Rationales and Objectives
To investigate the prognostic value of the radiomic-based prediction model in predicting
the interval growth rate of persistent subsolid nodules (SSNs) with an initial size
of ≤ 3 cm manifesting as lung adenocarcinomas.
Materials and Methods
A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent
a series of CT examinations at our hospital between 2012 and 2022 were included in
this study. Forty-one radiomic features were extracted from each volumetric region
of interest. Radiomic features combined with conventional clinical and semantic parameters
were then selected for radiomic-based model building. To investigate the model performance
in terms of substantial SSN growth and stage shift growth, the model performance was
compared by the area under the curve (AUC) obtained by receiver operating characteristic
analysis.
Results
The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based
model (Model 2) based on clinical characteristics, CT semantic features, and radiomic
features yielded an AUCs of 0.869 (95% CI: 0.799–0.922). In comparison with Model
1 (clinical characteristics and CT semantic features), Model 2 performed better than
Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p = 0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic
features was built for substantial SSN growth prediction. For the stage shift growth,
a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features,
and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815–0.933). Compared with
Model 3 (clinical characteristics and CT semantic features), Model 4 performed better
than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883,
p = 0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification,
follow-up period, and three radiomic features was built to predict the stage shift
growth.
Conclusion
Radiomic-based models have superior utility in estimating the prognostic interval
growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic
models in terms of substantial interval growth and stage shift growth, potentially
guiding clinical decision-making with follow-up strategies of SSNs in personalized
precision medicine.
Key Word
Abbreviations:
AUC (area under the curve), CI (confidence interval), GGNs (groundglass nodules), HU (hounsfield units), IPA (invasive pulmonary adenocarcinoma), LR (likelihood ratio), NPV (negative predictive value), OR (odds ratio), PSNs (part-solid nodules), PPV (positive predictive value), PACS (picture archiving and communication system), ROI (regions of interest), ROC (receiver operating characteristic), RQS (radiomics quality score), SD (standard deviation), SSNs (subsolid nodules), VDT (volume doubling time), VOI (volume of interest)To read this article in full you will need to make a payment
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REFERENCES
- Screening for lung cancer: US preventive services task force recommendation statement.JAMA. 2021; 325: 962-970
- Lung cancer screening in never-smokers: facts and remaining issues.Eur Respir J. 2020; 562002949
- Screening for lung cancer in individuals who never smoked: an international association for the study of lung cancer early detection and screening committee report.J Thorac Oncol. 2022; 17: 56-66
- Association of computed tomographic screening promotion with lung cancer overdiagnosis among Asian women.JAMA Intern Med. 2022; 182: 283-290
- Reduced lung-cancer mortality with low-dose computed tomographic screening.N Engl J Med. 2011; 365: 395-409
- International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary Classification of Lung Adenocarcinoma.J Thorac Oncol. 2011; 6: 244-285
- CT screening for lung cancer: nonsolid nodules in baseline and annual repeat rounds.Radiology. 2015; 277: 555-564
- CT screening for lung cancer: part-solid nodules in baseline and annual repeat rounds.AJR Am J Roentgenol. 2016; 207: 1176-1184
- Assessment of selection criteria for low-dose lung screening CT among Asian ethnic groups in Taiwan: from mass screening to specific risk-based screening for non-smoker lung cancer.Clin Lung Cancer. 2016; 17: e45-e56
- Modified lung-RADS improves performance of screening LDCT in a population with high prevalence of non–smoking-related lung cancer.Acad Radiol. 2018; 25 (1240-51)
- Natural history of persistent pulmonary subsolid nodules: long-term observation of different interval growth.Heart Lung Circ. 2019; 28: 1747-1754
- Growth and clinical impact of 6-mm or larger subsolid nodules after 5 years of stability at chest CT.Radiology. 2020; 295: 448-455
- Natural history of pulmonary subsolid nodules: a prospective multicenter study.J Thorac Oncol. 2016; 11: 1012-1028
- The growth rate of subsolid lung adenocarcinoma nodules at chest CT.Radiology. 2020; 297: 189-198
- Subsolid pulmonary nodule morphology and associated patient characteristics in a routine clinical population.Eur Radiol. 2017; 27: 689-696
- Management of ground-glass nodules: when and how to operate?.Cancers (Basel). 2022; 14: 715
- Radiomics in early lung cancer diagnosis: from diagnosis to clinical decision support and education.Diagnostics (Basel). 2022; 12: 1064
- The natural growth of subsolid nodules predicted by quantitative initial CT features: a systematic review.Front Oncol. 2020; 10
- Contextualizing the role of volumetric analysis in pulmonary nodule assessment: AJR expert panel narrative review.AJR Am J Roentgenol. 2022;
- British Thoracic Society guidelines for the investigation and management of pulmonary nodules: accredited by NICE.Thorax. 2015; 70: ii1-ii54
- European position statement on lung cancer screening.Lancet Oncol. 2017; 18: e754-ee66
- Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017.Radiology. 2017; 284: 228-243
- LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity.Cancer Res. 2018; 78: 4786-4789
- A postreconstruction harmonization method for multicenter radiomic studies in PET.J Nucl Med. 2018; 59: 1321-1328
- A biomathematical approach to clinical tumor growth.Cancer. 1961; 14: 1272-1294
- Pulmonary ground-glass nodules: increase in mass as an early indicator of growth.Radiology. 2010; 255: 199-206
- Prognostic significance of adenocarcinoma in situ, minimally invasive adenocarcinoma, and nonmucinous lepidic predominant invasive adenocarcinoma of the lung in patients with stage I disease.Am J Surg Pathol. 2014; 38: 448-460
- Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.Biometrics. 1988; 44: 837-845
- Characteristics of subsolid pulmonary nodules showing growth during follow-up with CT scanning.Chest. 2013; 143: 436-443
- The long-term course of ground-glass opacities detected on thin-section computed tomography.Respir Med. 2013; 107: 904-910
- Measurement of focal ground-glass opacity diameters on CT images: interobserver agreement in regard to identifying increases in the size of ground-glass opacities.Acad Radiol. 2012; 19: 389-394
- Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.Thorac Cancer. 2022; 13: 602-612
- A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.Sci Rep. 2021; 11: 66
- Prediction of the growth rate of early-stage lung adenocarcinoma by radiomics.Front Oncol. 2021; : 11
- Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics.Thorac Cancer. 2020; 11: 2600-2609
- Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers.Cancer Biomark. 2022; 33: 489-501
- Feature selection methods and predictive models in CT lung cancer radiomics.J Appl Clin Med Phys. 2022; 24: e13869
- Active surveillance or early resection for ground-glass nodules that need preoperative localization.J Surg Oncol. 2021; 123: 322-331
- Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography.Acad Radiol. 2019; 26: 1245-1252
- Optimal management of pulmonary ground-glass opacity nodules.Transl Lung Cancer Res. 2019; 8: S418-Ss24
- Shades of gray: subsolid nodule considerations and management.Chest. 2021; 159: 2072-2089
- Prognostic effect of implementation of the mass low-dose computed tomography lung cancer screening program: a hospital-based cohort study.Eur J Cancer Prev. 2020; 29: 445-451
- Feasibility of lung cancer screening in developing countries: challenges, opportunities and way forward.Transl Lung Cancer Res. 2019; 8: S106-SS21
- Inter-Reader Variability of Volumetric Subsolid Pulmonary Nodule Radiomic Features.Acad Radiol. 2022; 29: S98-s107
- Growth assessment of pulmonary adenocarcinomas manifesting as subsolid nodules on CT: comparison of diameter-based and volume measurements.Acad Radiol. 2020; 27: 1385-1393
- Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement.Eur Radiol. 2020; 30: 523-536
Article info
Publication history
Published online: April 18, 2023
Accepted:
February 27,
2023
Received in revised form:
December 23,
2022
Received:
November 15,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
