Skip to main content

Advertisement

SpringerLink
Log in

Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation

  • Chest
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients.

Methods

A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively.

Results

A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732–0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823–0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613–0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728–0.881) in the testing set.

Conclusion

This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization.

Clinical relevance statement

This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment.

Key Points

• Combination model that included rad-score and clinical features had a great predicting performance.

• Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models.

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

Abbreviations

AUC:

Area under the curve

BMI:

Body mass index

CT:

Computed tomography

CTV:

Clinical target volume

EC:

Esophageal cancer

ESCC:

Esophageal squamous cell carcinoma

GTV:

Gross tumor volume

KPS:

Karnofsky performance status

LOOCV:

Leave-one-out cross-validation

MRI:

Magnetic resonance imaging

nCRT:

Neoadjuvant chemoradiotherapy

NPV:

Negative predictive value

OAR:

Organs at risk

OS:

Overall survival

pCR:

Pathological complete response

PD:

Program death

PET:

Positron emission tomography

PFS:

Progression-free survival

ppCR:

Primary tumor lesion pCR

PPV:

Positive predictive value

PTV:

Plan target volume

ROC:

Receiver operating characteristics

TNR:

True negative rate

tpCR:

Total pCR

TPR:

True positive rate

VOI:

Volumn of interest

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72: https://doi.org/10.3322/caac.21708

  2. Eyck BM, van Lanschot JJB, Hulshof MCCM et al (2021) Ten-year outcome of neoadjuvant chemoradiotherapy plus surgery for esophageal cancer: the randomized controlled CROSS trial. J Clin Oncol 39:1995–2004. https://doi.org/10.1200/JCO.20.03614

    Article  CAS  PubMed  Google Scholar 

  3. Yang H, Liu H, Chen Y, et al (2018) Neoadjuvant chemoradiotherapy followed by surgery versus surgery alone for locally advanced squamous cell carcinoma of the esophagus (NEOCRTEC5010): a phase III multicenter, randomized, open-label clinical trial. J Clin Oncol 36: https://doi.org/10.1200/JCO.2018.79.1483

  4. Donahue JM, Nichols FC, Li Z, et al (2009) Complete pathologic response after neoadjuvant chemoradiotherapy for esophageal cancer is associated with enhanced survival. Ann Thorac Surg 87: https://doi.org/10.1016/j.athoracsur.2008.11.001

  5. Kelly RJ, Ajani JA, Kuzdzal J, et al (2021) Adjuvant nivolumab in resected esophageal or gastroesophageal junction cancer. N Engl J Med 384: https://doi.org/10.1056/nejmoa2032125

  6. Dittrick GW, Weber JM, Shridhar R, et al (2012) Pathologic nonresponders after neoadjuvant chemoradiation for esophageal cancer demonstrate no survival benefit compared with patients treated with primary esophagectomy. Ann Surg Oncol 19: https://doi.org/10.1245/s10434-011-2078-4

  7. Yang Z, He B, Zhuang X, et al (2019) CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy. J Radiat Res 60: https://doi.org/10.1093/jrr/rrz027

  8. Qu J, Ma L, Lu Y, et al (2022) DCE-MRI radiomics nomogram can predict response to neoadjuvant chemotherapy in esophageal cancer. Discover Oncol 13: https://doi.org/10.1007/s12672-022-00464-7

  9. van Rossum PSN, van Lier ALHMW, van Vulpen M, et al (2015) Diffusion-weighted magnetic resonance imaging for the prediction of pathologic response to neoadjuvant chemoradiotherapy in esophageal cancer. Radiother Oncol 115: https://doi.org/10.1016/j.radonc.2015.04.027

  10. Tan S, Kligerman S, Chen W, et al (2013) Spatial-temporal [18F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys 85: https://doi.org/10.1016/j.ijrobp.2013.09.037

  11. Zhu Y, Yao W, Xu BC, et al (2021) Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers. BMC Cancer 21: https://doi.org/10.1186/s12885-021-08899-x

  12. Zhang X, Gari A, Li M, et al (2022) Combining serum inflammation indexes at baseline and post treatment could predict pathological efficacy to anti PD 1 combined with neoadjuvant chemotherapy in esophageal squamous cell carcinoma. J Transl Med 20: https://doi.org/10.1186/s12967-022-03252-7

  13. Toxopeus ELA, Nieboer D, Shapiro J, et al (2015) Nomogram for predicting pathologically complete response after neoadjuvant chemoradiotherapy for oesophageal cancer. Radiother Oncol 115: https://doi.org/10.1016/j.radonc.2015.04.028

  14. van Rossum PSN, Fried DV, Zhang L, et al (2016) The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med 57: https://doi.org/10.2967/jnumed.115.163766

  15. Hu Y, Xie C, Yang H et al (2021) Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma. Radiother Oncol 154:6–13. https://doi.org/10.1016/j.radonc.2020.09.014

    Article  CAS  PubMed  Google Scholar 

  16. Duong C, Greenawalt DM, Kowalczyk A et al (2007) Pretreatment gene expression profiles can be used to predict response to neoadjuvant chemoradiotherapy in esophageal cancer. Ann Surg Oncol 14:3602–3609. https://doi.org/10.1245/s10434-007-9550-1

    Article  PubMed  Google Scholar 

  17. Cheng H, Garrick DJ, Fernando RL (2017) Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction. J Anim Sci Biotechnol 8:38. https://doi.org/10.1186/s40104-017-0164-6

    Article  PubMed  PubMed Central  Google Scholar 

  18. Shao Z, Er MJ, Wang N (2016) An efficient leave-one-out cross-validation-based extreme learning machine (ELOO-ELM) with minimal user intervention. IEEE Trans Cybern 46:1939–1951. https://doi.org/10.1109/TCYB.2015.2458177

    Article  PubMed  Google Scholar 

  19. Jeong YS, Jeon M, Park JH et al (2021) Machine-learning-based approach to differential diagnosis in tuberculous and viral meningitis. Infect Chemother 53:53–62. https://doi.org/10.3947/ic.2020.0104

    Article  CAS  PubMed  Google Scholar 

  20. Cheng J, Dekkers JCM, Fernando RL (2021) Cross-validation of best linear unbiased predictions of breeding values using an efficient leave-one-out strategy. J Anim Breed Genet 138:519–527. https://doi.org/10.1111/jbg.12545

    Article  PubMed  Google Scholar 

  21. Nagata T, Noyori SS, Noguchi H et al (2021) Skin tear classification using machine learning from digital RGB image. J Tissue Viability 30:588–593. https://doi.org/10.1016/j.jtv.2021.01.004

    Article  PubMed  Google Scholar 

  22. Li M, Jin Y-M, Zhang Y-C et al (2021) Radiomics for predicting perineural invasion status in rectal cancer. World J Gastroenterol 27:5610–5621. https://doi.org/10.3748/wjg.v27.i33.5610

    Article  PubMed  PubMed Central  Google Scholar 

  23. Xu W, Wu W, Zheng Y et al (2021) A computed tomography radiomics-based prediction model on interstitial lung disease in anti-MDA5-positive dermatomyositis. Front Med (Lausanne) 8:768052. https://doi.org/10.3389/fmed.2021.768052

    Article  PubMed  Google Scholar 

  24. Sun Y, Li C, Jin L et al (2020) Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction. Eur Radiol 30:3650–3659. https://doi.org/10.1007/s00330-020-06776-y

    Article  PubMed  PubMed Central  Google Scholar 

  25. Cui Y, Li Z, Xiang M et al (2022) Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures. Radiat Oncol 17:212. https://doi.org/10.1186/s13014-022-02186-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This work was supported by the grants from the Zhejiang province public welfare funds (No. GF20H160009) and the Medical Science and Technology Project of Zhejiang Province (No. 2020382901, and 2021455891).

Author information

Author notes

  1. J Wang, X Zhu, and J Zeng contributed equally and should be considered co-first authors

Authors and Affiliations

  1. Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China

    Jin Wang, Xiang Zhu, Xiaojiang Sun, Qingren Lin, Jun Fang & Yongling Ji

  2. Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China

    Jian Zeng & Qixun Chen

  3. Philips Healthcare, Shanghai, China

    Cheng Liu & Wei Shen

Authors
  1. Jin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaojiang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qingren Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jun Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qixun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yongling Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

WJ: data collection, statistical analysis, and writing and revising the manuscript. ZX, ZJ, LC, SW: statistical analysis and revising the manuscript. ZJ, SXJ: patient administration, and data collection. FJ, LQR: patient administration, and critical revision of the manuscript. JYL and CQX study design, statistical analysis, critical revision of the manuscript, and funds collection.

Corresponding authors

Correspondence to Jin Wang, Qixun Chen or Yongling Ji.

Ethics declarations

Guarantor

The scientific guarantor of this publication Yongling Ji.

Conflict of interest

The authors declare that they have no competing interests.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Not applicable.

Ethical approval

The study was approved by our hospital’s institutional review board.

Study subjects or cohorts overlap

No study subjects or cohorts have been previously reported.

Methodology

• retrospective

• observational

• performed at one institution

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 (PDF 416 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

Wang, J., Zhu, X., Zeng, J. et al. Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation. Eur Radiol 33, 8554–8563 (2023). https://doi.org/10.1007/s00330-023-09884-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-09884-7

Keywords

Search

Navigation