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Radiomic Signature Of 18F Fluorodeoxyglucose PET/CT For Prediction Of Gastric Cancer Survival And Chemotherapeutic Benefits

Y. Jiang, Qingyu Yuan, Wenbing Lv, S. Xi, W. Huang, Z. Sun, Hao Chen, Liying Zhao, W. Liu, Y. Hu, L. Lu, J. Ma, T. Li, Jiang Yu, Q. Wang, G. Li
Published 2018 · Medicine

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We aimed to evaluate whether radiomic feature-based fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits. Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict disease-free survival (DFS) and overall survival (OS) and were validated in the validation cohort. A radiomics nomogram with the radiomic signature incorporated was constructed to demonstrate the incremental value of the radiomic signature to the TNM staging system for individualized survival estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. The performance was assessed with concordance index (C-index) and integrated Brier scores. Results: Significant differences were found between the high- and low-radiomic score (Rad-score) patients in 5-year DFS and OS in training and validation cohorts. Multivariate analysis revealed that the Rad-score was an independent prognostic factor. Incorporating the Rad-score into the radiomics-based nomogram resulted in better performance (C-index: DFS, 0.800; OS, 0.786; in the training cohort) than TNM staging system and clinicopathologic nomogram. Further analysis revealed that patients with higher Rad-scores were prone to benefit from chemotherapy. Conclusion: The newly developed radiomic signature was a powerful predictor of OS and DFS. Moreover, the radiomic signature could predict which patients could benefit from chemotherapy.
This paper references
10.1016/j.ebiom.2018.09.007
Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer
Y. Jiang (2018)
-2-deoxyglucose PET, prognosis, and epithelial-mesenchymal transition in non-small cell lung cancer
F-Fluoro (2016)
10.1093/BIOMET/92.4.965
Concordance probability and discriminatory power in proportional hazards regression
M. Gonen (2005)
10.1007/s00330-015-4105-5
Multiparametric fully-integrated 18-FDG PET/MRI of advanced gastric cancer for prediction of chemotherapy response: a preliminary study
D. Lee (2015)
10.1016/j.acra.2012.07.002
Prognostic value of metabolic tumor burden from (18)F-FDG PET in surgical patients with non-small-cell lung cancer.
H. Zhang (2013)
10.1016/S1470-2045(13)70491-1
Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis.
J. Zhang (2013)
10.18637/JSS.V050.I11
Evaluating Random Forests for Survival Analysis using Prediction Error Curves.
U. Mogensen (2012)
10.1016/j.neuroimage.2006.01.015
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability
P. Yushkevich (2006)
10.1016/S1470-2045(14)70473-5
Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): 5-year follow-up of an open-label, randomised phase 3 trial.
S. Noh (2014)
10.1016/j.ebiom.2017.06.028
Prognostic and Predictive Value of p21-activated Kinase 6 Associated Support Vector Machine Classifier in Gastric Cancer Treated by 5-fluorouracil/Oxaliplatin Chemotherapy
Y. Jiang (2017)
10.1097/SLA.0000000000002116
ImmunoScore Signature: A Prognostic and Predictive Tool in Gastric Cancer
Y. Jiang (2018)
10.1158/1078-0432.CCR-04-0713
X-Tile
R. Camp (2004)
10.2214/AJR.18.19501
Utility of FDG PET/CT in the Characterization of Sinonasal Neoplasms: Analysis of Standardized Uptake Value Parameters.
K. Ozturk (2018)
10.1038/NCOMMS5644
Corrigendum: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1016/S1470-2045(18)30413-3
A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study.
R. Sun (2018)
10.1002/hep.27877
A Computed Tomography Radiogenomic Biomarker Predicts Microvascular Invasion and Clinical Outcomes in Hepatocellular Carcinoma
Sudeep Banerjee (2015)
10.3748/wjg.v23.i38.6923
Evolving role of FDG-PET/CT in prognostic evaluation of resectable gastric cancer
E. De Raffele (2017)
10.1371/journal.pone.0204918
The 18F-FDG PET/CT response to radiotherapy for patients with spinal metastasis correlated with the clinical outcomes
Jinhyun Choi (2018)
10.1186/1472-6947-8-53
Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers
A. J. Vickers (2008)
10.1007/s11307-016-0973-6
Robustness of Radiomic Features in [11C]Choline and [18F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization
L. Lu (2016)
10.1111/J.1467-9868.2011.00771.X
Regression shrinkage and selection via the lasso: a retrospective
R. Tibshirani (2011)
10.1111/J.1541-0420.2007.00832.X
Efron-type measures of prediction error for survival analysis.
T. Gerds (2007)
Genetics of gastric cancer.
N. Peddanna (1995)
10.1118/1.4924344
SU-E-J-258: Prediction of Cervical Cancer Treatment Response Using Radiomics Features Based On F18-FDG Uptake in PET Images
B. Altazi (2015)
10.1007/s00259-011-1934-6
Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer
S. Liao (2011)
10.1038/srep11075
The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis
R. Leijenaar (2015)
10.1080/14737159.2017.1286985
Molecular classification of gastric cancer
C. Röcken (2017)
10.1001/jama.2010.534
Benefit of adjuvant chemotherapy for resectable gastric cancer: a meta-analysis.
X. Paoletti (2010)
10.1007/s00259-017-3837-7
Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery
M. Kirienko (2017)
10.1158/1078-0432.CCR-16-0617
Interleukin-17–Producing Neutrophils Link Inflammatory Stimuli to Disease Progression by Promoting Angiogenesis in Gastric Cancer
Tuan-Jie Li (2016)
10.1038/nrclinonc.2016.162
Imaging biomarker roadmap for cancer studies
J. O’Connor (2017)
10.3322/caac.21262
Global cancer statistics, 2012
L. Torre (2015)
Relationship between F-18-FDG PET/CT findings and HER2 expression in gastric cancer
RH Chen (2016)
10.1371/journal.pone.0161278
Tumor Heterogeneity in Human Epidermal Growth Factor Receptor 2 (HER2)-Positive Advanced Gastric Cancer Assessed by CT Texture Analysis: Association with Survival after Trastuzumab Treatment
Sung Hyun Yoon (2016)
10.1111/j.1475-1313.2011.00851.x
Statistical methods for conducting agreement (comparison of clinical tests) and precision (repeatability or reproducibility) studies in optometry and ophthalmology
C. Mcalinden (2011)
10.18637/JSS.V039.I05
Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.
Noah Simon (2011)
10.1001/jamasurg.2017.1087
Association of Adjuvant Chemotherapy With Survival in Patients With Stage II or III Gastric Cancer
Y. Jiang (2017)
10.1200/JCO.2009.26.4325
Prognostic and predictive gene signature for adjuvant chemotherapy in resected non-small-cell lung cancer.
Chang-Qi Zhu (2010)
10.1097/PPO.0000000000000116
How Imaging Can Impact Clinical Trial Design: Molecular Imaging as a Biomarker for Targeted Cancer Therapy
D. Mankoff (2015)
10.1148/radiol.2015151169
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
10.1097/MD.0000000000001037
Preoperative Standardized Uptake Value of Metastatic Lymph Nodes Measured by 18F-FDG PET/CT Improves the Prediction of Prognosis in Gastric Cancer
B. Song (2015)
10.1016/j.lungcan.2017.10.015
Radiomics and radiogenomics in lung cancer: A review for the clinician.
R. Thawani (2018)
10.1038/npjbcancer.2016.12
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
H. Li (2016)
10.1002/jmri.26253
Volumetric Apparent Diffusion Coefficient Histogram Analysis in Differentiating Intrahepatic Mass‐Forming Cholangiocarcinoma From Hepatocellular Carcinoma
Xianlun Zou (2019)
10.1158/1078-0432.CCR-16-0702
Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response
P. Kickingereder (2016)
10.18632/oncotarget.11574
Prognostic value of metabolic parameters on preoperative 18F-Fluorodeoxyglucose positron emission tomography/computed tomography in patients with stage III gastric cancer
Sae Jung Na (2016)
10.1007/s00330-016-4540-y
Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker
F. Giganti (2016)
10.1371/journal.pone.0155333
Multiparametric [18F]Fluorodeoxyglucose/ [18F]Fluoromisonidazole Positron Emission Tomography/ Magnetic Resonance Imaging of Locally Advanced Cervical Cancer for the Non-Invasive Detection of Tumor Heterogeneity: A Pilot Study
K. Pinker (2016)
10.1186/s12885-017-3271-z
Prognostic value of pretreatment standardized uptake value of F-18-fluorodeoxyglucose PET in patients with gastric cancer: a meta-analysis
Zhonghua Wu (2017)
10.1002/SIM.4780030207
Regression modelling strategies for improved prognostic prediction.
F. Harrell (1984)
10.1038/nrclinonc.2014.138
Genetics: New molecular classification of gastric adenocarcinoma proposed by The Cancer Genome Atlas
M. Razzak (2014)
10.1016/j.radonc.2015.02.015
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
T. Coroller (2015)
10.1016/S1470-2045(18)30108-6
Predictive test for chemotherapy response in resectable gastric cancer: a multi-cohort, retrospective analysis.
J. Cheong (2018)
10.2967/jnmt.116.181479
Metabolic Signature on 18F-FDG PET/CT, HER2 Status, and Survival in Gastric Adenocarcinomas
R. Celli (2016)
10.2967/jnumed.115.171165
Relationship Between 18F-FDG PET/CT Findings and HER2 Expression in Gastric Cancer
Ruohua Chen (2016)
10.1016/j.ejrad.2016.08.014
Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis.
Su Yeon Ahn (2016)
Radiogenomic analysis demonstrates associations between
S Yamamoto
10.1016/j.mri.2012.06.010
Radiomics: the process and the challenges.
Virendra Kumar (2012)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
10.1007/s00259-012-2164-2
Role of 18F-FDG PET/CT in the prediction of gastric cancer recurrence after curative surgical resection
J. Lee (2012)
10.18637/JSS.V033.I01
Regularization Paths for Generalized Linear Models via Coordinate Descent.
J. Friedman (2010)
10.1148/radiol.2016160259
Radiogenomic Analysis Demonstrates Associations between (18)F-Fluoro-2-Deoxyglucose PET, Prognosis, and Epithelial-Mesenchymal Transition in Non-Small Cell Lung Cancer.
S. Yamamoto (2016)
10.1158/1078-0432.CCR-18-0848
Immunomarker Support Vector Machine Classifier for Prediction of Gastric Cancer Survival and Adjuvant Chemotherapeutic Benefit
Y. Jiang (2018)
10.1148/RADIOL.2016152234
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.
Yanqi Huang (2016)
10.3322/canjclin.49.1.33
Global cancer statistics
D. Parkin (1999)



This paper is referenced by
10.7150/jca.37531
Young age increases risk for lymph node positivity in gastric cancer: A Chinese multi-institutional database and US SEER database study
Yuming Jiang (2020)
10.1002/jmri.26749
A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors
X. Xu (2019)
10.35712/aig.v1.i4.71
Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives
Michihiro Kudou (2020)
10.18632/aging.103508
Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment 18F-PET/CT
Zheran Liu (2020)
Application of radiomics and machine learning in head and neck cancers
Zhouying Peng (2020)
10.1055/A-0838-8135
Big Imaging Data: Klinische Bildanalyse mit Radiomics und Deep Learning
Aydin Demircioglu (2019)
10.1038/s41598-020-78963-2
Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy
Shin Hye Yoo (2021)
10.7150/thno.37429
Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer
J. Fang (2020)
10.1158/2326-6066.CIR-19-0311
Tumor Immune Microenvironment and Chemosensitivity Signature for Predicting Response to Chemotherapy in Gastric Cancer
Yuming Jiang (2019)
10.18632/aging.103406
A validated survival nomogram for early-onset diffuse gastric cancer
Fei Liao (2020)
10.3389/fonc.2020.552270
Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
Junmeng Li (2020)
10.1007/978-981-15-4431-6_12
Big Data Analytics and Radiomics to Discover Diagnostics and Therapeutics for Gastric Cancer
Kummetha Jagadish (2020)
10.3389/fonc.2019.00340
Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer
Y. Jiang (2019)
10.1007/978-981-15-4431-6
Recent Advancements in Biomarkers and Early Detection of Gastrointestinal Cancers
G. P. Nagaraju (2020)
10.1002/mp.14350
Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study.
S. Wang (2020)
10.1101/2019.12.27.19015982
Radiomics Features of 18F-fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer
J. Kang (2019)
10.1097/CM9.0000000000000360
Radiomics approaches in gastric cancer: a frontier in clinical decision making
Yue Wang (2019)
10.1186/s12938-019-0744-0
Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer
X. Tang (2020)
10.3748/wjg.v26.i19.2427
Nomogram for predicting pathological complete response to neoadjuvant chemotherapy in patients with advanced gastric cancer
Y. Chen (2020)
10.23736/S1824-4785.19.03192-3
CT radiomics and PET radiomics: ready for clinical implementation?
M. Bogowicz (2019)
10.7150/jca.46704
Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation
J. Tan (2020)
10.7150/thno.46428
Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19
Qingxia Wu (2020)
10.1007/s00330-019-06370-x
Gastric cancer and image-derived quantitative parameters: Part 2—a critical review of DCE-MRI and 18F-FDG PET/CT findings
Lei Tang (2019)
10.3390/biomedicines8090304
Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review
Anne-Leen Deleu (2020)
10.1038/s41467-020-18162-9
Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer
Zhenyu Liu (2020)
10.2967/jnumed.118.222893
Introduction to Radiomics
M. Mayerhoefer (2020)
10.3233/cbm-190561
Identification of hsa_circ_0005654 as a new early biomarker of gastric cancer.
Yezhao Wang (2019)
10.1007/s00330-020-06796-8
Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study
Huanjun Wang (2020)
10.3389/fonc.2020.01416
Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer
W. Huang (2020)
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