Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To analyze whether CT image normalization can improve 3-year recurrence-free survival (RFS) prediction performance in patients with non-small cell lung cancer (NSCLC) relative to the use of unnormalized CT images.

Methods

A total of 106 patients with NSCLC were included in the training set. For each patient, 851 radiomic features were extracted from the normalized and the unnormalized CT images, respectively. After the feature selection, random forest models were constructed with selected radiomic features and clinical features. The models were then externally validated in the test set consisting of 79 patients with NSCLC.

Results

The model using normalized CT images yielded better performance than the model using unnormalized CT images (with an area under the receiver operating characteristic curve of 0.802 vs 0.702, p = 0.01), with the model performing especially well among patients with adenocarcinoma (with an area under the receiver operating characteristic curve of 0.880 vs 0.720, p < 0.01).

Conclusions

CT image normalization may improve prediction performance among patients with NSCLC, especially for patients with adenocarcinoma.

Key Points

• After CT image normalization, more radiomic features were able to be identified.

• Prognostic performance in patients was improved significantly after CT image normalization compared with before the CT image normalization.

• The improvement in prognostic performance following CT image normalization was superior in patients with adenocarcinoma.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

AC:

Adenocarcinoma

AUC:

Area under the receiver operating characteristic curve

CT:

Computed tomography

GGO:

Ground-glass opacity

GLCM:

Gray level co-occurrence matrix

GLSZM:

Gray level size zone matrix

HR:

Hazard ratio

NSCLC:

Non-small cell lung cancer

RF:

Random forest

SqCC:

Squamous cell carcinoma

TNM:

Tumor-node-metastasis

References

  1. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34. https://doi.org/10.3322/caac.21551

    Article  PubMed  Google Scholar 

  2. Crosbie PA, Shah R, Summers Y, Dive C, Blackhall F (2013) Prognostic and predictive biomarkers in early stage NSCLC: CTCs and serum/plasma markers. Transl Lung Cancer Res 2:382. https://doi.org/10.3978/j.issn.2218-6751.2013.09.02

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Detterbeck FC, Boffa DJ, Tanoue LT (2009) The new lung cancer staging system. Chest 136:260–271. https://doi.org/10.1378/chest.08-0978

    Article  PubMed  Google Scholar 

  4. Lee SY, Jung DK, Choi JE et al (2017) Functional polymorphisms in PD-L1 gene are associated with the prognosis of patients with early stage non-small cell lung cancer. Gene 599:28–35. https://doi.org/10.1016/j.gene.2016.11.007

    Article  CAS  PubMed  Google Scholar 

  5. Lee SY, Jin CC, Choi JE et al (2016) Genetic polymorphisms in glycolytic pathway are associated with the prognosis of patients with early stage non-small cell lung cancer. Sci Rep 6:35603. https://doi.org/10.1038/srep35603

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Aoki T, Hanamiya M, Uramoto H, Hisaoka M, Yamashita Y, Korogi Y (2012) Adenocarcinomas with predominant ground-glass opacity: correlation of morphology and molecular biomarkers. Radiology 264:590–596. https://doi.org/10.1148/radiol.12111337

    Article  PubMed  Google Scholar 

  7. Lee HY, Lee SW, Lee KS et al (2015) Role of CT and PET imaging in predicting tumor recurrence and survival in patients with lung adenocarcinoma: a comparison with the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society Classification of Lung Adenocarcinoma. J Thorac Oncol 10:1785–1794. https://doi.org/10.1097/JTO.0000000000000689

    Article  CAS  PubMed  Google Scholar 

  8. Aerts HJWL, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:1–9. https://doi.org/10.1038/ncomms5006

    Article  CAS  Google Scholar 

  9. Liu Z, Wang S, Dong D et al (2019) The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics 9:1303. https://doi.org/10.7150/thno.30309

    Article  PubMed  PubMed Central  Google Scholar 

  10. Oh D, Kim S, Park D et al (2018) Correction of severe beam-hardening artifacts via a high-order linearization function using a prior-image-based parameter selection method. Med Phys 45:4133–4144. https://doi.org/10.1002/mp.13072

  11. Kim Y, Oh D, Hwang D (2017) Small-scale noise-like moiré pattern caused by detector sensitivity inhomogeneity in computed tomography. Opt Express 25:27127–27145. https://doi.org/10.1364/OE.25.027127

    Article  PubMed  Google Scholar 

  12. Kim Y, Baek J, Hwang D (2014) Ring artifact correction using detector line-ratios in computed tomography. Opt Express 22:13380–13392. https://doi.org/10.1364/OE.22.013380

    Article  PubMed  Google Scholar 

  13. Eo T, Jun Y, Kim T, Jang J, Lee HJ, Hwang D (2018) KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn Reson Med 80:2188–2201. https://doi.org/10.1002/mrm.27201

    Article  CAS  PubMed  Google Scholar 

  14. Eo T, Shin H, Jun Y, Kim T, Hwang D (2020) Accelerating Cartesian MRI by domain-transform manifold learning in phase-encoding direction. Med Image Anal 63:101689. https://doi.org/10.1016/j.media.2020.101689

    Article  PubMed  Google Scholar 

  15. Shafiq-ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep 8:1–9. https://doi.org/10.1038/s41598-018-28895-9

    Article  CAS  Google Scholar 

  16. Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158. https://doi.org/10.1016/j.ijrobp.2018.05.053

    Article  PubMed  PubMed Central  Google Scholar 

  17. Choe J, Lee S, Do K et al (2019) Deep learning–based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 292:365–373. https://doi.org/10.1148/radiol.2019181960

    Article  PubMed  Google Scholar 

  18. Berenguer R, Pastor-Juan MR, Canales-Vázquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–415. https://doi.org/10.1148/radiol.2018172361

    Article  PubMed  Google Scholar 

  19. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I (2019) Validation of a method to compensate multicenter effects affecting CT radiomics. Radiology 291:53–59. https://doi.org/10.1148/radiol.2019182023

    Article  PubMed  Google Scholar 

  20. Park BW, Kim JK, Heo C, Park KJ (2020) Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters. Sci Rep 10:1–13. https://doi.org/10.1038/s41598-020-60868-9

    Article  CAS  Google Scholar 

  21. Kawase A, Yoshida J, Ishii G et al (2011) Differences between squamous cell carcinoma and adenocarcinoma of the lung: are adenocarcinoma and squamous cell carcinoma prognostically equal? Jpn J Clin Oncol 42:189–195. https://doi.org/10.1093/jjco/hyr188

    Article  PubMed  Google Scholar 

  22. Gallardo-Estrella L, Lynch DA, Prokop M et al (2016) Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification. Eur Radiol 26:478–486. https://doi.org/10.1007/s00330-015-3824-y

    Article  PubMed  Google Scholar 

  23. Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001

    Article  PubMed  PubMed Central  Google Scholar 

  24. Griethuysen JJ, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Parmar C, Velazquez ER, Leijenaar R et al (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9:e102107. https://doi.org/10.1371/journal.pone.0102107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Owens CA, Peterson CB, Tang C et al (2018) Lung tumor segmentation methods: impact on the uncertainty of radiomics features for non-small cell lung cancer. PLoS One 13:e0205003. https://doi.org/10.1371/journal.pone.0205003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kim S, Bae WC, Masuda K, Chung CB, Hwang D (2018) Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net. Appl Sci Basel 8:1656. https://doi.org/10.3390/app8091656

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kim S, Bae WC, Masuda K, Chung CB, Hwang D (2018) Semi-automatic segmentation of vertebral bodies in MR images of human lumbar spines. Appl Sci Basel 8:1586. https://doi.org/10.3390/app8091586

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zhao B, James LP, Moskowitz CS et al (2009) Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non–small cell lung cancer. Radiology 252:263–272. https://doi.org/10.1148/radiol.2522081593

    Article  PubMed  PubMed Central  Google Scholar 

  30. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087. https://doi.org/10.1038/srep13087

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60

    Article  Google Scholar 

  32. Woodard GA, Jones KD, Jablons DM (2016) Lung cancer staging and prognosis. Lung Cancer 170:47–75. https://doi.org/10.1007/978-3-319-40389-2_3

    Article  Google Scholar 

  33. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  34. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845. https://doi.org/10.2307/2531595

    Article  CAS  PubMed  Google Scholar 

  35. Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457–481. https://doi.org/10.1080/01621459.1958.10501452

    Article  Google Scholar 

  36. Cox DR (1972) Regression models and life-tables. J R Stat Soc Ser B Stat Methodol 34:187–202. https://doi.org/10.1111/j.2517-6161.1972.tb00899.x

    Article  Google Scholar 

  37. Mantel N (1966) Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep 50:163–170

    CAS  PubMed  Google Scholar 

  38. Clausi DA (2002) An analysis of co-occurrence texture statistics as a function of grey level quantization. Can Aeronaut Space J 28:45–62. https://doi.org/10.5589/m02-004

    Article  Google Scholar 

  39. Moon Y, Sung SW, Moon SW, Park JK (2016) Risk factors for recurrence after sublobar resection in patients with small (2 cm or less) non-small cell lung cancer presenting as a solid-predominant tumor on chest computed tomography. J Thorac Dis 8:2018. https://doi.org/10.21037/jtd.2016.07.90

    Article  PubMed  PubMed Central  Google Scholar 

  40. Hattori A, Matsunaga T, Takamochi K, Oh S, Suzuki K (2017) Importance of ground glass opacity component in clinical stage IA radiologic invasive lung cancer. Ann Thorac Surg 104:313–320. https://doi.org/10.1016/j.athoracsur.2017.01.076

    Article  PubMed  Google Scholar 

  41. Bakr S, Gevaert O, Echegaray S et al (2018) A radiogenomic dataset of non-small cell lung cancer. Sci Data 5:1–9. https://doi.org/10.1038/sdata.2018.202

    Article  CAS  Google Scholar 

  42. Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative. arXiv 1612:07003. https://doi.org/10.48550/arXiv.1612.07003

    Article  Google Scholar 

  43. Lambin P, Leijenaar RT, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762. https://doi.org/10.1038/nrclinonc.2017.141

    Article  PubMed  Google Scholar 

  44. Chen Q, Zhang L, Mo X et al (2021) Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 49:345–360. https://doi.org/10.1007/s00259-021-05509-7

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

 This research was supported by D&P BIOTECH Inc. and partially supported by the Yonsei Signature Research Cluster Program of 2022 (2022-22-0002), the KIST Institutional Program(Project No.2E31051-21-204), the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT) Artificial Intelligence Graduate School Program, Yonsei University (2020-0-01361), and the Graduate School of YONSEI University Research Scholarship Grants in 2018. The authors sincerely thank In Yong Park for his diligent proofreading of this paper.

Funding

This research was funded by D&P BIOTECH Inc.

Author information

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea

    Doohyun Park, Daejoong Oh & Dosik Hwang

  2. D&P BIOTECH Inc., Seoul, Republic of Korea

    Daejoong Oh, MyungHoon Lee & Johnson SG Jun

  3. Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea

    Shin Yup Lee

  4. Lung Cancer Center, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea

    Shin Yup Lee

  5. Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea

    Kyung Min Shin

  6. Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul, 02792, Republic of Korea

    Dosik Hwang

  7. Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea

    Dosik Hwang

  8. Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Republic of Korea

    Dosik Hwang

Authors
  1. Doohyun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daejoong Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. MyungHoon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shin Yup Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kyung Min Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Johnson SG Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dosik Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dosik Hwang.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Prof. Dosik Hwang.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: D&P BIOTECH Inc. Mr. Park, Mr. Oh, Dr. Lee, Dr. Jun, and Dr. Hwang have a patent “METHOD FOR PREDICTING PROGNOSIS IN CANCER PATIENT USING CLINICAL INFORMATION AND RADIOMIC FEATURE” pending. Dr. Shin and Dr. Lee have nothing to disclose.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 7829 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, D., Oh, D., Lee, M. et al. Importance of CT image normalization in radiomics analysis: prediction of 3-year recurrence-free survival in non-small cell lung cancer. Eur Radiol 32, 8716–8725 (2022). https://doi.org/10.1007/s00330-022-08869-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-022-08869-2

Keywords

Search

Navigation