, et al. HER2 status of gastric carcinoma and corresponding lymph node metastasis. Pathol Oncol Res 2013;19(1):103–109;
23. 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):172–182;
24. Avanzo M, Wei L, Stancanello J, et al. Machine and deep learning methods for radiomics. Med Phys 2020;47(5):e185–e202;
25. Currie G, Hawk KE, Rohren E, et al. Machine learning and deep learning in medical imaging: Intelligent imaging. J Med Imaging Radiat Sci 2019;50(4):477–487;
26. Zhang Y, Yuan N, Zhang Z, et al. Unsupervised domain selective graph convolutional network for preoperative prediction of lymph node metastasis in gastric cancer. Med Image Anal 2022;79:102467;
27. Li Y, Cheng Z, Gevaert O, et al. A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer. Chin J Cancer Res 2020;32(1):62–71;
28. Wang S, Chen Y, Zhang H, et al. The value of predicting human epidermal growth factor receptor 2 status in adenocarcinoma of the esophagogastric junction on CT-based radiomics nomogram. Front Oncol 2021;11:707686;
29. Ma T, Cui J, Wang L, et al. A multiphase contrast-enhanced CT radiomics model for prediction of human epidermal growth factor receptor 2 status in advanced gastric cancer. Front Genet 2022;13:968027;
30. Wang Y, Yu Y, Han W, et al. CT radiomics for distinction of human epidermal growth factor receptor 2 negative gastric cancer. Acad Radiol 2021;28(3):e86–e92;
31. Chen R, Zhou X, Liu J, et al. Relationship between 18F-FDG PET/CT findings and HER2 expression in gastric cancer. J Nucl Med 2016;57(7):1040–1044;
32. Bai L, Guo CH, Zhao Y, et al. SUVmax of 18F-FDG PET/CT correlates to expression of major chemotherapy-related tumor markers and serum tumor markers in gastric adenocarcinoma patients. Oncol Rep 2017;37(6):3433–3440;
33. Li C, Yu L, Jiang Y, et al. The histogram analysis of intravoxel incoherent motion-kurtosis model in the diagnosis and grading of prostate cancer—A preliminary study. Front Oncol 2021;11:604428;
34. Wagner F, Hakami YA, Warnock G, et al. Comparison of contrast-enhanced CT and [(18)F]FDG PET/CT analysis using kurtosis and skewness in patients with primary colorectal cancer. Mol Imaging Biol 2017;19(5):795–803;
35. Lee HS, Oh JS, Park YS, et al. Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via (18)F-FDG PET/CT. Ann Nucl Med 2016;30(4):309–319;
36. Kunimatsu A, Kunimatsu N, Kamiya K, et al. Comparison between glioblastoma and primary central nervous system lymphoma using MR image-based texture analysis. Magn Reson Med Sci 2018;17(1):50–57;
37. Zhang J, Zhao X, Zhao Y, et al. Value of pre-therapy (18)F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging 2020;47(5):1137–1146;
38. Hu Y, Zhao X, Zhang J, et al. Value of (18)F-FDG PET/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis. Eur J Nucl Med Mol Imaging 2021;48(1):231–240;

Information & Authors

Information

Published In

cover image Cancer Biotherapy and Radiopharmaceuticals
Cancer Biotherapy & Radiopharmaceuticals
Volume 39Issue Number 3April 2024
Pages: 169 - 177
PubMed: 38193811

History

Published online: 8 April 2024
Published in print: April 2024
Published ahead of print: 9 January 2024

Permissions

Request permissions for this article.

Topics

Authors

Affiliations

Xiaojing Jiang*
Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Jianfang Wang
Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Zhaoqi Zhang
Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Xiaolin Chen
Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Jingmian Zhang [email protected]
Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China.
Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, China.

Notes

*
Both these authors contributed equally to this work.
This article has been preprinted (Research Square; https://doi.org/10.21203/rs.3.rs-2962066/v1).
Address correspondence to: Jingmian Zhang; Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University; 12 Jiankang Road, Shijiazhuang 050011, Hebei, China [email protected]

Authors' Contributions

X.J. and T.L. were involved in the drafting and final editing of the report. They contributed equally to this work and share first authorship; Z.Z. and J.W. segmented GC on PET/CT scan; X.C. conducted statistical analysis; J.Z. and X.Z. designed the study and supervised the entire project and work. All the authors critically reviewed and approved the final version of the article.

Disclosure Statement

There are no existing financial conflicts.

Funding Information

This work was supported by the Hebei Provincial Department of Science and Technology in China (No. 20377728D), the 2021 Government Funded Clinical Medicine Excellent Talent Training Project of Hebei Provincial Department of Finance in China [(2021)379], the Health Commission of Hebei Province in China (20160227).

Metrics & Citations

Metrics

Citations

Export citation

Select the format you want to export the citations of this publication.

View Options

Access content

To read the fulltext, please use one of the options below to sign in or purchase access.

Society Access

If you are a member of a society that has access to this content please log in via your society website and then return to this publication.

Restore your content access

Enter your email address to restore your content access:

Note: This functionality works only for purchases done as a guest. If you already have an account, log in to access the content to which you are entitled.

View options

PDF/EPUB

View PDF/EPUB

Full Text

View Full Text

Figures

Tables

Media

Share

Share

Copy the content Link

Share on social media

Back to Top