Display options
Cite
Rundo F, Banna GL, Prezzavento L, et al. 3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma. J Imaging. 2020;6(12)doi: 10.3390/jimaging6120133.
Rundo, F., Banna, G. L., Prezzavento, L., Trenta, F., Conoci, S., & Battiato, S. (2020). 3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma. Journal of imaging, 6(12), . https://doi.org/10.3390/jimaging6120133
Rundo, Francesco, et al. "3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma." Journal of imaging vol. 6,12 (2020). doi: https://doi.org/10.3390/jimaging6120133
Rundo F, Banna GL, Prezzavento L, Trenta F, Conoci S, Battiato S. 3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma. J Imaging. 2020 Dec 03;6(12). doi: 10.3390/jimaging6120133. PMID: 34460530; PMCID: PMC8321180.
Copy Download .nbib Format: NLM
Share it on

J Imaging. 2020 Dec 03;6(12). doi: 10.3390/jimaging6120133.

3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma.

Journal of imaging

Francesco Rundo, Giuseppe Luigi Banna, Luca Prezzavento, Francesca Trenta, Sabrina Conoci, Sebastiano Battiato

Affiliations

  1. STMicroelectronics-ADG Central R&D Division, 95125 Catania, Italy.
  2. Medical Oncology Department, United Lincolnshire NHS Hospital Trust, Lincoln LN2, Lincolnshire, UK.
  3. DIEEI, University of Catania, 95125 Catania, Italy.
  4. IPLAB, University of Catania, 95125 Catania, Italy.
  5. Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, 98100 Messina, Italy.

PMID: 34460530 PMCID: PMC8321180 DOI: 10.3390/jimaging6120133

Abstract

Immunotherapy is regarded as one of the most significant breakthroughs in cancer treatment. Unfortunately, only a small percentage of patients respond properly to the treatment. Moreover, to date, there are no efficient bio-markers able to early discriminate the patients eligible for this treatment. In order to help overcome these limitations, an innovative non-invasive deep pipeline, integrating Computed Tomography (CT) imaging, is investigated for the prediction of a response to immunotherapy treatment. We report preliminary results collected as part of a case study in which we validated the implemented method on a clinical dataset of patients affected by Metastatic Urothelial Carcinoma. The proposed pipeline aims to discriminate patients with high chances of response from those with disease progression. Specifically, the authors propose ad-hoc 3D Deep Networks integrating Self-Attention mechanisms in order to estimate the immunotherapy treatment response from CT-scan images and such hemato-chemical data of the patients. The performance evaluation (average accuracy close to 92%) confirms the effectiveness of the proposed approach as an immunotherapy treatment response biomarker.

Keywords: 3D-CNN; immunotherapy; radiomics; self-attention

References

  1. Eur Urol. 2019 Dec;76(6):714-718 - PubMed
  2. Comput Biol Med. 2015 Aug;63:124-32 - PubMed
  3. Eur Radiol Exp. 2020 Jan 3;4(1):2 - PubMed
  4. Cancer Manag Res. 2019 May 08;11:4171-4184 - PubMed
  5. Sci Rep. 2017 Aug 18;7(1):8738 - PubMed
  6. IEEE Trans Pattern Anal Mach Intell. 2019 May 23;: - PubMed
  7. Front Pharmacol. 2017 Aug 23;8:561 - PubMed
  8. Front Med (Lausanne). 2019 Jul 31;6:172 - PubMed
  9. Eur J Cancer. 2012 Mar;48(4):441-6 - PubMed
  10. Cancer Biomark. 2011-2012;10(2):79-89 - PubMed
  11. J Clin Oncol. 2017 Jul 1;35(19):2117-2124 - PubMed
  12. Urol Oncol. 2017 Jan;35(1):14-20 - PubMed
  13. Cancer Treat Rev. 2015 Apr;41(4):341-53 - PubMed
  14. Nat Biomed Eng. 2019 Mar;3(3):173-182 - PubMed
  15. Eur J Cancer. 2009 Jan;45(2):228-47 - PubMed
  16. J Urol. 2001 Jan;165(1):47-50; discussion 50 - PubMed
  17. Int J Cancer. 2015 Mar 1;136(5):E359-86 - PubMed
  18. J Clin Oncol. 2016 Sep 10;34(26):3119-25 - PubMed
  19. Lancet. 2018 Feb 24;391(10122):748-757 - PubMed
  20. Tomography. 2019 Mar;5(1):201-208 - PubMed
  21. Lancet Oncol. 2017 Nov;18(11):1483-1492 - PubMed
  22. J Clin Oncol. 2012 Jan 10;30(2):191-9 - PubMed
  23. Med Phys. 2017 Nov;44(11):5814-5823 - PubMed
  24. J Immunother Cancer. 2017 Nov 21;5(1):94 - PubMed
  25. Am Soc Clin Oncol Educ Book. 2016;35:e493-503 - PubMed
  26. Eur Urol. 2019 Jul;76(1):73-81 - PubMed
  27. PLoS One. 2019 Feb 20;14(2):e0210976 - PubMed
  28. Med Phys. 2016 Apr;43(4):1882 - PubMed
  29. Lancet. 2017 Jan 7;389(10064):67-76 - PubMed
  30. N Engl J Med. 2017 Mar 16;376(11):1015-1026 - PubMed
  31. JAMA Oncol. 2019 Jul 1;5(7):934-935 - PubMed

Publication Types