Skip to main content

Advertisement

SpringerLink
Log in

Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model

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

Abstract

Objectives

To develop a multi-institutional prediction model to estimate the local response to oesophageal squamous cell carcinoma (ESCC) treated with definitive radiotherapy based on radiomics and dosiomics features.

Methods

The local responses were categorised into two groups (incomplete and complete). An external validation model and a hybrid model that the patients from two institutions were mixed randomly were proposed. The ESCC patients at stages I–IV who underwent chemoradiotherapy from 2012 to 2017 and had follow-up duration of more than 5 years were included. The patients who received palliative or pre-operable radiotherapy and had no FDG PET images were excluded. The segmentations included the GTV, CTV, and PTV which are used in treatment planning. In addition, shrinkage, expansion, and shell regions were created. Radiomic and dosiomic features were extracted from CT, FDG PET images, and dose distribution. Machine learning-based prediction models were developed using decision tree, support vector machine, k-nearest neighbour (kNN) algorithm, and neural network (NN) classifiers.

Results

A total of 116 and 26 patients enrolled at Centre 1 and Centre 2, respectively. The external validation model exhibited the highest accuracy with 65.4% for CT-based radiomics, 77.9% for PET-based radiomics, and 72.1% for dosiomics based on the NN classifiers. The hybrid model exhibited the highest accuracy of 84.4% for CT-based radiomics based on the kNN classifier, 86.0% for PET-based radiomics, and 79.0% for dosiomics based on the NN classifiers.

Conclusion

The proposed hybrid model exhibited promising predictive performance for the local response to definitive radiotherapy in ESCC patients.

Clinical relevance statement

The prediction of the complete response for oesophageal cancer patients may contribute to improving overall survival. The hybrid model has the potential to improve prediction performance than the external validation model that was conventionally proposed.

Key Points

• Radiomics and dosiomics used to predict response in patients with oesophageal cancer receiving definitive radiotherapy.

• Hybrid model with neural network classifier of PET-based radiomics improved prediction accuracy by 8.1%.

• The hybrid model has the potential to improve prediction performance.

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

Similar content being viewed by others

Abbreviations

AI:

Artificial intelligence

CM:

Confusion matrix

CRT:

Chemoradiotherapy

CT:

Computed tomography

CTV:

Clinical target volume

DIR:

Deformable image registration

ESCC:

Oesophageal squamous cell carcinoma

FDG-PET:

Fluorodeoxyglucose-Positron Emission Tomography

GTV:

Gross tumour volume

KNN:

K-nearest neighbour

LASSO:

Least absolute shrinkage and selection operator

LN:

Lymph node

NN:

Neural network

pCR:

Pathological complete response

PTV:

Planning target volume

ROC:

Receiver operator characteristic

ROI:

Regions of interest

RT:

Radiotherapy

SVM:

Support vector machine

VIF:

Variance inflation factor

References

  1. Zhang HZ, Jin GF, Shen HB (2012) Epidemiologic differences in esophageal cancer between Asian and Western populations. Chin J Cancer 31(6):281–286

    Article  PubMed  PubMed Central  Google Scholar 

  2. van Hagen P, Hulshof MC, van Lanschot JJ et al (2012) Preoperative chemoradiotherapy for esophageal or junctional cancer. N Engl J Med 366:2074–2084

    Article  PubMed  Google Scholar 

  3. van Heijl M, van Lanschot JJ, Koppert LB et al (2008) Neoadjuvant chemoradiation followed by surgery versus surgery alone for patients with adenocarcinoma or squamous cell carcinoma of the esophagus (CROSS). BMC Surg 8:21

    Article  PubMed  PubMed Central  Google Scholar 

  4. National Comprehensive Cancer Network (2017) NCCN Guidelines for Treatment of Cancer by Site: Esophageal Cancer [online]. Version 1.2017. http://www.nccn.org/professionals/default.aspx. Accessed 5 April 2022

  5. Nomura M, Kato K, Ando N et al (2017) Comparison between neoadjuvant chemotherapy followed by surgery and definitive chemoradiotherapy for overall survival in patients with clinical stage II/III esophageal squamous cell carcinoma (JCOG 1406-A). Jpn J Clin Oncol 47(6):480–6

    Article  PubMed  Google Scholar 

  6. Yan MH, Hou XB, Cai BN et al (2020) Neoadjuvant chemoradiotherapy plus surgery in the treatment of potentially resectable thoracic esophageal squamous cell carcinoma. World J Clin Cases 8(24):6315–6321

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rizzo S, Botta F, Raimondi S et al (2018) Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2(1):36

    Article  PubMed  PubMed Central  Google Scholar 

  8. Parekh V, Jacobs MA (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 1(2):207–226

    Article  PubMed  PubMed Central  Google Scholar 

  9. Xie CY, Pang CL, Chan B et al (2021) Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 13(10):2469. https://doi.org/10.3390/cancers13102469

    Article  PubMed  Google Scholar 

  10. Hu Y, Xie C, Yang H et al (2020) Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma. JAMA Netw Open 3:e2015927

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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 60:538–545

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Tixier F, Le Rest CC, Hatt M et al (2011) Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52(3):369–378

    Article  PubMed  Google Scholar 

  13. Desbordes P, Ruan S, Modzelewski R et al (2017) Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier. PLoS One 12:e0173208

  14. Murakami Y, Kawahara D, Tani S et al (2021) Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using 18F-FDG PET Images. Diagnostics (Basel) 11(6):1049

    Article  CAS  PubMed  Google Scholar 

  15. Hou Z, Li S, Ren W et al (2018) Radiomic analysis in T2W and SPAIR T2W MRI: Predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis 10:2256–2267

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cao Q, Li Y, Li Z et al (2020) Development and validation of a radiomics signature on differentially expressed features of 18F-FDG PET to predict treatment response of concurrent chemoradiotherapy in thoracic esophagus squamous cell carcinoma. Radiother Oncol 146:9–15

    Article  CAS  PubMed  Google Scholar 

  17. Fulmer JD, Roberts WC, von Gal ER et al (1979) Morphologic-physiologic correlates of the severity of fibrosis and degree of cellularity in idiopathic pulmonary fibrosis. J Clin Invest 63(4):665–676

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res 77:e104–e107

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16:385–395

    Article  CAS  PubMed  Google Scholar 

  20. Zhang JX, Song W, Chen ZH et al (2013) Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol 14:1295–1306

    Article  CAS  PubMed  Google Scholar 

  21. Breiman L (2001) Random Forests. Machine Learn 45:5–32

    Article  Google Scholar 

  22. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297

    Article  Google Scholar 

  23. Zhang Z (2016) Introduction to machine learning: k-nearest neighbors. Ann Transl Med 4:218

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 20:832–844

    Article  Google Scholar 

  25. Hou Z, Ren W, Li S et al (2017) Radiomic analysis in contrast-enhanced CT: Predict treatment response to chemoradiotherapy in esophageal carcinoma. Oncotarget 8:104444–104454

    Article  PubMed  PubMed Central  Google Scholar 

  26. van Rossum PS, 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(5):691–700

    Article  PubMed  Google Scholar 

  27. 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(5):1375–1382

    Article  PubMed  Google Scholar 

  28. Tan S, Zhang H, Zhang Y et al (2013) Predicting pathologic tumor response to chemoradiotherapy with histogram distances characterizing longitudinal changes in 18F-FDG uptake patterns. Med Phys 40(10):101707

    Article  PubMed  PubMed Central  Google Scholar 

  29. 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

    Article  CAS  PubMed  Google Scholar 

  30. Bleker J, Yakar D, van Noort B et al (2021) Single-center versus multi-center biparametric MRI radiomics approach for clinically significant peripheral zone prostate cancer. Insights Imaging 12(1):150

    Article  PubMed  PubMed Central  Google Scholar 

  31. Pati S, Baid U, Edwards B et al (2022) Nat Commun 13(1):7346

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  32. Braman NM, Etesami M, Prasanna P et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19(1):57

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sun C, Tian X, Liu Z et al (2019) Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study. EBioMedicine 46:160–169

    Article  PubMed  PubMed Central  Google Scholar 

  34. Beig N, Khorrami M, Alilou M et al (2019) Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology 290(3):783–792

    Article  PubMed  Google Scholar 

  35. Wu Q, Wang S, Chen X et al (2019) Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. Radiother Oncol 138:141–148

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Mathworks, Inc. for providing technical support.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, Graduate School of Biomedical Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan

    Daisuke Kawahara, Yuji Murakami & Yasushi Nagata

  2. School of Medicine, Hiroshima University, Hiroshima, 734-8551, Japan

    Shota Awane

  3. Department of Radiation Oncology, Hyogo Cancer Center, 70, Kitaoji-Cho 13, Akashi-Shi, Hyogo, Japan

    Yuki Emoto

  4. Division of Radiation Oncology, Kobe University Graduate School of Medicine, 7-5-2 Kusunokicho, Chuouku, Kobe, Hyogo, 650-0017, Japan

    Kazuma Iwashita, Hikaru Kubota & Ryohei Sasaki

  5. Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, 732-0057, Japan

    Yasushi Nagata

Authors
  1. Daisuke Kawahara
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuji Murakami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shota Awane
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuki Emoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kazuma Iwashita
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hikaru Kubota
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ryohei Sasaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yasushi Nagata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daisuke Kawahara.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Yasushi Nagata.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Study subjects or cohorts overlap

None.

Methodology

• retrospective

• cross sectional study

• multicentre study

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 226 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

Kawahara, D., Murakami, Y., Awane, S. et al. Radiomics and dosiomics for predicting complete response to definitive chemoradiotherapy patients with oesophageal squamous cell cancer using the hybrid institution model. Eur Radiol 34, 1200–1209 (2024). https://doi.org/10.1007/s00330-023-10020-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-023-10020-8

Keywords

Search

Navigation