Online citations, reference lists, and bibliographies.
← Back to Search

Radiomics Features Of 18F-fluorodeoxyglucose Positron-Emission Tomography As A Novel Prognostic Signature In Colorectal Cancer

J. Kang, J. Lee, H. Lee, Eun-Suk Cho, E. Park, Seung Hyuk Baik, K. Lee, Chihyun Park, Y. Yeu, Jean R Clemenceau, Sun-ho Park, H. Xu, Changjin Hong, Tae Hyun Hwang
Published 2019 · Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
Share
Purpose: The aim of this study was to investigate the prognostic value of radiomics signatures derived from 18F-fluorodeoxyglucose (18F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). Methods: From April 2008 to Jan 2014, we identified CRC patients who underwent 18F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on 18F-FDG-PET. Patients were divided into a training and a validation set by random sampling. A least absolute shrinkage and selection operator (LASSO) Cox regression model was applied for prognostic signature building with progression-free survival (PFS) using the training set. Using the calculated radiomics score, a nomogram was developed, and the clinical utility of this nomogram was assessed in the validation set. Results: Three-hundred-and-eight-one patients with surgically resected CRC patients (training set 228 vs. validation set 153) were included. In the training set, a radiomics signature called a rad_score was generated using two PET-derived features such as Gray Level Run Length Matrix_Long-Run Emphasis (GLRLM_LRE) and Grey-Level Zone Length Matrix_Short-Zone Low Gray-level Emphasis (GLZLM_SZLGE). Patients with a high-rad_score in the training and validation set had shorter PFS. Multivariable analysis revealed that the rad_score was an independent prognostic factor in both training and validation sets. A radiomics nomogram, developed using rad_score, nodal stage, and lymphovascular invasion, showed good performance in the calibration curve and comparable predictive power with the staging system in the validation set. Conclusion: Textural features derived from 18F-FDG-PET images may enable more detailed stratification of prognosis in patients with CRC.
This paper references
10.1007/s00330-017-4859-z
The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies
Isaac Shiri (2017)
10.1007/s00259-019-04372-x
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
M. Sollini (2019)
10.1200/JCO.2015.65.9128
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
Yanqi Huang (2016)
10.3748/wjg.v18.i36.5072
Prognostic value of 18-fluorodeoxyglucose positron emission tomography-computed tomography in resectable colorectal cancer.
J. Lee (2012)
10.1007/s11605-017-3566-z
Neoadjuvant Chemotherapy Improves Survival in Patients with Clinical T4b Colon Cancer
Ahmed N Dehal (2017)
10.3348/kjr.2018.0611
Response Assessment with MRI after Chemoradiotherapy in Rectal Cancer: Current Evidences
Nieun Seo (2019)
10.1158/1078-0432.CCR-17-1510
A Radiomics Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer
Shaoxu Wu (2017)
10.1007/s00259-016-3577-0
Total lesion glycolysis (TLG) as an imaging biomarker in metastatic colorectal cancer patients treated with regorafenib
Y. Lim (2016)
10.1158/1078-0432.CCR-17-3783
Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer
Hyunjin Park (2018)
10.1097/RLU.0000000000001332
Metabolic Tumor Volume and Total Lesion Glycolysis in PET/CT Correlate With the Pathological Findings of Colorectal Cancer and Allow Its Accurate Staging
Y. Suzuki (2016)
10.1007/s00259-019-04313-8
PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy
L. Antunovic (2019)
10.1007/s00259-017-3855-5
FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer
P. Lovinfosse (2017)
10.2967/jnumed.106.035774
Partial-Volume Effect in PET Tumor Imaging*
M. Soret (2007)
10.3748/wjg.v20.i17.5104
TNM staging of colorectal cancer should be reconsidered by T stage weighting.
J. Li (2014)
10.1158/1078-0432.CCR-04-0713
X-Tile
R. Camp (2004)
10.3390/mi9060300
Liquid Biopsy in Colorectal Cancer-Current Status and Potential Clinical Applications
G. Norčič (2018)
10.2967/jnumed.118.217612
Optimized Feature Extraction for Radiomics Analysis of 18F-FDG PET Imaging
L. Papp (2019)
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)
10.1016/S0140-6736(18)30789-X
International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study
F. Pagès (2018)
10.1007/s00259-018-4100-6
Radiomics analysis of pre-treatment [18F]FDG PET/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment
E. V. van Helden (2018)
10.1001/jamaoncol.2019.0512
Prognostic Potential of Circulating Tumor DNA Measurement in Postoperative Surveillance of Nonmetastatic Colorectal Cancer.
Yuxuan Wang (2019)
10.2967/jnumed.113.129858
Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis
F. Orlhac (2014)
10.1038/modpathol.2017.120
Optimal detection of clinically relevant mutations in colorectal carcinoma: sample pooling overcomes intra-tumoral heterogeneity
A. Nelson (2018)
10.1007/978-1-4612-0919-5_38
Information Theory and an Extension of the Maximum Likelihood Principle
H. Akaike (1973)
10.1016/S1470-2045(12)70348-0
Feasibility of preoperative chemotherapy for locally advanced, operable colon cancer: the pilot phase of a randomised controlled trial
D. Agbamu (2012)
10.1002/(SICI)1097-0258(19970228)16:4<385::AID-SIM380>3.0.CO;2-3
The lasso method for variable selection in the Cox model.
R. Tibshirani (1997)
10.1186/s12885-015-1991-5
The preoperative SUVmax for 18F-FDG uptake predicts survival in patients with colorectal cancer
D. Shi (2015)
10.2967/jnumed.113.127340
Textural Parameters of Tumor Heterogeneity in 18F-FDG PET/CT for Therapy Response Assessment and Prognosis in Patients with Locally Advanced Rectal Cancer
R. Bundschuh (2014)
10.1038/s41571-019-0241-1
Biomarker-guided therapy for colorectal cancer: strength in complexity
A. Sveen (2019)
10.2967/jnumed.115.156927
Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET
J. Yan (2015)
10.4143/crt.2019.138
Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2016
K. Jung (2019)
10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS
K. Lee (1996)
10.1007/s00432-018-2804-4
A novel histologic grading system based on lymphovascular invasion, perineural invasion, and tumor budding in colorectal cancer
J. Huh (2018)
10.1007/s00259-015-3180-9
Prediction of neoadjuvant radiation chemotherapy response and survival using pretreatment [18F]FDG PET/CT scans in locally advanced rectal cancer
Ji-In Bang (2015)
10.1007/s00259-017-3779-0
Elevated tumor-to-liver uptake ratio (TLR) from 18F–FDG-PET/CT predicts poor prognosis in stage IIA colorectal cancer following curative resection
Jun Huang (2017)
10.1001/jamaoncol.2019.0528
Analysis of Plasma Cell-Free DNA by Ultradeep Sequencing in Patients With Stages I to III Colorectal Cancer
T. Reinert (2019)
10.1158/0008-5472.CAN-18-0125
LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity.
C. Nioche (2018)
10.1080/0284186X.2016.1230274
Lymphovascular and perineural invasion in stage II rectal cancer: a report from the Swedish colorectal cancer registry
M. Nikberg (2016)
10.1136/gutjnl-2014-308859
Analysis of circulating tumour DNA to monitor disease burden following colorectal cancer surgery
T. Reinert (2015)
10.2967/jnumed.118.210161
Validation of Metabolically Active Tumor Volume and Total Lesion Glycolysis as 18F-FDG PET/CT–derived Prognostic Biomarkers in Chemorefractory Metastatic Colorectal Cancer
E. Woff (2019)
10.1007/s00259-018-4250-6
Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features
V. Giannini (2018)
10.1148/radiol.2018172229
Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.
R. J. Beukinga (2018)



Semantic Scholar Logo Some data provided by SemanticScholar