Background
: Radiomics, defined as quantitative features extracted from images, provide a non-invasive
means of assessing malignant versus benign pulmonary nodules. In this study, we evaluate the consistency with which perinodular radiomics extracted
from low-dose computed tomography images serve to identify malignant pulmonary nodules.
Materials and Methods
: Using the National Lung Screening Trial (NLST), we selected individuals with pulmonary
nodules between 4mm to 20mm in diameter. Nodules were segmented to generate four distinct
datasets; 1) a Tumor dataset containing tumor-specific features, 2) a 10 mm Band dataset
containing parenchymal features between the segmented nodule boundary and 10mm out
from the boundary, 3) a 15mm Band dataset, and 4) a Tumor Size dataset containing
the maximum nodule diameter. Models to predict malignancy were constructed using support-vector
machine (SVM), random forest (RF), and least absolute shrinkage and selection operator
(LASSO) approaches. Ten-fold cross validation with 10 repetitions per fold was used
to evaluate the performance of each approach applied to each dataset.
Results
: With respect to the RF, the Tumor, 10mm Band, and 15mm Band datasets achieved areas
under the receiver-operator curve (AUC) of 84.44%, 84.09%, and 81.57%, respectively.
Significant differences in performance were observed between the Tumor and 15mm Band
datasets (adj. p-value <0.001). However, when combining tumor-specific features with
perinodular features, the 10mm Band + Tumor and 15mm Band + Tumor datasets (AUC 87.87%
and 86.75%, respectively) performed significantly better than the Tumor Size dataset
(66.76%) or the Tumor dataset. Similarly, the AUCs from the SVM and LASSO were 84.71%
and 88.91%, respectively, for the 10mm Band + Tumor.
Conclusions
: The combined 10mm Band + Tumor dataset improved the differentiation between benign
and malignant lung nodules compared to the Tumor datasets across all methodologies.
This demonstrates that parenchymal features capture novel diagnostic information beyond
that present in the nodule itself. (data agreement: NLST-163)
Key Words
Abbreviation:
: SVM (Support Vector Machine), LASSO (Least Absolute Shrinkage Selection Operator), RF (Random Forest), LDCT (Low-Dose Computed Tomography)To read this article in full you will need to make a payment
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References
- Screening for lung cancer with low-dose computed tomography: a review of current status.J Thorac Dis. AME Publishing Company;. 2013; 5https://doi.org/10.3978/j.issn.2072-1439.2013.09.06
- The National Lung Screening Trial Research Team. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening.N Engl J Med. 2011; 365: 395-409https://doi.org/10.1056/NEJMoa1102873
- False-positive screens and lung cancer risk in the National Lung Screening Trial: Implications for shared decision-making.J Med Screen. 2018; 25: 110-112https://doi.org/10.1177/0969141317727771
- Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models.Thorax. 2019; 74: 643-649https://doi.org/10.1136/thoraxjnl-2018-212638
- Overdiagnosis in Low-Dose Computed Tomography Screening for Lung Cancer.JAMA Intern Med. 2014; 174: 269-274https://doi.org/10.1001/jamainternmed.2013.12738
- Assessing the benefits and harms of low-dose computed tomography screening for lung cancer.Lung Cancer Manag. 2014; 3: 491-498https://doi.org/10.2217/LMT.14.41
- Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement.JAMA. 2021; 325: 962https://doi.org/10.1001/jama.2021.1117
- Performance of Lung-RADS in the National Lung Screening Trial.Ann Intern Med. 2015; 162: 485-491https://doi.org/10.7326/M14-2086
- Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.Tomography. 2016; 2: 430-437https://doi.org/10.18383/j.tom.2016.00235
- Factors Influencing the False Positive Rate in CT Lung Cancer Screening.Acad Radiol. 2022; 29: S18-S22https://doi.org/10.1016/j.acra.2020.07.040
- Assessing the Relationship Between Lung Cancer Risk and Emphysema Detected on Low-Dose CT of the Chest.Chest. 2007; 132: 1932-1938https://doi.org/10.1378/chest.07-1490
- Lung cancer histologies associated with emphysema on computed tomography.Lung Cancer. 76. Elsevier, 2012: 61-66https://doi.org/10.1016/j.lungcan.2011.09.003
- Regional Emphysema of a Non-Small Cell Tumor Is Associated with Larger Tumors and Decreased Survival.Ann Am Thorac Soc. 2015; 150603140911000https://doi.org/10.1513/AnnalsATS.201411-539OC
- Combined pulmonary fibrosis and emphysema and idiopathic pulmonary fibrosis in non-small cell lung cancer: impact on survival and acute exacerbation.BMC Pulm Med. 2019; 19: 177https://doi.org/10.1186/s12890-019-0951-2
- Radiomics: Images Are More than Pictures, They Are Data.Radiology. Radiological Society of North America;. 2015; 278: 563-577https://doi.org/10.1148/radiol.2015151169
- Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.J Med Imaging Bellingham Wash. 2015; 2041004https://doi.org/10.1117/1.JMI.2.4.041004
- Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.Med Phys. 2019; 46: 3207-3216https://doi.org/10.1002/mp.13592
- Can Peritumoral Radiomics Improve the Prediction of Malignancy of Solid Pulmonary Nodule Smaller Than 2 cm?.Acad Radiol. 2022; 29: S47-S52https://doi.org/10.1016/j.acra.2020.10.029
- Performance of Lung Nodule Management Algorithms for Lung-RADS Category 4 Lesions.Acad Radiol. 2021; 28: 1037-1042https://doi.org/10.1016/j.acra.2020.04.041
- Chest Imaging Platform: An Open-Source Library and Workstation for Quantitative Chest Imaging. C66 LUNG IMAGING II NEW PROBES Emerg Technol.American Thoracic Society. 2015; (A4975–A4975)https://doi.org/10.1164/ajrccm-conference.2015.191.1_MeetingAbstracts.A4975
- Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation.PLOS ONE. 2017; 12e0178944https://doi.org/10.1371/journal.pone.0178944
- Scikit-learn: Machine Learning in Python.Scikit-Learn Mach Learn Python. 2011; 12: 2825-2830
- Comparison between semantic features and lung-RADS in predicting malignancy of screening lung nodule.Clin Lung Cancer. 2018; 19 (e3): 148-156https://doi.org/10.1016/j.cllc.2017.10.002
- Inflammation in chronic obstructive pulmonary disease and its role in cardiovascular disease and lung cancer.Clin Transl Med. 2015; 4https://doi.org/10.1186/s40169-015-0068-z
- Chronic obstructive pulmonary disease (COPD) and lung cancer: common pathways for pathogenesis.J Thorac Dis. 2019; 11: S2155-S2172https://doi.org/10.21037/jtd.2019.10.54
- Risk factors and clinical characteristics of lung cancer in idiopathic pulmonary fibrosis: a retrospective cohort study.BMC Pulm Med. 2019; 19: 149https://doi.org/10.1186/s12890-019-0905-8
- Thin-section CT of the secondary pulmonary lobule: anatomy and the image–the 2004 Fleischner lecture.Radiology. 2006; 239: 322-338https://doi.org/10.1148/radiol.2392041968
- A Practical Approach to High-Resolution CT of Diffuse Lung Disease.Eur J Radiol. 2014; 83: 6-19https://doi.org/10.1016/j.ejrad.2012.12.028
- Radiomics: an overview in lung cancer management—a narrative review.Ann Transl Med. 2020; 8: 1191https://doi.org/10.21037/atm-20-4589
Article info
Publication history
Published online: August 03, 2022
Accepted:
July 6,
2022
Received in revised form:
June 24,
2022
Received:
April 23,
2022
Footnotes
Research originated from the University of Vermont, Health and Science Research Facility, 95 Carrigan Drive, Burlington VT, 05405.
Funding sources: NIH K23 HL133476, T32 HL076122, NCI F31 CA268908
Data generated or analyzed during the study are available from the corresponding author by reques.
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
