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Correlation study between 18F-FDG PET/MR imaging radiomic features and PD-L1 expression in cervical cancer
LI Wang  LI Langjun  LIU Zhuonan  SUN Hongzan 

Cite this article as: LI W, LI L J, LIU Z N, et al. Correlation study between 18F-FDG PET/MR imaging radiomic features and PD-L1 expression in cervical cancer[J]. Chin J Magn Reson Imaging, 2024, 15(7): 32-38, 45. DOI:10.12015/issn.1674-8034.2024.07.006.


[Abstract] Objective To explore the correlation between 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/MR radiomic features and the expression of programmed death-ligand 1 (PD-L1) in cervical cancer.Materials and Methods A retrospective analysis was conducted on 26 cervical cancer patients who underwent 18F-FDG PET/MR scans at Shengjing Hospital, China Medical University, from May 2017 to July 2023. Regions of interest (ROIs) were delineated on the images of the primary lesions in PET, T1WI, and T2WI. Each cross-sectional image containing an ROI for each patient was treated as a sample. Based on sampling and staining of surgical specimens, the samples were divided into a positive group (n=233, 73.97%) and a negative group (n=82, 26.03%). Radiomic features were extracted from the images using first-order statistics. Independent sample t-tests or Mann-Whitney U tests were used to compare the differences in feature parameters between the two groups. The correlation between image feature parameters and PD-L1 expression was analyzed. The samples were randomly divided into training and testing sets in a 7∶3 ratio. Radiomic features with statistically significant differences were used as parameters to establish PET radiomic models, MR radiomic models, and PET/MR combined models through logistic regression. The diagnostic performance of each model for PD-L1 expression was evaluated using the area under the curve (AUC) analysis of receiver operating characteristic (ROC) curves.Results The 15 first-order statistical features of PET images, including 10Percentile (P<0.01), 90Percentile (P<0.01), Energy (P<0.01), Interquartile Range (P<0.01), and others, exhibit a strong correlation with PD-L1 expression. Similarly, the T1WI image parameters, such as 10Percentile (P<0.01), 90Percentile (P<0.05), Maximum (P<0.05), Mean (P<0.01), and nine other first-order statistical features, show a strong correlation with PD-L1 expression. Additionally, the T2WI image parameters, including Entropy (P<0.05), Skewness (P<0.01), Energy (P<0.05), Interquartile Range (P<0.01), and eight other first-order statistical features, demonstrate a strong correlation with PD-L1 expression.Conclusions 18F-FDG PET/MR radiomic features show a strong correlation with the differential expression of PD-L1 in cervical cancer. The PET/MR radiomic model demonstrates better performance in predicting PD-L1 expression, providing a potential clinical tool for assessing PD-L1 expression in cervical cancer patients to optimize individualized treatment plans and improve patient prognosis.
[Keywords] gynecologic oncology；cervical cancer；programmed death-ligand 1；radiomics；magnetic resonance imaging；positron emission tomography

LI Wang   LI Langjun   LIU Zhuonan   SUN Hongzan*  

Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110022, China

Corresponding author: SUN H Z, E-mail: sunhongzan@126.com

Conflicts of interest   None.

Received  2024-01-16
Accepted  2024-04-17
DOI: 10.12015/issn.1674-8034.2024.07.006
Cite this article as: LI W, LI L J, LIU Z N, et al. Correlation study between 18F-FDG PET/MR imaging radiomic features and PD-L1 expression in cervical cancer[J]. Chin J Magn Reson Imaging, 2024, 15(7): 32-38, 45. DOI:10.12015/issn.1674-8034.2024.07.006.

[1]
BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424. DOI: 10.3322/caac.21492.
[2]
TABATABAEI F S, SAEEDIAN A, AZIMI A, et al. Evaluation of survival rate and associated factors in patients with cervical cancer: a retrospective cohort study[J/OL]. J Res Health Sci, 2022, 22(2): e00552 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/36511264/. DOI: 10.34172/jrhs.2022.87.
[3]
WANG R Z, ZHANG Y, SHAN F P. PD-L1: can it be a biomarker for the prognosis or a promising therapeutic target in cervical cancer?[J/OL]. Int Immunopharmacol, 2022, 103: 108484 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/34954558/. DOI: 10.1016/j.intimp.2021.108484.
[4]
ROTMAN J, DEN OTTER L A S, BLEEKER M C G, et al. PD-L1 and PD-L2 expression in cervical cancer: regulation and biomarker potential[J/OL]. Front Immunol, 2020, 11: 596825 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/33424844/. DOI: 10.3389/fimmu.2020.596825.
[5]
AI L Y, XU A T, XU J. Roles of PD-1/PD-L1 pathway: signaling, cancer, and beyond[J]. Adv Exp Med Biol, 2020, 1248: 33-59. DOI: 10.1007/978-981-15-3266-5_3.
[6]
HUI E F, CHEUNG J, ZHU J, et al. T cell costimulatory receptor CD28 is a primary target for PD-1-mediated inhibition[J]. Science, 2017, 355(6332): 1428-1433. DOI: 10.1126/science.aaf1292.
[7]
HUANG W J, LIU J W, XU K, et al. PD-1/PD-L1 inhibitors for advanced or metastatic cervical cancer: from bench to bed[J/OL]. Front Oncol, 2022, 12: 849352 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/36313730/. DOI: 10.3389/fonc.2022.849352.
[8]
YU C C, YUE X T, ZHANG X L, et al. Research progress on the application of PD-1/PD-L1 in tumor immunotherapy[J]. J Jilin Norm Univ Nat Sci Ed, 2022, 43(3): 112-118. DOI: 10.16862/j.cnki.issn1674-3873.2022.03.015.
[9]
JOU J, KATO S, MIYASHITA H, et al. Cancer-immunity marker RNA expression levels across gynecologic cancers: implications for immunotherapy[J]. Mol Cancer Ther, 2023, 22(11): 1352-1362. DOI: 10.1158/1535-7163.MCT-23-0270.
[10]
VRANIC S, GATALICA Z. PD-L1 testing by immunohistochemistry in immuno-oncology[J]. Biomol Biomed, 2023, 23(1): 15-25. DOI: 10.17305/bjbms.2022.7953.
[11]
KAIRA K, KUJI I, KAGAMU H. Value of 18F-FDG-PET to predict PD-L1 expression and outcomes of PD-1 inhibition therapy in human cancers[J/OL]. Cancer Imaging, 2021, 21(1): 11 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/33441183/. DOI: 10.1186/s40644-021-00381-y.
[12]
KEEK S A, LEIJENAAR R T, JOCHEMS A, et al. A review on radiomics and the future of theranostics for patient selection in precision medicine[J/OL]. Br J Radiol, 2018, 91(1091): 20170926 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/29947266/. DOI: 10.1259/bjr.20170926.
[13]
MONACO L, BERNARDI E D, BONO F, et al. The "digital biopsy" in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of[18F]FDG PET/CT[J]. Eur J Nucl Med Mol Imaging, 2022, 49(10): 3401-3411. DOI: 10.1007/s00259-022-05783-z.
[14]
WU Z J, LIN Q, WANG H B, et al. Intratumoral and peritumoral radiomics based on preoperative MRI for evaluation of programmed cell death ligand-1 expression in breast cancer[J/OL]. J Magn Reson Imaging, 2023 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/37916918/. DOI: 10.1002/jmri.29109.
[15]
VISWANATHAN C, FARIA S, DEVINE C, et al. 18F]-2-fluoro- 2-deoxy-D-glucose-PET assessment of cervical cancer[J]. PET Clin, 2018, 13(2): 165-177. DOI: 10.1016/j.cpet.2017.11.004.
[16]
LI H Y, XIA X T, LAN X L, et al. Visualization of tumor microenvironment and PET molecular imaging[J]. Chin J Nucl Med Mol Imag, 2019, 39(3): 174-177. DOI: 10.3760/cma.j.issn.2095-2848.2019.03.014.
[17]
BOGDANOVIC B, SOLARI E L, ASIARES A V, et al. PET/MR technology: advancement and challenges[J]. Semin Nucl Med, 2022, 52(3): 340-355. DOI: 10.1053/j.semnuclmed.2021.11.014.
[18]
VAN GRIETHUYSEN J J M, FEDOROV A, PARMAR C, et al. Computational radiomics system to decode the radiographic phenotype[J/OL]. Cancer Res, 2017, 77(21): e104-e107 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/29092951/. DOI: 10.1158/0008-5472.CAN-17-0339.
[19]
YEONG J, LUM H Y J, TEO C B, et al. Choice of PD-L1 immunohistochemistry assay influences clinical eligibility for gastric cancer immunotherapy[J]. Gastric Cancer, 2022, 25(4): 741-750. DOI: 10.1007/s10120-022-01301-0.
[20]
ZHONG L, YIN R T. Interpretation of "cervical cancer immunotherapy: facts and hopes"[J]. West China J Pharm Sci, 2022, 37(5): 594-600. DOI: 10.13375/j.cnki.wcjps.2022.05.026.
[21]
BOSE C K. Balstilimab and other immunotherapy for recurrent and metastatic cervical cancer[J/OL]. Med Oncol, 2022, 39(4): 47 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/35092506/. DOI: 10.1007/s12032-022-01646-7.
[22]
CHENG K, SHI Y, LIU D L, et al. Research progress of PD-1/PD-L1 inhibitors in the treatment of gynecological cancers[J]. J Cancer Contr Treat, 2020, 33(10): 903-908. DOI: 10.3969/j.issn.1674-0904.2020.10.014.
[23]
HASHIMOTO K, MURAKAMI Y, OMURA K, et al. Prediction of tumor PD-L1 expression in resectable non-small cell lung cancer by machine learning models based on clinical and radiological features: performance comparison with preoperative biopsy[J/OL]. Clin Lung Cancer, 2024, 25(1): e26-e34 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/37673781/. DOI: 10.1016/j.cllc.2023.08.010.
[24]
BEZZI C, ZAMBELLA E, GHEZZO S, et al. 18F-FDG PET/MRI in endometrial cancer: systematic review and meta-analysis[J]. Clin Transl Imag, 2022, 10(1): 45-58. DOI: 10.1007/s40336-021-00463-8.
[25]
LI Y, QIU L H. Current status and progresses in MRI radiomics of cervical cancer[J]. Chin J Med Imag Technol, 2023, 39(9): 1407-1410. DOI: 10.13929/j.issn.1003-3289.2023.09.029.
[26]
JI J F, PANG W Q, SONG J L, et al. Retrospective analysis of the predictive value of 18F-FDG PET/CT metabolic parameters for PD-L1 expression in cervical cancer[J/OL]. Diagnostics, 2023, 13(6): 1015 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/36980323/. DOI: 10.3390/diagnostics13061015.
[27]
ALANEZI S T, SULLIVAN F, KLEEFELD C, et al. Quantifying tumor heterogeneity from multiparametric magnetic resonance imaging of prostate using texture analysis[J/OL]. Cancers, 2022, 14(7): 1631 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/35406403/. DOI: 10.3390/cancers14071631.
[28]
ZHANG G Y, ZHANG Z S, PEI Y L, et al. Biological and clinical significance of radiomics features obtained from magnetic resonance imaging preceding pre-carbon ion radiotherapy in prostate cancer based on radiometabolomics[J/OL]. Front Endocrinol, 2023, 14: 1272806 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/38027108/. DOI: 10.3389/fendo.2023.1272806.
[29]
EHLERDING E B, LAN X L, CAI W B. Predicting PD-1/PD-L1 status in bladder cancer with 18F-FDG PET?[J]. Eur J Nucl Med Mol Imag, 2019, 46(4): 791-793. DOI: 10.1007/s00259-018-4224-8.
[30]
ZHANG J, XUE H D, LI S, et al. Value of magnetic resonance imaging in parametrial invasion of cervical cancer[J]. Chin J Magn Reson Imag, 2014, 5(4): 287-290. DOI: 10.3969/j.issn.1674-8034.2014.04.010.
[31]
COSTA G, CAVINATO L, FIZ F, et al. Mapping tumor heterogeneity via local entropy assessment: making biomarkers visible[J]. J Digit Imaging, 2023, 36(3): 1038-1048. DOI: 10.1007/s10278-023-00799-9.
[32]
AMINI B, LIU C L, DURAN-SIERRA E, et al. Role of apparent diffusion coefficient map-based first- and high-order radiomic features for the discrimination of sacral chordomas and chondrosarcomas with overlapping conventional imaging features[J/OL]. JCO Precis Oncol, 2023, 7: e2300243 [2024-01-10]. https://pubmed.ncbi.nlm.nih.gov/38127828/. DOI: 10.1200/PO.23.00243.
[33]
GRAHOVAC M, SPIELVOGEL C P, KRAJNC D, et al. Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts[J]. Eur J Nucl Med Mol Imaging, 2023, 50(6): 1607-1620. DOI: 10.1007/s00259-023-06127-1.
[34]
WAGNER-LARSEN K S, HODNELAND E, FASMER K E, et al. MRI-based radiomic signatures for pretreatment prognostication in cervical cancer[J/OL]. Cancer Med, 2023, 12(20): 20251-20265. DOI: 10.1002/cam4.6526.
[35]
YE L D, LESLIE C, JACQUES A, et al. Programmed death ligand-1 expression in non-small cell lung cancer in a Western Australian population and correlation with clinicopathologic features[J]. Mod Pathol, 2019, 32(4): 524-531. DOI: 10.1038/s41379-018-0173-9.

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