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European Radiology

, Volume 29, Issue 12, pp 6741–6749 | Cite as

Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography

  • Wei-Chih Shen
  • Shang-Wen Chen
  • Kuo-Chen Wu
  • Te-Chun Hsieh
  • Ji-An Liang
  • Yao-Ching Hung
  • Lian-Shung Yeh
  • Wei-Chun Chang
  • Wu-Chou Lin
  • Kuo-Yang Yen
  • Chia-Hung KaoEmail author
Molecular Imaging

Abstract

Background

We designed a deep learning model for assessing 18F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer.

Methods

All 142 patients with cervical cancer underwent 18F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. In each round of k-fold cross-validation, a well-trained proposed model and a slice-based optimal threshold were derived from a training set and used to classify each slice set in the test set into the categories of with or without local or distant failure. The classification results of each tumor were aggregated to summarize a tumor-based prediction result.

Results

In total, 21 and 26 patients experienced local and distant failures, respectively. Regarding local recurrence, the tumor-based prediction result summarized from all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.

Conclusion

This is the first study to use deep learning model for assessing 18F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients.

Key Points

• This is the first study to use deep learning model for assessing 18 F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients.

• All 142 patients with cervical cancer underwent 18 F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets.

• For local recurrence, all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.

Keywords

AI (artificial intelligence) Neural network models Fluorodeoxyglucose F18 PET-CT scan Cervical cancer 

Abbreviations

18F-FDG PET/CT

18F-fluorodeoxyglucose positron emission tomography-computed tomography

CRT

Chemoradiotherapy

CTV

Clinical target volume

HGRE

High gray-level run emphasis

MTV

Metabolic tumor volume

PLNs

Pelvic lymph nodes

SUV

Standardized uptake value

VOI

Volume of interest

Notes

Funding

This work was supported by grants from the Ministry of Health and Welfare, Taiwan (MOHW107-TDU-B-212-123004); China Medical University Hospital (DMR-107-192, CRS-106-036, CRS106-039, CRS106-040, CRS106-041); Asia University (DMR-106-150); Academia Sinica Stroke Biosignature Project (BM10701010021); MOST Clinical Trial Consortium for Stroke (MOST 107-2321-B-039-004-); Tseng-Lien Lin Foundation, Taichung, Taiwan; and Katsuzo and Kiyo Aoshima Memorial Funds, Japan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding was received for this study.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Chia-Hung Kao, MD, Graduate Institute of Biomedical Sciences and School of Medicine, College of Medicine, China Medical University, No. 2, Yuh-Der Road, Taichung 404, Taiwan. E-mail: d10040@mail.cmuh.org.tw; dr.kaochiahung@gmail.com

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 (Prof. Shang-Wen Chen) has significant statistical expertise.

Informed consent

This is a retrospective study for images’ analyses. The IRB also specifically waived the consent requirement.

Ethical approval

This study was approved by the local institutional review board (certificate numbers CMUH102-REC2-74 and DMR99-IRB-010-1).

Study subjects or cohorts overlap

This work was partially presented at NVIDIA GTC Taiwan 2018 Poster Contest.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

Supplementary material

330_2019_6265_MOESM1_ESM.doc (208 kb)
ESM 1 (DOC 208 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Wei-Chih Shen
    • 1
  • Shang-Wen Chen
    • 2
    • 3
    • 4
  • Kuo-Chen Wu
    • 1
  • Te-Chun Hsieh
    • 5
    • 6
  • Ji-An Liang
    • 2
    • 7
  • Yao-Ching Hung
    • 3
    • 8
  • Lian-Shung Yeh
    • 3
    • 8
  • Wei-Chun Chang
    • 3
    • 8
  • Wu-Chou Lin
    • 3
    • 8
  • Kuo-Yang Yen
    • 5
    • 6
  • Chia-Hung Kao
    • 5
    • 7
    • 9
    Email author
  1. 1.Department of Computer Science and Information EngineeringAsia UniversityTaichungTaiwan
  2. 2.Department of Radiation OncologyChina Medical University HospitalTaichungTaiwan
  3. 3.School of Medicine, College of MedicineChina Medical UniversityTaichungTaiwan
  4. 4.Department of Radiology, School of Medicine, College of MedicineTaipei Medical UniversityTaipeiTaiwan
  5. 5.Department of Nuclear Medicine and PET CenterChina Medical University HospitalTaichungTaiwan
  6. 6.Department of Biomedical Imaging and Radiological ScienceChina Medical UniversityTaichungTaiwan
  7. 7.Graduate Institute of Biomedical Sciences, School of Medicine, College of MedicineChina Medical UniversityTaichungTaiwan
  8. 8.Department of Obstetrics and GynecologyChina Medical University HospitalTaichungTaiwan
  9. 9.Department of Bioinformatics and Medical EngineeringAsia UniversityTaichungTaiwan

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