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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.

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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

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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

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  1. Doohyun Park
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Correspondence to Dosik Hwang.

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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.

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One of the authors has significant statistical expertise.

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• multicenter study

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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

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

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