Skip to main content

Advertisement

SpringerLink
Log in

CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners

  • ABDOMINAL RADIOLOGY
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

To test the technical reproducibility of acquisition and scanners of CT image-based radiomics model for early recurrent hepatocellular carcinoma (HCC).

Methods

We included primary HCC patient undergone curative therapies, using early recurrence as endpoint. Four datasets were constructed: 109 images from hospital #1 for training (set 1: 1-mm image slice thickness), 47 images from hospital #1 for internal validation (sets 2 and 3: 1-mm and 10-mm image slice thicknesses, respectively), and 47 images from hospital #2 for external validation (set 4: vastly different from training dataset). A radiomics model was constructed. Radiomics technical reproducibility was measured by overfitting and calibration deviation in external validation dataset. The influence of slice thickness on reproducibility was evaluated in two internal validation datasets.

Results

Compared with set 1, the model in set 2 indicated favorable prediction efficiency (the area under the curve 0.79 vs. 0.80, P = 0.47) and good calibration (unreliability statistic U: P = 0.33). However, in set 4, significant overfitting (0.63 vs. 0.80, P < 0.01) and calibration deviation (U: P < 0.01) were observed. Similar poor performance was also observed in set 3 (0.56 vs. 0.80, P = 0.02; U: P < 0.01).

Conclusions

CT-based radiomics has poor reproducibility between centers. Image heterogeneity, such as slice thickness, can be a significant influencing factor.

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

Similar content being viewed by others

Data availability

Previously reported picture and model construction data were used to support this study and are available at https://doi.org/10.1186/s40644-019-0197-5. The prior study is cited at relevant places within the text as Ref. [26].

References

  1. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer (Oxford, England: 1990) 48(4):441–446

    Article  Google Scholar 

  2. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    Article  Google Scholar 

  3. Neri E, Del Re M, Paiar F et al (2018) Radiomics and liquid biopsy in oncology: the holons of systems medicine. Insights Imaging 9(6):915–924

    Article  Google Scholar 

  4. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762

    Article  Google Scholar 

  5. Cozzi L, Dinapoli N, Fogliata A et al (2017) Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer 17(1):829

    Article  Google Scholar 

  6. Zhou Y, He L, Huang Y et al (2017) CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom Radiol (NY) 42:1695–1704

    Article  Google Scholar 

  7. Coroller TP, Grossmann P, Hou Y et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol J Eur Soc Ther Radiol Oncol 114(3):345–350

    Article  Google Scholar 

  8. Parmar C, Leijenaar RT, Grossmann P et al (2015) Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer. Sci Rep 5:11044

    Article  Google Scholar 

  9. Fried DV, Tucker SL, Zhou S et al (2014) Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys 90(4):834–842

    Article  Google Scholar 

  10. 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 BCR 19(1):57

    Article  Google Scholar 

  11. Leijenaar RT, Carvalho S, Hoebers FJ et al (2015) External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol (Stockh, Swed) 54(9):1423–1429

    Article  CAS  Google Scholar 

  12. Chen LD, Liang JY, Wu H et al (2018) Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics. Life Sci 208:55–63

    Article  CAS  Google Scholar 

  13. Chalkidou A, O’Doherty MJ, Marsden PK (2015) False discovery rates in PET and CT studies with texture features: a systematic review. PLoS ONE 10(5):e0124165

    Article  Google Scholar 

  14. Mackin D, Fave X, Zhang L et al (2015) Measuring computed tomography scanner variability of radiomics features. Investig Radiol 50(11):757–765

    Article  Google Scholar 

  15. Kim H, Park CM, Lee M et al (2016) Impact of reconstruction algorithms on CT radiomic features of pulmonary tumors: analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability. PLoS ONE 11(10):e0164924

    Article  Google Scholar 

  16. Shafiq-Ul-Hassan M, Zhang GG, Latifi K et al (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 44(3):1050–1062

    Article  CAS  Google Scholar 

  17. Lu L, Ehmke RC, Schwartz LH, Zhao B (2016) Assessing agreement between radiomic features computed for multiple CT imaging settings. PLoS ONE 11(12):e0166550

    Article  Google Scholar 

  18. Balagurunathan Y, Gu Y, Wang H et al (2014) Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol 7(1):72–87

    Article  Google Scholar 

  19. Balagurunathan Y, Kumar V, Gu Y et al (2014) Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 27(6):805–823

    Article  Google Scholar 

  20. Berenguer R, Pastor-Juan MDR, Canales-Vazquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288(2):407–415

    Article  Google Scholar 

  21. van Timmeren JE, Leijenaar RTH, van Elmpt W et al (2016) Test–retest data for radiomics feature stability analysis: generalizable or study-specific? Tomography (Ann Arbor, Mich) 2(4):361–365

    Google Scholar 

  22. Shafiq-Ul-Hassan M, Zhang GG, Hunt DC et al (2018) Accounting for reconstruction kernel-induced variability in CT radiomic features using noise power spectra. J Med Imaging (Bellingham, Wash) 5(1):011013

    Google Scholar 

  23. Ahn SJ, Kim JH, Lee SM, Park SJ, Han JK (2019) CT reconstruction algorithms affect histogram and texture analysis: evidence for liver parenchyma, focal solid liver lesions, and renal cysts. Eur Radiol 29(8):4008–4015

    Article  Google Scholar 

  24. Solomon J, Mileto A, Nelson RC, Roy Choudhury K, Samei E (2016) Quantitative features of liver lesions, lung nodules, and renal stones at multi-detector row CT examinations: dependency on radiation dose and reconstruction algorithm. Radiology 279(1):185–194

    Article  Google Scholar 

  25. Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P (2017) Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur Radiol 27(10):4188–4197

    Article  Google Scholar 

  26. Shan QY, Hu HT, Feng ST et al (2019) CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging 19(1):11

    Article  Google Scholar 

  27. Kudo M (2011) Adjuvant therapy after curative treatment for hepatocellular carcinoma. Oncology 81(Suppl 1):50–55

    Article  CAS  Google Scholar 

  28. Lin S, Hoffmann K, Schemmer P (2012) Treatment of hepatocellular carcinoma: a systematic review. Liver Cancer 1(3–4):144–158

    Article  CAS  Google Scholar 

  29. Vasquez MM, Hu C, Roe DJ, Chen Z, Halonen M, Guerra S (2016) Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application. BMC Med Res Methodol 16(1):154

    Article  Google Scholar 

  30. Gui J, Li H (2005) Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics (Oxford, England) 21(13):3001–3008

    Article  CAS  Google Scholar 

  31. Soubrane O, Schwarz L, Cauchy F et al (2015) A conceptual technique for laparoscopic right hepatectomy based on facts and oncologic principles: the caudal approach. Ann Surg 261(6):1226–1231

    Article  Google Scholar 

  32. Babyak MA (2004) What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 66(3):411–421

    PubMed  Google Scholar 

  33. Fave X, Cook M, Frederick A et al (2015) Preliminary investigation into sources of uncertainty in quantitative imaging features. Comput Med Imaging Graph 44:54–61

    Article  Google Scholar 

  34. Kaasalainen T, Palmu K, Reijonen V, Kortesniemi M (2014) Effect of patient centering on patient dose and image noise in chest CT. AJR Am J Roentgenol 203(1):123–130

    Article  Google Scholar 

  35. Toth T, Ge Z, Daly MP (2007) The influence of patient centering on CT dose and image noise. Med Phys 34(7):3093–3101

    Article  Google Scholar 

  36. Habibzadeh MA, Ay MR, Asl AR, Ghadiri H, Zaidi H (2012) Impact of miscentering on patient dose and image noise in x-ray CT imaging: phantom and clinical studies. Phys Med PM Int J Devoted Appl Phys Med Biol 28(3):191–199

    CAS  Google Scholar 

  37. Li J, Udayasankar UK, Toth TL, Seamans J, Small WC, Kalra MK (2007) Automatic patient centering for MDCT: effect on radiation dose. AJR Am J Roentgenol 188(2):547–552

    Article  Google Scholar 

  38. Matsubara K, Koshida K, Ichikawa K et al (2009) Misoperation of CT automatic tube current modulation systems with inappropriate patient centering: phantom studies. AJR Am J Roentgenol 192(4):862–865

    Article  Google Scholar 

  39. Kaasalainen T, Palmu K, Lampinen A, Kortesniemi M (2013) Effect of vertical positioning on organ dose, image noise and contrast in pediatric chest CT—phantom study. Pediatr Radiol 43(6):673–684

    Article  Google Scholar 

  40. Zhao B, Tan Y, Tsai WY et al (2016) Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 6:23428

    Article  CAS  Google Scholar 

Download references

Funding

This study was supported by National Natural Science Foundation of China (No. 81701701), Natural Science Foundation of Guangdong province (Nos. 2017A030313661 and 2016A030310143), and Science and Technology Planning Project of Guangdong Province of China (No. 20160904).

Author information

Author notes

  1. Hang-tong Hu and Quan-yuan Shan contributed equally to this manuscript and should be considered co-first authors.

Authors and Affiliations

  1. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Hang-tong Hu, Quan-yuan Shan, Shu-ling Chen, Xiao-yan Xie, Ming-de Lu, Ming Kuang & Wei Wang

  2. Clinical trials Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Bin Li & Jian-yan Long

  3. Department of Radiology, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Shi-ting Feng

  4. Department of Medical Ultrasonics, The Third Affiliated Hospital of Sun Yat-Sen University, Guangdong Key Laboratory of Liver Disease Research, Guangzhou, 510630, Guangdong Province, China

    Er-jiao Xu

  5. GE Healthcare, Shanghai, 200030, China

    Xin Li

  6. Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China

    Ming-de Lu & Ming Kuang

  7. Department of Radiology, State Key Laboratory of Oncology in South China, The Cancer Center, Sun Yat-sen University, 651 Dongfeng East Road, Guangzhou, 510060, Guangdong, China

    Jing-xian Shen

Authors
  1. Hang-tong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Quan-yuan Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shu-ling Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shi-ting Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Er-jiao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jian-yan Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiao-yan Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ming-de Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ming Kuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jing-xian Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jing-xian Shen or Wei Wang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

Research involves human participants. All data was from routine clinical test, and there was no clinical intervention for the participants in the study.

Informed consent

All informed consent for the routine clinical tests were obtained from participants. While for the retrospective nature of this study, informed consents for participating in this study was waived by the Institutional Review Board.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 76 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Ht., Shan, Qy., Chen, Sl. et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol med 125, 697–705 (2020). https://doi.org/10.1007/s11547-020-01174-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11547-020-01174-2

Keywords

Search

Navigation