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01-12-2018 | Chest

The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules

Authors: Yunlang She, Lei Zhang, Huiyuan Zhu, Chenyang Dai, Dong Xie, Huikang Xie, Wei Zhang, Lilan Zhao, Liling Zou, Ke Fei, Xiwen Sun, Chang Chen

Published in: European Radiology | Issue 12/2018

Abstract

Objectives

Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are assumed to be indolent lung adenocarcinoma with excellent prognosis. We aim to identify these lesions from invasive adenocarcinoma (IA) by a radiomics approach.

Methods

This retrospective study was approved by institutional review board with a waiver of informed consent. Pathologically confirmed lung adenocarcinomas manifested as lung nodules less than 3 cm were retrospectively identified. In-house software was used to quantitatively extract 60 CT-based radiomics features quantifying nodule’s volume, intensity and texture property through manual segmentation. In order to differentiate AIS/MIA from IA, least absolute shrinkage and selection operator (LASSO) logistic regression was used for feature selection and developing radiomics signatures. The predictive performance of the signature was evaluated via receiver operating curve (ROC) and calibration curve, and validated using an independent cohort.

Results

402 eligible patients were included and divided into the primary cohort (n = 207) and the validation cohort (n = 195). Using the primary cohort, we developed a radiomics signature based on five radiomics features. The signature showed good discrimination between MIA/AIS and IA in both the primary and validation cohort, with AUCs of 0.95 (95% CI, 0.91–0.98) and 0.89 (95% CI, 0.84–0.93), respectively. Multivariate logistic analysis revealed that the signature (OR, 13.3; 95% CI, 6.2–28.5; p < 0.001) and gender (OR, 3.5; 95% CI, 1.2–10.9; p = 0.03) were independent predictors of indolent lung adenocarcinoma.

Conclusion

The signature based on radiomics features helps to differentiate indolent from invasive lung adenocarcinoma, which might be useful in guiding the intervention choice for patients with pulmonary nodules.

Key points

• Based on radiomics features, a signature is established to differentiate adenocarcinoma in situ and minimally invasive adenocarcinoma from invasive lung adenocarcinoma.

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Travis WD, Brambilla E, Noguchi M et al (2011) International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society International Multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 6:244–285CrossRef

Travis WD, Brambilla E, Nicholson AG et al (2015) The 2015 World Health Organization classification of lung tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol 10:1243–1260CrossRef

Yoshizawa A, Motoi N, Riely GJ et al (2011) Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases. Mod Pathol 24:653–664CrossRef

Woo T, Okudela K, Mitsui H et al (2012) Prognostic value of the IASLC/ATS/ERS classification of lung adenocarcinoma in stage I disease of Japanese cases. Pathol Int 62:785–791CrossRef

Kadota K, Villena-Vargas J, Yoshizawa A et al (2014) 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 38:448–460CrossRef

Liu S, Wang R, Zhang Y et al (2016) Precise diagnosis of intraoperative frozen section is an effective method to guide resection strategy for peripheral small-sized lung adenocarcinoma. J Clin Oncol 34:307–313CrossRef

Yeh YC, Nitadori J, Kadota K et al (2015) Using frozen section to identify histological patterns in stage I lung adenocarcinoma of </= 3 cm: accuracy and interobserver agreement. Histopathology 66:922–938CrossRef

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef

Verma V, Simone CB, Krishnan S, Lin SH, Yang J, Hahn SM (2017) The rise of radiomics and implications for oncologic management. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djx055

10.

Lee G, Lee HY, Park H et al (2017) Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: state of the art. Eur J Radiol 86:297–307CrossRef

11.

Son JY, Lee HY, Lee KS et al (2014) Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. PLoS One 9:e104066CrossRef

12.

Chae HD, Park CM, Park SJ, Lee SM, Kim KG, Goo JM (2014) Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 273:285–293CrossRef

13.

Hwang IP, Park CM, Park SJ et al (2015) Persistent pure ground-glass nodules larger than 5 mm: differentiation of invasive pulmonary adenocarcinomas from preinvasive lesions or minimally invasive adenocarcinomas using texture analysis. Invest Radiol 50:798–804CrossRef

14.

Hawkins S, Wang H, Liu Y et al (2016) Predicting malignant nodules from screening CT scans. J Thorac Oncol 11:2120–2128CrossRef

15.

Ost DE, Gould MK (2012) Decision making in patients with pulmonary nodules. Am J Respir Crit Care Med 185:363–372CrossRef

16.

Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRef

17.

McNeish DM (2015) Using Lasso for predictor selection and to assuage overfitting: a method long overlooked in behavioral sciences. Multivariate Behav Res 50:471–484CrossRef

18.

Jiang Y, Zhang Q, Hu Y et al (2016) ImmunoScore signature: a prognostic and predictive tool in gastric cancer. Ann Surg 267:504–513CrossRef

19.

Guo BL, Ouyang FS, Yang SM et al (2017) Development of a preprocedure nomogram for predicting contrast-induced acute kidney injury after coronary angiography or percutaneous coronary intervention. Oncotarget 8:75087–75093PubMedPubMedCentral

20.

Rios Velazquez E, Parmar C, Liu Y et al (2017) Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 77:3922–3930CrossRef

21.

Lee SH, Lee SM, Goo JM et al (2014) Usefulness of texture analysis in differentiating transient from persistent part-solid nodules (PSNs): a retrospective study. PLoS One 9:e85167CrossRef

22.

Song SH, Park H, Lee G et al (2017) Imaging phenotyping using radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol 12:624–632CrossRef

23.

Coroller TP, Agrawal V, Huynh E et al (2017) Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol 12:467–476CrossRef

24.

Coroller TP, Grossmann P, Hou Y et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350CrossRef

25.

Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRef

26.

Zhang JX, Song W, Chen ZH et al (2013) Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol 14:1295–1306CrossRef

27.

Bankier AA, MacMahon H, Goo JM, Rubin GD, Schaefer-Prokop CM, Naidich DP (2017) Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology. https://doi.org/10.1148/radiol.2017162894 CrossRef

28.

Davnall F, Yip CS, Ljungqvist G et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3:573–589CrossRef

Title: The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules
Authors: Yunlang She
Lei Zhang
Huiyuan Zhu
Chenyang Dai
Dong Xie
Huikang Xie
Wei Zhang
Lilan Zhao
Liling Zou
Ke Fei
Xiwen Sun
Chang Chen
Publication date: 01-12-2018
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 12/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5509-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules

Abstract

Objectives

Methods

Results

Conclusion

Key points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Key points

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