Skip to main content
SpringerLink
Log in

Development and Validation of Multi-Omics Thymoma Risk Classification Model Based on Transfer Learning

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

Abstract

The paper aims to develop prediction model that integrates clinical, radiomics, and deep features using transfer learning to stratifying between high and low risk of thymoma. Our study enrolled 150 patients with thymoma (76 low-risk and 74 high-risk) who underwent surgical resection and pathologically confirmed in Shengjing Hospital of China Medical University from January 2018 to December 2020. The training cohort consisted of 120 patients (80%) and the test cohort consisted of 30 patients (20%). The 2590 radiomics and 192 deep features from non-enhanced, arterial, and venous phase CT images were extracted and ANOVA, Pearson correlation coefficient, PCA, and LASSO were used to select the most significant features. A fusion model that integrated clinical, radiomics, and deep features was developed with SVM classifiers to predict the risk level of thymoma, and accuracy, sensitivity, specificity, ROC curves, and AUC were applied to evaluate the classification model. In both the training and test cohorts, the fusion model demonstrated better performance in stratifying high and low risk of thymoma. It had AUCs of 0.99 and 0.95, and an accuracy of 0.93 and 0.83, respectively. This was compared to the clinical model (AUCs of 0.70 and 0.51, accuracy of 0.68 and 0.47), the radiomics model (AUCs of 0.97 and 0.82, accuracy of 0.93 and 0.80), and the deep model (AUCs of 0.94 and 0.85, accuracy of 0.88 and 0.80). The fusion model integrating clinical, radiomics and deep features based on transfer learning was efficient for noninvasively stratifying high risk and low risk of thymoma. The models could help to determine surgery strategy for thymoma cancer.

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

The dataset from Shengjing hospital was used under approval for the current study. Restrictions apply to the availability of this dataset and so it is not publicly available.

Abbreviations

LASSO:

Least absolute shrinkage and selection operator

SVM:

Support vector machines

CI:

Confidence intervals

VGG:

Visual geometry group

ROC:

Receiver operating characteristic curve

AUC:

Area under curve

DCA:

Decision curve analysis

ANOVA:

Analysis of variance

References

  1. https://www.cancer.org/cancer/thymus-.cancer/detection-diagnosis-staging/survival-rates.html.

  2. Travis WD, Burke, et al. WHO Classification of Tumors of the Lung, Pleura, Thymus and Heart. 4 edition[J].

  3. Jeong YJ, Lee KS, Kim J, et al. Does CT of Thymic Epithelial Tumors Enable Us to Differentiate Histologic Subtypes and Predict Prognosis?[J]. American Journal of Roentgenology, 2004, 183(2): 283- 289.

    Article  PubMed  Google Scholar 

  4. Motohiko Y, Kanako O, Hajime U, et al. Quantitative 3D Shape Analysis of CT Images of Thymoma: A Comparison With Histological Types[J].AJR AM J Roentgenol, 2020, 214(2):341–347.

  5. Lee H S, Oh J S, Park Y S, et al. Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via18F-FDG PET/CT[J]. Annals of Nuclear Medicine, 2016, 30(4):309-319.

    Article  CAS  PubMed  Google Scholar 

  6. Yasaka K, Akai H, Nojima M, et al. Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors[J].European Journal of Radiology, 2017:S0720048X17301651.

  7. Li B, Xin Y K, Xiao G, et al. Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters[J].European Radiology, 2019, 29(10):5330–5340

  8. Xiao G, Rong W C, Hu Y C, et al. MRI Radiomics Analysis for Predicting the Pathologic Classification and TNM Staging of Thymic Epithelial Tumors: A Pilot Study[J]. AJR AM J Roentgenol, 2020, 214(2):328-340.

    Article  PubMed  Google Scholar 

  9. Ayten Kayi Cangir et al, CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”. World Journal of Surgical Oncology (2021) 19:147.

  10. Wentao Dong et al, Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization. Frontiers in Oncology, (2022), https://doi.org/10.3389/fonc.2022.944005).

  11. Jin Liu et al, CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors. Frontiers in Oncology, (2021), https://doi.org/10.3389/fonc.2021.628534.

  12. Lan Shang et al, Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multicenter study. Frontiers in Oncology, 2022. https://doi.org/10.3389/fonc.2022.1043163

  13. Chunhai Yu et al, Contrast-enhanced CT-based radiomics model for diferentiating risk subgroups of thymic epithelial tumors. BMC Medical Imaging, 2022, 22:37. https://doi.org/10.1186/s12880-022-00768-8.

  14. Zhong, Y. et al. Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma. AJR Am. J. Roentgenol. 2018,211:109–113

    Article  PubMed  Google Scholar 

  15. Walsh TN, Noonan N, Hollywood D, Kelly A, Keeling N, Hennessy TP. A comparison of multimodal therapy and surgery for esophageal adenocarcinoma. N Engl J Med. 1996;335(7):462-467.

    Article  CAS  PubMed  Google Scholar 

  16. Dey N, et al. Customized VGG19 Architecture for Pneumonia Detection in Chest X-Rays. PATTERN RECOGNITION LETTERS, (2021):67–74.

  17. Skrede OJ, De Raedt S, Kleppe A, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 2020; 395: 350–60.

    Article  CAS  PubMed  Google Scholar 

  18. Shin, H. C. et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35, 1285–1298 (2016).

    Article  PubMed  Google Scholar 

  19. Huang YQ, Liang CH, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 2016; 34: 2157–64.

    Article  PubMed  Google Scholar 

  20. Chang, C. C. & Lin, C. J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).

    Article  Google Scholar 

  21. Ueno H, Ishiguro M, Nakatani E, et al. Prospective multicenter study on the prognostic and predictive impact of tumor budding in stage II colon cancer: results from the SACURA trial. J Clin Oncol 2019; 37: 1886–94.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Wenyu Gao et al, A predictive model integrating deep and radiomics features based on gadobenate dimeglumine‑enhanced MRI for postoperative early recurrence of hepatocellular carcinoma. La radiologia medica (2022) 127:259–271, https://doi.org/10.1007/s11547-021-01445-6.

    Article  PubMed  Google Scholar 

  23. Xueyi Zheng et al, Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. NATURE COMMUNICATIONS, https://doi.org/10.1038/s41467-020-15027-z

  24. Wei Wang, MD, PhD, et al. Development and Validation of a Computed Tomography–Based Radiomics Signature to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Gastric Cancer. JAMA network open 2021 4(8).

  25. D. Dong et al, Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study. Annals of oncology 2021 31:912-920.

    Article  Google Scholar 

  26. Song D et al, Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol. https://doi.org/10.1007/s00432.021.03617.3

  27. Ning Z et al, Pattern classification for gastrointestinal stromal tumors by integration of radiomics and deep convolutional features. IEEE J Biomed Health Inform 23(3):1181–1191. https://doi.org/10.1109/JBHI.2018.2841992

  28. Paul R et al, Predicting malignant nodules by fusing deep features with classical radiomics features. J Med Imaging (Bellingham) 5(1):011021. https://doi.org/10.1117/1.JMI.5.1.011021

  29. Xueyi Zheng et al, Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nature communications 2020. https://doi.org/10.1007/s00330-021-08237-6.

    Article  PubMed Central  PubMed  Google Scholar 

  30. Ruitian Gao et al, Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data. J Hematol Oncol, (2021) 144:154. https://doi.org/10.1186/s13045-021-01167-2.

    Article  Google Scholar 

Download references

Funding

The study was supported by natural science funding project of education department of Liaoning Province, China (No. LJKMZ20221196).

Author information

Author notes

  1. Wei Wang has contributed equally to this work.

Authors and Affiliations

  1. School of Health Management, China Medical University, Shenyang, China

    Wei Liu, Yingxin Xu & Xiaoqi Liu

  2. Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China

    Wei Wang

  3. Department of Radiology, Liaoning Cancer Hospital and Institute, Shenyang, China

    Hanyi Zhang

  4. Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China

    Miaoran Guo

Authors
  1. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hanyi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Miaoran Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yingxin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoqi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Liu.

Ethics declarations

Competing Interests

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.

Key Points

• The study introduces a novel method for clinical diagnosis of thymoma risk based on transfer learning technology.

• An integrated analysis of multi-omics and multi-modal features provided a strong foundation for a deeper understanding of tumor risk.

• The fusion model was efficient for noninvasively stratifying high and low risk of thymoma with an accuracy of 0.83, an AUC of 0.95, a sensitivity of 0.80 and a specificity of 1.00.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 13.4 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

Liu, W., Wang, W., Zhang, H. et al. Development and Validation of Multi-Omics Thymoma Risk Classification Model Based on Transfer Learning. J Digit Imaging 36, 2015–2024 (2023). https://doi.org/10.1007/s10278-023-00855-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-023-00855-4

Keywords

Search

Navigation