CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.

By T. Coroller, Patrick Grossmann, Ying Hou, E. Rios Velazquez, R. Leijenaar, Gretchen Hermann, Philippe Lambin, Benjamin Haibe-Kains, R. Mak, Hugo J. W. L. Aerts. Radiotherapy and Oncology, 2015

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BACKGROUND AND PURPOSE Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. MATERIAL AND METHODS We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). RESULTS Thirty-five radiomic features were found to be prognostic (CI>0.60, FDR<5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI=0.55, p-value=2.77×10(-5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79×10(-17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56×10(-11)). CONCLUSIONS Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data.

Subfields: Oncology, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1d72f25545d93332b7e2f5d7211655e63d864796

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Citations in the corpus

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging (2019, background): “Previous associationswere found for risk of distant metastases with the pretreatment scan only, with machine learning techniques (15).”
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer (2015, background): “It has been shown that radiomic features are associated with tumor histology (15–17), tumor grades or stages (16), patient survival (2, 7, 18–20), metabolism (21), and various other clinical outcomes (7, 16, 22, 23).”
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, methodology): “Size (volume and maximal diameter) and shape measures (sphericity, compactness and surface-to-volume ratio) have been described in radiomics research.(37,61) Finally, features relating to tumour location (e.”
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, background): “Another study [84] demonstrated that radiomics features are prognostic for both distant metastasis and survival, and a radiomics signature significantly improves prognostication when added to clinical data.”
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, methodology): “Specific quantitative imaging modeling studies were performed on a comprehensive list of tumor sites, including head and neck, (4, 8-13) lung, (3, 4, 14-18) breast, (19-29) liver, (30, 31) cervix, (32-36) prostate, (37, 38) extremities (sarcoma), (39, 40) and brain.”
Machine Learning methods for Quantitative Radiomic Biomarkers (2015, background): “Several studies have investigated various radiomic features in terms of their prognostic or predictive abilities and reliability across different clinical settings 4–10 .”
Artificial intelligence in radiology (2018, positive): “For example, studies in non-small-cell lung cancer (NSCLC) used radiomics to predict distant metastasis in lung adenocarcinoma 17 and tumour histological subtypes 18 as well as disease recurrence 19 , somatic mutations 20 , gene-expression profiles 21 and overall survival 22 .”
Data Analysis Strategies in Medical Imaging (2018, background): “Similarly, it has been demonstrated that different radiomic features based on the shape, texture, and regional heterogeneity of the diseased tissues are associated with several clinical endpoints in oncology (4, 10, 11, 13, 25 – 32) and in other domains such as cardiology (14).”
Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications (2019, background): “…including lung cancer ( P < 3.53 × 10 −6 ) 69 ; in addition, it is associated with distant metastasis in lung adenocarcinoma ( P = 1.79 × 10 −17 ), 40 tumor histologic subtypes ( P = 2.3 × 10 -7 ), 68 and underlying biological patterns, including somatic mutations 39 and gene expression profiles.”
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “Jia et al [138] Retrospective, single center study 70 + 31 70 (statistical, histogram, morphologic, and texture) c-means, ICC, linear correlation coefficient, LASSO and Cox PET Prognosis”