Skip to main content

Advertisement

SpringerLink
Log in

Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan–Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan–Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data Availability

All datasets are publicly available at The Cancer Imaging Archive (TCIA) (https://www.cancerimagingarchive.net/). The first cohort, Lung 1 (NSCLC-Radiomics): https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI. The second cohort, Lung 2 (NSCLC Radiogenomics): http://doi.org/10.7937/K9/TCIA.2017.7hs46erv. The third cohort, Head-Neck-Radiomics-HN1: https://doi.org/10.7937/tcia.2019.8kap372n. The fourth cohort, Training Set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19): https://doi.org/10.7937/TCIA.2019.IX49E8NX Source codes for analyses are made available online at https://github.com/huanlevietMD/Overall-survival-prediction-in-multi-organ-cancer.

References

  1. Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 2021. 71(3): p. 209-249.

    Article  PubMed  Google Scholar 

  2. Hoffman, P.C., A.M. Mauer, and E.E. Vokes, Lung cancer. Lancet, 2000. 355(9202): p. 479-485.

    Article  CAS  PubMed  Google Scholar 

  3. Pearson, F.G., Non-small cell lung cancer: role of surgery for stages I-III. Chest, 1999. 116: p. 500S-503S.

    Article  CAS  PubMed  Google Scholar 

  4. Khodabakhshi, Z., et al., Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information. J Digit Imaging, 2021. 34(5): p. 1086-1098.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pulte, D. and H.J.T.o. Brenner, Changes in survival in head and neck cancers in the late 20th and early 21st century: a period analysis. Oncologist, 2010. 15(9): p. 994.

  6. Aerts, H.J., The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review. JAMA Oncol, 2016. 2(12): p. 1636-1642.

    Article  PubMed  Google Scholar 

  7. GGillies, R., P. Kinahan, and H. Hricak, Radiomics: Images Are More than Pictures, They Are Data. Radiology, 2016. 278(2): p. 563–577.

  8. Le, N.Q.K., et al., Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI. Comput Biol Med, 2021. 132: p. 104320.

    Article  CAS  PubMed  Google Scholar 

  9. Zhang, B., et al., Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma. Clin Cancer Res, 2017. 23(15): p. 4259-4269.

    Article  PubMed  Google Scholar 

  10. Reiazi, R., et al., The impact of the variation of imaging parameters on the robustness of Computed Tomography radiomic features: A review. Comput Biol Med, 2021. 133: p. 104400.

    Article  PubMed  Google Scholar 

  11. Luo, W.Q., et al., Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS. Sci Rep, 2019. 9(1): p. 11921.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Liu, X., et al., Multiregional-Based Magnetic Resonance Imaging Radiomics Combined With Clinical Data Improves Efficacy in Predicting Lymph Node Metastasis of Rectal Cancer. Front Oncol, 2020. 10: p. 585767.

    Article  PubMed  Google Scholar 

  13. Chiesa-Estomba, C.M., et al., Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis. Cancers (Basel), 2019. 11(10).

  14. Le, V.-H., et al., Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer. Cancers (Basel), 2021. 13(14): p. 3616.

    Article  CAS  PubMed  Google Scholar 

  15. Soufi, M., H. Arimura, and N. Nagami, Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition‐based radiomic features. Med Phys, 2018. 45(11): p. 5116-5128.

    Article  PubMed  Google Scholar 

  16. Sun, W., et al., Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis. Radiat Oncol, 2018. 13(1): p. 1-8.

    Article  Google Scholar 

  17. Peng, Z., et al., Application of radiomics and machine learning in head and neck cancers. Int J Biol Sci, 2021. 17(2): p. 475.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yuan, Y., et al., MRI-based radiomic signature as predictive marker for patients with head and neck squamous cell carcinoma. Eur J Radiol, 2019. 117: p. 193-198.

    Article  PubMed  Google Scholar 

  19. Nazari, M., I. Shiri, and H. Zaidi, Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med, 2021. 129: p. 104135.

    Article  PubMed  Google Scholar 

  20. Yip, S.S. and H.J. Aerts, Applications and limitations of radiomics. Phys Med Biol, 2016. 61(13): p. R150-66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Baba, A.I. and C. Câtoi, Comparative oncology. 2007: Publishing House of the Romanian Academy Bucharest.

  22. García-Figueiras, R., et al., How clinical imaging can assess cancer biology. Insights Imaging, 2019. 10(1): p. 1-35.

    Article  Google Scholar 

  23. Aerts, H.J., et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014. 5: p. 4006.

    Article  CAS  PubMed  Google Scholar 

  24. Xu, L., et al., A multi-organ cancer study of the classification performance using 2D and 3D image features in radiomics analysis. Phys Med Biol, 2019. 64(21): p. 215009.

    Article  PubMed  Google Scholar 

  25. Lee, S.H., et al., Are radiomics features universally applicable to different organs? Cancer Imaging, 2021. 21(1): p. 31.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Clark, K., et al., The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging, 2013. 26(6): p. 1045-1057.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bakr, S., et al., A radiogenomic dataset of non-small cell lung cancer. Sci Data, 2018. 5: p. 180202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Heller, N., et al., The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge. Med Image Anal, 2021. 67: p. 101821.

    Article  PubMed  Google Scholar 

  29. van Griethuysen, J.J.M., et al., Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res, 2017. 77(21): p. e104-e107.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sugai, Y., et al., Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat Oncol, 2021. 16(1): p. 80.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Shukla, S., et al., Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma. J Natl Cancer Inst, 2017. 109(1).

  32. Chen, H.-Y., et al., A five-gene signature and clinical outcome in non–small-cell lung cancer. N Engl J Med, 2007. 356(1): p. 11-20.

    Article  CAS  PubMed  Google Scholar 

  33. Bae, S., et al., Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology, 2018. 289(3): p. 797-806.

    Article  PubMed  Google Scholar 

  34. Choi, Y., et al., Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models. Eur Radiol, 2021. 31(4): p. 2084-2093.

    Article  CAS  PubMed  Google Scholar 

  35. Wang, X., et al., Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT? Eur Radiol, 2019. 29(11): p. 6049-6058.

    Article  PubMed  Google Scholar 

  36. Li, H., et al., CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study. Front Oncol, 2020. 10: p. 57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kim, M.-J., et al., Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics. Transl Oncol, 2021. 14(10): p. 101180.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Blanche, P., J.-F. Dartigues, and H. Jacqmin-Gadda, Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med, 2013. 32(30): p. 5381-5397.

    Article  PubMed  Google Scholar 

  39. Yang, L., et al., Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients. Eur Radiol, 2019. 29(5): p. 2196-2206.

    Article  PubMed  Google Scholar 

  40. Mo, X., et al., Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation. Eur Radiol, 2020. 30(2): p. 833-843.

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by the National Science and Technology Council, Taiwan (grant numbers MOST110-2221-E-038–001-MY2 and MOST111-2628-E-038–002-MY3), and the Taiwan Higher Education Sprout Project by the Ministry of Education (grant number DP2-111–21121-01-A-12).

Author information

Authors and Affiliations

  1. International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan

    Viet Huan Le, Quang Hien Kha, Tran Nguyen Tuan Minh, Van Hiep Nguyen & Van Long Le

  2. Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang, 65000, Vietnam

    Viet Huan Le

  3. Oncology Center, Bai Chay Hospital, Quang Ninh, 20000, Vietnam

    Van Hiep Nguyen

  4. Department of Anesthesiology and Critical Care, Hue University of Medicine and Pharmacy, Hue University, Hue City, 52000, Vietnam

    Van Long Le

  5. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan

    Nguyen Quoc Khanh Le

  6. Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan

    Nguyen Quoc Khanh Le

  7. Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan

    Nguyen Quoc Khanh Le

Authors
  1. Viet Huan Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Quang Hien Kha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tran Nguyen Tuan Minh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Van Hiep Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Van Long Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nguyen Quoc Khanh Le
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Viet Huan Le: conceptualization, methodology, validation, formal analysis, investigation, writing—original draft preparation, visualization, Quang Hien Kha: validation, investigation, data curation, writing—original draft preparation, visualization, Tran Nguyen Tuan Minh: investigation, data curation, Van Hiep Nguyen: validation, data curation, Van Long Le: validation, visualization, Nguyen Quoc Khanh Le: conceptualization, methodology, validation, writing—review and editing, supervision, funding acquisition, All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Nguyen Quoc Khanh Le.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 63 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Le, V.H., Kha, Q.H., Minh, T.N.T. et al. Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer. J Digit Imaging 36, 911–922 (2023). https://doi.org/10.1007/s10278-023-00778-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-023-00778-0

Keywords

Search

Navigation