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Radiomics in precision medicine for gastric cancer: opportunities and challenges

  • Imaging Informatics and Artificial Intelligence
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC.

Methods

We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality.

Results

Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies.

Conclusions

Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application.

Key Points

• Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival.

• Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes.

• Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

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Abbreviations

AUC:

Area under the curve

GC:

Gastric cancer

ROI:

Region of interest

RQS:

Radiomics quality scoring

References

  1. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F (2020) Gastric cancer. Lancet 396(10251):635–648

  2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6): 394-424

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

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bae H, Lee H, Kim S et al (2021) Radiomics analysis of contrast-enhanced CT for classification of hepatic focal lesions in colorectal cancer patients: its limitations compared to radiologists. Eur Radiol 31(11):8786–8796

    Article  PubMed  Google Scholar 

  5. Dercle L, Lu L, Schwartz LH et al (2020) Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway. J Natl Cancer Inst 112(9):902–912

    Article  PubMed  PubMed Central  Google Scholar 

  6. Rao SX, Lambregts DM, Schnerr RS et al (2014) Whole-liver CT texture analysis in colorectal cancer: does the presence of liver metastases affect the texture of the remaining liver? United European Gastroenterol J 2(6):530–538

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C et al (2019) Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int 13(5):546–559

    Article  PubMed  Google Scholar 

  8. Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N (2020) Overview of radiomics in breast cancer diagnosis and prognostication. Breast 49:74–80

  9. Wilson RDevaraj A (2017) Radiomics of pulmonary nodules and lung cancer. Transl Lung Cancer Res 6(1):86–91

    Article  Google Scholar 

  10. Fiz F, Vigano L, Gennaro N et al (2020) Radiomics of liver metastases: a systematic review. Cancers (Basel) 12(10)

  11. Ai Y, Zhu H, Xie C, Jin X (2020) Radiomics in cervical cancer: current applications and future potential. Crit Rev Oncol Hematol 152:102985

  12. Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. PLoS Med 18(3):e1003583

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sollini M, Antunovic L, Chiti A, Kirienko M (2019) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 46(13):2656–2672

  14. 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  PubMed  Google Scholar 

  15. Gao X, Ma T, Bai S et al (2020) A CT-based radiomics signature for evaluating tumor infiltrating Treg cells and outcome prediction of gastric cancer. Ann Transl Med 8(7):469

    Article  PubMed  PubMed Central  Google Scholar 

  16. Giganti F, Antunes S, Salerno A et al (2017) Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol 27(5):1831–1839

    Article  PubMed  Google Scholar 

  17. Giganti F, Marra P, Ambrosi A et al (2017) Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: comparison with tumour regression grade at final histology. Eur J Radiol 90:129–137

    Article  PubMed  Google Scholar 

  18. Hou Z, Yang Y, Li S et al (2018) Radiomic analysis using contrast-enhanced CT: predict treatment response to pulsed low dose rate radiotherapy in gastric carcinoma with abdominal cavity metastasis. Quant Imaging Med Surg 8(4):410–420

    Article  PubMed  PubMed Central  Google Scholar 

  19. Jiang Y, Chen C, Xie J et al (2018) Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer. EBioMedicine 36:171–182

    Article  PubMed  PubMed Central  Google Scholar 

  20. Jiang Y, Jin C, Yu H et al (2021) Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter, retrospective study. Ann Surg 274(6):e1153–e1161

    Article  PubMed  Google Scholar 

  21. Jiang Y, Yuan Q, Lv W et al (2018) Radiomic signature of (18)F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits. Theranostics 8(21):5915–5928

    Article  PubMed  PubMed Central  Google Scholar 

  22. Li J, Zhang C, Wei J et al (2020) Intratumoral and peritumoral radiomics of contrast-enhanced CT for prediction of disease-free survival and chemotherapy response in stage II/III gastric cancer. Front Oncol 10:552270

    Article  PubMed  PubMed Central  Google Scholar 

  23. Li W, Zhang L, Tian C et al (2019) Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection. Eur Radiol 29(6):3079–3089

    Article  PubMed  Google Scholar 

  24. Li Z, Zhang D, Dai Y et al (2018) Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: a pilot study. Chin J Cancer Res 30(4):406–414

    Article  PubMed  PubMed Central  Google Scholar 

  25. Sun KY, Hu HT, Chen SL et al (2020) CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer. BMC Cancer 20(1):468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Tan JW, Wang L, Chen Y et al (2020) Predicting chemotherapeutic response for far-advanced gastric cancer by radiomics with deep learning semi-automatic segmentation. J Cancer 11(24):7224–7236

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wang S, Feng C, Dong D et al (2020) Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study. Med Phys 47(10):4862–4871

    Article  PubMed  Google Scholar 

  28. Wang X, Sun J, Zhang W et al (2020) Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer. Eur J Surg Oncol 46(10 Pt A): 1932-1940

  29. Xu Q, Sun Z, Li X et al (2021) Advanced gastric cancer: CT radiomics prediction and early detection of downstaging with neoadjuvant chemotherapy. Eur Radiol 31(11):8765–8774

    Article  PubMed  PubMed Central  Google Scholar 

  30. Yoon SH, Kim YH, Lee YJ et al (2016) Tumor heterogeneity in human epidermal growth factor receptor 2 (HER2)-positive advanced gastric cancer assessed by CT texture analysis: association with survival after trastuzumab treatment. PLoS One 11(8):e0161278

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zhang W, Fang M, Dong D et al (2020) Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer. Radiother Oncol 145:13–20

    Article  CAS  PubMed  Google Scholar 

  32. Chen X, Yang Z, Yang J et al (2020) Radiomics analysis of contrast-enhanced CT predicts lymphovascular invasion and disease outcome in gastric cancer: a preliminary study. Cancer Imaging 20(1):24

    Article  PubMed  PubMed Central  Google Scholar 

  33. Chen Y, Wei K, Liu D et al (2021) A machine learning model for predicting a major response to neoadjuvant chemotherapy in advanced gastric cancer. Front Oncol 11:675458

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chen Y, Yuan F, Wang L et al (2021) Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy. Eur J Surg Oncol

  35. Jiang Y, Wang H, Wu J et al (2020) Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann Oncol 31(6):760–768

    Article  CAS  PubMed  Google Scholar 

  36. Li Q, Qi L, Feng QX et al (2019) Machine learning-based computational models derived from large-scale radiographic-radiomic images can help predict adverse histopathological status of gastric cancer. Clin Transl Gastroenterol 10(10):e00079

    Article  PubMed  PubMed Central  Google Scholar 

  37. Mazzei MA, Di Giacomo L, Bagnacci G et al (2021) Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer). Quant Imaging Med Surg 11(6):2376–2387

    Article  PubMed  PubMed Central  Google Scholar 

  38. Shin J, Lim JS, Huh YM et al (1879) (2021) A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting. Sci Rep 11(1)

  39. Zhang L, Dong D, Zhang W et al (2020) A deep learning risk prediction model for overall survival in patients with gastric cancer: a multicenter study. Radiother Oncol 150:73–80

    Article  PubMed  Google Scholar 

  40. Shi L, He Y, Yuan Z et al (2018) Radiomics for response and outcome assessment for non-small cell lung cancer. Technol Cancer Res Treat 17:1533033818782788

    Article  PubMed  PubMed Central  Google Scholar 

  41. Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 31(2):1049–1058

  42. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248

    Article  PubMed  PubMed Central  Google Scholar 

  43. He L, Huang Y, Ma Z, Liang C, Liang C, Liu Z (2016) Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule. Sci Rep 6: 34921

  44. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R (2010) Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 49(7):1012–1016

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

    Article  PubMed  PubMed Central  Google Scholar 

  46. Grootjans W, Tixier F, van der Vos CS et al (2016) The impact of optimal respiratory gating and image noise on evaluation of intratumor heterogeneity on 18F-FDG PET imaging of lung cancer. J Nucl Med 57(11):1692–1698

    Article  PubMed  Google Scholar 

  47. Huynh E, Coroller TP, Narayan V et al (2017) Associations of radiomic data extracted from static and respiratory-gated CT scans with disease recurrence in lung cancer patients treated with SBRT. PLoS One 12(1):e0169172

    Article  PubMed  PubMed Central  Google Scholar 

  48. Larue RT, Defraene G, De Ruysscher D, Lambin P, van Elmpt W (2017) Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol 90(1070):20160665

  49. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures. They Are Data. Radiology 278(2):563–577

  50. Hatt M, Vallieres M, Visvikis D, Zwanenburg A (2018) IBSI: an international community radiomics standardization initiative. Journal of Nuclear Medicine 59

  51. Esteva A, Robicquet A, Ramsundar B et al (2019) A guide to deep learning in healthcare. Nat Med 25(1):24–29

    Article  CAS  PubMed  Google Scholar 

  52. Ding JL, Zhao RB, Qiu QT et al (2022) Developing and validating a deep learning and radiomic model for glioma grading using multiplanar reconstructed magnetic resonance contrast-enhanced T1-weighted imaging: a robust, multi-institutional study. Quant Imag Med Surg 12(2):1517-+

    Article  Google Scholar 

  53. Li C, Dong D, Li L et al (2020) Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics. Ieee J Biomed Health 24(12):3585–3594

    Article  Google Scholar 

  54. Li C, Qin Y, Zhang WH et al (2022) Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer. Medical Physics

  55. Tomaszewski MR, Gillies RJ (2021) The Biological Meaning of Radiomic Features. Radiology 299(2):E256

  56. Staal FCR, van der Reijd DJ, Taghavi M, Lambregts DMJ, Beets-Tan RGH, Maas M (2021) Radiomics for the prediction of treatment outcome and survival in patients with colorectal cancer: a systematic review. Clin Colorectal Cancer 20(1):52–71

  57. Hatt M, Vallieres M, Visvikis DZwanenburg A (2018) IBSI: an international community radiomics standardization initiative. Journal of Nuclear Medicine 59

  58. Lohmann P, Galldiks N, Kocher M et al (2021) Radiomics in neuro-oncology: basics, workflow, and applications. Methods 188:112–121

    Article  CAS  PubMed  Google Scholar 

  59. Schwier M, van Griethuysen J, Vangel MG et al (2019) Repeatability of multiparametric prostate MRI radiomics features. Sci Rep 9(1):9441

    Article  PubMed  PubMed Central  Google Scholar 

  60. Bianchini L, Santinha J, Loucao N et al (2021) A multicenter study on radiomic features from T2 -weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics. Magn Reson Med 85(3):1713–1726

    Article  PubMed  Google Scholar 

  61. Starmans MPA, Timbergen MJM, Vos M et al (2022) Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach. J Digit Imaging

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Funding

This work received funding from the National Natural Science Foundation of China (81871323 and 81801665); National Natural Science Foundation of Guangdong Province (2018B030311024); Scientific Research Cultivation and Innovation Foundation of Jinan University (21620447); Outstanding Innovative Talents Cultivation Funded Programs for Doctoral Students of Jinan University (2021CXB012). The funders had no role in study design, data collection and analysis, preparation of the manuscript, or decision to publish.

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Authors and Affiliations

  1. Department of Radiology, the First Affiliated Hospital, Jinan University, No.613, Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China

    Qiuying Chen, Lu Zhang, Shuyi Liu, Jingjing You, Luyan Chen, Zhe Jin, Shuixing Zhang & Bin Zhang

  2. Graduate College, Jinan University, Guangzhou, Guangdong, China

    Qiuying Chen, Lu Zhang, Shuyi Liu, Jingjing You, Luyan Chen, Zhe Jin, Shuixing Zhang & Bin Zhang

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  1. Qiuying Chen
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The scientific guarantor of this publication is Bin Zhang.

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Chen, Q., Zhang, L., Liu, S. et al. Radiomics in precision medicine for gastric cancer: opportunities and challenges . Eur Radiol 32, 5852–5868 (2022). https://doi.org/10.1007/s00330-022-08704-8

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