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

Radiomics Nomogram For Prediction Disease-free Survival And Adjuvant Chemotherapy Benefits In Patients With Resected Stage I Lung Adenocarcinoma.

D. Xie, Tingting Wang, Shujung Huang, Jiajun Deng, Yi-jiu Ren, Y. Yang, Junqi Wu, L. Zhang, K. Fei, X. Sun, Yunlang She, Chang Chen
Published 2020 · Medicine

Cite This
Download PDF
Analyze on Scholarcy
Share
Background Robust imaging biomarkers are needed for risk stratification in stage I lung adenocarcinoma patients in order to select optimal treatment regimen. We aimed to construct and validate a radiomics nomogram for predicting the disease-free survival (DFS) of patients with resected stage I lung adenocarcinoma, and further identifying candidates benefit from adjuvant chemotherapy (ACT). Methods Using radiomics approach, we analyzed 554 patients' computed tomography (CT) images from three multicenter cohorts. Prognostic radiomics features were extracted from computed tomography (CT) images and selected using least absolute shrinkage and selection operator (LASSO) Cox regression model to build a radiomics signature for DFS stratification. The biological basis of radiomics was explored in the Radiogenomics dataset (n=79) by gene set enrichment analysis (GSEA). Then a nomogram that integrated the signature with these significant clinicopathologic factors in the multivariate analysis were constructed in the training cohort (n=238), and its prognostic accuracy was evaluated in the validation cohort (n=237). Finally, the predictive value of nomogram for ACT benefits was assessed. Results The radiomics signature with higher score was significantly associated with worse DFS in both the training and validation cohorts (P<0.001). The GSEA presented that the signature was highly correlated to characteristic metabolic process and immune system during cancer progression. Multivariable analysis revealed that age (P=0.031), pathologic TNM stage (P=0.043), histologic subtype (P=0.010) and the signature (P<0.001) were independently associated with patients' DFS. The integrated radiomics nomogram showed good discrimination performance, as well as good calibration and clinical utility, for DFS prediction in the validation cohort. We further found that the patients with high points (point ≥8.788) defined by the radiomics nomogram obtained a significant favorable response to ACT (P=0.04) while patients with low points (point <8.788) showed no survival difference (P=0.7). Conclusions The radiomics nomogram could be used for prognostic prediction and ACT benefits identification for patient with resected stage I lung adenocarcinoma.
This paper references
Stage I-IIIb NSCLC 116 Overall stage, selected radiomics features (metabolic volume, PET entropy
Figure S3 The Benefit Analysis of adjuvant chemotherapy (ACT) in All Patients with Stage IB Adenocarcinoma. These patients showed no survival difference between with and without ACT
10.1200/JCO.2008.16.4855
Adjuvant paclitaxel plus carboplatin compared with observation in stage IB non-small-cell lung cancer: CALGB 9633 with the Cancer and Leukemia Group B, Radiation Therapy Oncology Group, and North Central Cancer Treatment Group Study Groups.
G. Strauss (2008)
10.1093/bioinformatics/btr260
Molecular signatures database (MSigDB) 3.0
A. Liberzon (2011)
10.1097/JTO.0000000000000630
The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.
W. Travis (2015)
10.1007/s00432-018-2801-7
Should patients with stage IB non-small cell lung cancer receive adjuvant chemotherapy? A comparison of survival between the 8th and 7th editions of the AJCC TNM staging system for stage IB patients
J. Wang (2018)
10.1007/s00259-018-3987-2
Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions
M. Kirienko (2018)
10.1200/JCO.2017.72.4401
Adjuvant Systemic Therapy and Adjuvant Radiation Therapy for Stage I to IIIA Completely Resected Non-Small-Cell Lung Cancers: American Society of Clinical Oncology/Cancer Care Ontario Clinical Practice Guideline Update.
M. Kris (2017)
10.1016/j.acra.2019.05.019
CT Radiomics Signature of Tumor and Peritumoral Lung Parenchyma to Predict Nonsmall Cell Lung Cancer Postsurgical Recurrence Risk.
Tugba Akinci D'Antonoli (2019)
Data for NSCLC Radiogenomics Collection. The Cancer Imaging Archive
S Bakr
10.1016/j.jtho.2016.11.2226
Radiomic‐Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC
T. Coroller (2017)
10.1093/nar/gkt111
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods
Leif Väremo (2013)
10.1038/ncb3124
Metabolic pathways promoting cancer cell survival and growth
Lindsey K. Boroughs (2015)
10.1097/JTO.0b013e31824cbad8
Predictors of Death, Local Recurrence, and Distant Metastasis in Completely Resected Pathological Stage-I Non–Small-Cell Lung Cancer
Jung-Jyh Hung (2012)
10.6004/JNCCN.2014.0068
International adaptations of NCCN Clinical Practice Guidelines in Oncology.
R. Carlson (2014)
10.1016/j.mri.2012.05.001
3D Slicer as an image computing platform for the Quantitative Imaging Network.
Andriy Fedorov (2012)
Cisplatinbased adjuvant chemotherapy in patients with completely resected non-small-cell lung cancer
R Arriagada (2004)
10.1016/J.LUNGCAN.2004.08.016
Compliance with post-operative adjuvant chemotherapy in non-small cell lung cancer. An analysis of National Cancer Institute of Canada and intergroup trial JBR.10 and a review of the literature.
N. Alam (2005)
Age <65) + 1.210228 × 1 (Age >=65) + 0.0 × 0 (Pathological stage: IA) + 1.896196 × 1 (Pathological stage IB)
10.1016/j.jtho.2016.05.022
Adjuvant Chemotherapy for Patients with T2N0M0 NSCLC
D. Morgensztern (2016)
10.1007/s00259-016-3325-5
Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III
Marie-Charlotte Desseroit (2016)
10.1158/0008-5472.CAN-17-0122
Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer.
E. Rios Velazquez (2017)
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)
10.1101/gad.314617.118
Roles of the immune system in cancer: from tumor initiation to metastatic progression.
H. González (2018)
10.1016/j.jtho.2016.07.002
Predicting Malignant Nodules from Screening CT Scans
Samuel H. Hawkins (2016)
10.1634/theoncologist.2017-0538
Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.
G. Lee (2018)
Stage I-IIIb NSCLC 371 Radiomics signature, age, sex, T stage, N stage
Advanced NSCLC 118 Radiomics signature, age, lymph node
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)
10.1200/JCO.2007.13.9030
Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group.
J. Pignon (2008)
10.1200/JCO.2011.37.2185
The novel histologic International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification system of lung adenocarcinoma is a stage-independent predictor of survival.
A. Warth (2012)
10.1158/0008-5472.CAN-17-0339
Computational Radiomics System to Decode the Radiographic Phenotype.
J. van Griethuysen (2017)
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/j.tranon.2017.10.012
Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer
L. Zhang (2018)
10.1007/s00330-018-5949-2
Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer
L. Wang (2018)
10.1164/RCCM.200706-815OC
Survival after surgery in stage IA and IB non-small cell lung cancer.
D. Ost (2008)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
10.1371/journal.pmed.1002711
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
A. Hosny (2018)
10.1007/s00330-018-5770-y
Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients
Lifeng Yang (2018)
Randomized phase III trial of vinorelbine plus cisplatin compared with observation in completely resected stage IB and II nonsmall-cell lung cancer: updated survival analysis of
C A Butts
10.7554/eLife.23421
Defining the biological basis of radiomic phenotypes in lung cancer
P. Grossmann (2017)
10.1038/nrclinonc.2017.141
Radiomics: the bridge between medical imaging and personalized medicine
P. Lambin (2017)
10.1016/j.jtcvs.2017.09.143
Prognostic significance and adjuvant chemotherapy survival benefits of a solid or micropapillary pattern in patients with resected stage IB lung adenocarcinoma
F. Qian (2018)
10.1093/jnci/djx055
The Rise of Radiomics and Implications for Oncologic Management.
V. Verma (2017)
10.1007/s00330-018-5509-9
The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules
Yunlang She (2018)
X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization
R L Camp (2004)
Predicting Response to Cancer Immunotherapy using Non-invasive Radiomic
S Trebeschi (2019)
10.1016/j.jtho.2016.06.028
The IASLC Lung Cancer Staging Project: Methodology and Validation Used in the Development of Proposals for Revision of the Stage Classification of NSCLC in the Forthcoming (Eighth) Edition of the TNM Classification of Lung Cancer
F. Detterbeck (2016)
10.1038/ncomms5006
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1097/JTO.0000000000000689
Role of CT and PET Imaging in Predicting Tumor Recurrence and Survival in Patients with Lung Adenocarcinoma: A Comparison with the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification of Lung Adenocarcinoma
H. Lee (2015)
10.1016/j.athoracsur.2015.10.075
Adjuvant Chemotherapy Improves the Probability of Freedom From Recurrence in Patients With Resected Stage IB Lung Adenocarcinoma.
Jung-Jyh Hung (2016)
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)



Semantic Scholar Logo Some data provided by SemanticScholar