Low-Dose CT Screening of Persistent Subsolid Lung Nodules: First-Order Features in Radiomics

Abstract

Background Nondisappearing subsolid nodules requiring follow-up are often detected during lung cancer screening, but changes in their invasiveness can be overlooked owing to slow growth. We aimed to develop a method for automatic identification of invasive tumors among subsolid nodules during multiple health checkups using radiomics technology based on low-dose computed tomography (LD-CT) and examine its effectiveness.

Methods We examined patients who underwent LD-CT screening from 2014 to 2019 and had lung adenocarcinomas resected after 5-year follow-ups. They were categorized into the invasive or less-invasive group; the annual growth/change rate (Δ) of the nodule voxel histogram using three-dimensional CT (e.g., tumor volume, solid volume percentage, mean CT value, variance, kurtosis, skewness, and entropy) was assessed. A discriminant model was designed through multivariate regression analysis with internal validation to compare its efficacy with that of a volume doubling time of < 400 days.

Results The study included 47 tumors (23 invasive, 24 less invasive), with no significant difference in the initial tumor volumes. Δskewness was identified as an independent predictor of invasiveness (adjusted odds ratio, 0.021; p = 0.043), and when combined with Δvariance, it yielded high accuracy in detecting invasive lesions (88% true-positive, 80% false-positive). The detection model indicated surgery 2 years earlier than the volume doubling time, maintaining accuracy (median 3 years vs.1 year before actual surgery, p = 0.011).

Conclusion LD-CT radiomics showed promising potential in ensuring timely detection and monitoring of subsolid nodules that warrant follow-up over time.

Keywords

lung cancer treatment (surgery medical) - lung cancer - diagnosis (includes staging imaging fiducials) - minimally invasive surgery (includes port access minithoracotomy)

Authors' Contribution

Data collection: N.Y., D.Y.; design of the study: N.Y., F.K., T.B.; statistical analysis: N.Y., K.H.; analysis and interpretation of the data: N.Y., K.H., D.Y., F.K., T.B.; drafting the manuscript: N.Y., K.H., F.K., T.B.; critical revision of the manuscript: N.Y., D.Y., K.H., F.K., T.B.

Publication History

Received: 07 July 2023

Accepted: 17 August 2023

Accepted Manuscript online:
22 August 2023

Article published online:
25 September 2023

• References

• 1 World Health Organization. Global cancer observatory. Accessed December 20, 2021 at: http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx
• 2 Lung RADS. Assessment categories, version 1.0. American College of Radiology. Lung CT Screening Reporting and Data System (Lung-RADS™). Accessed February 15, 2021 at: https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads
• 3 de Koning HJ, van der Aalst CM, de Jong PA. et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med 2020; 382 (06) 503-513
• 4 Ettinger DS, Wood DE, Aggarwal C. et al; OCN. NCCN guidelines insights: non-small cell lung cancer, version 1.2020. J Natl Compr Canc Netw 2019; 17 (12) 1464-1472
• 5 Yoshiyasu N, Kojima F, Hayashi K, Bando T. Radiomics technology for identifying early-stage lung adenocarcinomas suitable for sublobar resection. J Thorac Cardiovasc Surg 2021; 162 (02) 477-485.e1
• 6 Oh J, Piao Z, Cho HJ. et al. CT-based three-dimensional invasiveness analysis of adenocarcinoma presenting as pure ground-glass nodules. Transl Cancer Res 2023; 12 (04) 765-773
• 7 Gao J, Qi Q, Li H. et al. Artificial-intelligence-based computed tomography histogram analysis predicting tumor invasiveness of lung adenocarcinomas manifesting as radiological part-solid nodules. Front Oncol 2023; 13: 1096453
• 8 Goldstraw P, Chansky K, Crowley J. et al; International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee, Advisory Boards, and Participating Institutions, International Association for the Study of Lung Cancer Staging and Prognostic Factors Committee Advisory Boards and Participating Institutions. The IASLC Lung Cancer Staging Project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2016; 11 (01) 39-51
• 9 Spratt JS, Spratt JA. The prognostic value of measuring the gross linear radial growth of pulmonary metastases and primary pulmonary cancers. J Thorac Cardiovasc Surg 1976; 71 (02) 274-278
• 10 Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278 (02) 563-577
• 11 Parmar C, Rios Velazquez E, Leijenaar R. et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 2014; 9 (07) e102107
• 12 Lambin P, Rios-Velazquez E, Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48 (04) 441-446
• 13 Vaidya P, Bera K, Gupta A. et al. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health 2020; 2 (03) e116-e128
• 14 Pérez-Morales J, Tunali I, Stringfield O. et al. Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening. Sci Rep 2020; 10 (01) 10528
• 15 Netto SMB, Silva AC, Nunes RA, Gattass M. Voxel-based comparative analysis of lung lesions in CT for therapeutic purposes. Med Biol Eng Comput 2017; 55 (02) 295-314
• 16 Chae HD, Park CM, Park SJ, Lee SM, Kim KG, Goo JM. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 2014; 273 (01) 285-293
• 17 Xu DM, Gietema H, de Koning H. et al. Nodule management protocol of the NELSON randomised lung cancer screening trial. Lung Cancer 2006; 54 (02) 177-184
• 18 Xu DM, van der Zaag-Loonen HJ, Oudkerk M. et al. Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON study: cancer risk during 1 year of follow-up. Radiology 2009; 250 (01) 264-272
• 19 de Margerie-Mellon C, Ngo LH, Gill RR. et al. The growth rate of subsolid lung adenocarcinoma nodules at chest CT. Radiology 2020; 297 (01) 189-198