Skip to main content
SpringerLink
Log in

Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation

  • Radiotherapy
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Purpose

To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.

Materials and methods

Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients, and a semiautomatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR.

Results

The two DL models achieved a median [interquartile range] dice similarity coefficient (DSC) of 0.84 [0.71;0.93] and 0.85 [0.70;0.93] across the OARs. The absolute median Dmean difference between manual and the two DL models was 2.0 [0.7;6.6]% and 2.4 [0.9;7.1]%. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmean differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 h the time required to complete the entire segmentation process.

Conclusion

DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models.

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

Similar content being viewed by others

References:

  1. Paix A, Antoni D, Waissi W, Ledoux M-P, Bilger K, Fornecker L et al (2018) Total body irradiation in allogeneic bone marrow transplantation conditioning regimens: a review. Crit Rev Oncol Hematol 123:138–148. https://doi.org/10.1016/j.critrevonc.2018.01.011

    Article  PubMed  Google Scholar 

  2. Wong JYC, Filippi AR, Scorsetti M, Hui S, Muren LP, Mancosu P (2020) Total marrow and total lymphoid irradiation in bone marrow transplantation for acute leukaemia. Lancet Oncol 21:e477–e487. https://doi.org/10.1016/S1470-2045(20)30342-9

    Article  PubMed  Google Scholar 

  3. Wilkie JR, Tiryaki H, Smith BD, Roeske JC, Radosevich JA, Aydogan B (2008) Feasibility study for linac-based intensity modulated total marrow irradiation: linac-based intensity modulated total marrow irradiation. Med Phys 35:5609–5618. https://doi.org/10.1118/1.2990779

    Article  PubMed  Google Scholar 

  4. Yeginer M, Roeske JC, Radosevich JA, Aydogan B (2011) Linear accelerator-based intensity-modulated total marrow irradiation technique for treatment of hematologic malignancies: a dosimetric feasibility study. Int J Radiat Oncol Biol Phys 79:1256–1265. https://doi.org/10.1016/j.ijrobp.2010.06.029

    Article  PubMed  Google Scholar 

  5. Fogliata A, Cozzi L, Clivio A, Ibatici A, Mancosu P, Navarria P et al (2011) Preclinical assessment of volumetric modulated arc therapy for total marrow irradiation. Int J Radiat Oncol Biol Phys 80:628–636. https://doi.org/10.1016/j.ijrobp.2010.11.028

    Article  PubMed  Google Scholar 

  6. Han C, Schultheisss TE, Wong JYC (2012) Dosimetric study of volumetric modulated arc therapy fields for total marrow irradiation. Radiother Oncol 102:315–320. https://doi.org/10.1016/j.radonc.2011.06.005

    Article  PubMed  Google Scholar 

  7. Aydogan B, Yeginer M, Kavak GO, Fan J, Radosevich JA, Gwe-Ya K (2011) Total marrow irradiation with rapidarc volumetric arc therapy. Int J Radiat Oncol Biol Phys 81:592–599. https://doi.org/10.1016/j.ijrobp.2010.11.035

    Article  PubMed  Google Scholar 

  8. Dei D, Lambri N, Stefanini S, Vernier V, Brioso RC, Crespi L et al (2023) Internal guidelines for reducing lymph node contour variability in total marrow and lymph node irradiation. Cancers 15:1536. https://doi.org/10.3390/cancers15051536

    Article  PubMed  PubMed Central  Google Scholar 

  9. Cha E, Elguindi S, Onochie I, Gorovets D, Deasy JO, Zelefsky M et al (2021) Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy. Radiother Oncol 159:1–7. https://doi.org/10.1016/j.radonc.2021.02.040

    Article  PubMed  PubMed Central  Google Scholar 

  10. van der Veen J, Willems S, Bollen H, Maes F, Nuyts S (2020) Deep learning for elective neck delineation: More consistent and time efficient. Radiother Oncol 153:180–188. https://doi.org/10.1016/j.radonc.2020.10.007

    Article  PubMed  Google Scholar 

  11. Ma C, Zhou J, Xu X, Guo J, Han M, Gao Y et al (2022) Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer. J Appl Clin Med Phys. https://doi.org/10.1002/acm2.13470

    Article  PubMed  PubMed Central  Google Scholar 

  12. Byun HK, Chang JS, Choi MS, Chun J, Jung J, Jeong C et al (2021) Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy. Radiat Oncol 16:203. https://doi.org/10.1186/s13014-021-01923-1

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kim H, Jung J, Kim J, Cho B, Kwak J, Jang JY et al (2020) Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network. Sci Rep 10:6204. https://doi.org/10.1038/s41598-020-63285-0

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Amjad A, Xu J, Thill D, Lawton C, Hall W, Awan MJ et al (2022) General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis. Med Phys 49:1686–1700. https://doi.org/10.1002/mp.15507

    Article  PubMed  Google Scholar 

  15. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Zabel WJ, Conway JL, Gladwish A, Skliarenko J, Didiodato G, Goorts-Matthews L et al (2021) Clinical evaluation of deep learning and atlas-based auto-contouring of bladder and rectum for prostate radiation therapy. Pract Radiat Oncol 11:e80–e89. https://doi.org/10.1016/j.prro.2020.05.013

    Article  PubMed  Google Scholar 

  17. Wong WKH, Leung LHT, Kwong DLW (2016) Evaluation and optimization of the parameters used in multiple-atlas-based segmentation of prostate cancers in radiation therapy. BJR 89:20140732. https://doi.org/10.1259/bjr.20140732

    Article  PubMed  Google Scholar 

  18. Sharp G, Fritscher KD, Pekar V, Peroni M, Shusharina N, Veeraraghavan H et al (2014) Vision 20/20: perspectives on automated image segmentation for radiotherapy: perspectives on automated image segmentation for radiotherapy. Med Phys 41:050902. https://doi.org/10.1118/1.4871620

    Article  PubMed  PubMed Central  Google Scholar 

  19. D’Aviero A, Re A, Catucci F, Piccari D, Votta C, Piro D et al (2022) Clinical validation of a deep-learning segmentation software in head and neck: an early analysis in a developing radiation oncology center. IJERPH 19:9057. https://doi.org/10.3390/ijerph19159057

    Article  PubMed  PubMed Central  Google Scholar 

  20. Radici L, Ferrario S, Borca VC, Cante D, Paolini M, Piva C et al (2022) Implementation of a commercial deep learning-based auto segmentation software in radiotherapy: evaluation of effectiveness and impact on workflow. Life 12:2088. https://doi.org/10.3390/life12122088

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  21. Chen W, Wang C, Zhan W, Jia Y, Ruan F, Qiu L et al (2021) A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer. Sci Rep 11:23002. https://doi.org/10.1038/s41598-021-02330-y

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. Duan J, Bernard M, Downes L, Willows B, Feng X, Mourad WF et al (2022) Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process. Med Phys 49:2570–2581. https://doi.org/10.1002/mp.15525

    Article  PubMed  Google Scholar 

  23. Chen X, Sun S, Bai N, Han K, Liu Q, Yao S et al (2021) A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy. Radiother Oncol 160:175–184. https://doi.org/10.1016/j.radonc.2021.04.019

    Article  PubMed  Google Scholar 

  24. Shi J, Wang Z, Kan H, Zhao M, Xue X, Yan B (2022) Automatic segmentation of target structures for total marrow and lymphoid irradiation in bone marrow transplantation. In: 44th annual international conference of the IEEE engineering in medicine & biology society (EMBC), Glasgow, Scotland, United Kingdom: IEEE; 2022, p. 5025–9. https://doi.org/10.1109/EMBC48229.2022.9871824

  25. Mancosu P, Navarria P, Muren LP, Castagna L, Reggiori G, Clerici E et al (2021) Development of an immobilization device for total marrow irradiation. Pract Radiat Oncol 11:e98-105. https://doi.org/10.1016/j.prro.2020.02.012

    Article  PubMed  Google Scholar 

  26. Mancosu P, Navarria P, Castagna L, Roggio A, Pellegrini C, Reggiori G et al (2012) Anatomy driven optimization strategy for total marrow irradiation with a volumetric modulated arc therapy technique. J Appl Clin Med Phys 13:138–147. https://doi.org/10.1120/jacmp.v13i1.3653

    Article  PubMed Central  Google Scholar 

  27. Mancosu P, Navarria P, Castagna L, Reggiori G, Sarina B, Tomatis S et al (2013) Interplay effects between dose distribution quality and positioning accuracy in total marrow irradiation with volumetric modulated arc therapy: TMI by VMAT: positioning uncertainties. Med Phys 40:111713. https://doi.org/10.1118/1.4823767

    Article  PubMed  Google Scholar 

  28. Mancosu P, Navarria P, Castagna L, Reggiori G, Stravato A, Gaudino A et al (2015) Plan robustness in field junction region from arcs with different patient orientation in total marrow irradiation with VMAT. Physica Med 31:677–682. https://doi.org/10.1016/j.ejmp.2015.05.012

    Article  Google Scholar 

  29. Lambri N, Dei D, Hernandez V, Castiglioni I, Clerici E, Crespi L et al (2022) Automatic planning of the lower extremities for total marrow irradiation using volumetric modulated arc therapy. Strahlenther Onkol. https://doi.org/10.1007/s00066-022-02014-0

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sarina B, Mancosu P, Navarria P, Bramanti S, Mariotti J, De Philippis C et al (2021) Nonmyeloablative conditioning regimen including low-dose total marrow/lymphoid irradiation before haploidentical transplantation with post-transplantation cyclophosphamide in patients with advanced lymphoproliferative diseases. Transplant Cell Ther 27:492.e1-492.e6. https://doi.org/10.1016/j.jtct.2021.03.013

    Article  CAS  PubMed  Google Scholar 

  31. https://github.com/qurit/rt-utils n.d.

  32. Jingnan Jia. Jingnan-Jia/segmentation_metrics: v1.1.3. Zenodo; 2022. https://doi.org/10.5281/ZENODO.7264630.

  33. Panchal A, Pyup.Io Bot, Couture G, Gertsikkema, Galler N, Hideki_Nakamoto, (2019) dicompyler/dicompyler-core v0.5.5. Zenodo; 2019. https://doi.org/10.5281/ZENODO.3236628

  34. Robert C, Munoz A, Moreau D, Mazurier J, Sidorski G, Gasnier A et al (2021) Clinical implementation of deep-learning based auto-contouring tools–Experience of three French radiotherapy centers. Cancer/Radiothérapie 25:607–616. https://doi.org/10.1016/j.canrad.2021.06.023

    Article  CAS  PubMed  Google Scholar 

  35. Chen M, Wu S, Zhao W, Zhou Y, Zhou Y, Wang G (2022) Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy. Cancer/Radiothérapie 26:494–501. https://doi.org/10.1016/j.canrad.2021.08.020

    Article  CAS  PubMed  Google Scholar 

  36. Francolini G, Desideri I, Stocchi G, Salvestrini V, Ciccone LP, Garlatti P et al (2020) Artificial Intelligence in radiotherapy: state of the art and future directions. Med Oncol 37:50. https://doi.org/10.1007/s12032-020-01374-w

    Article  PubMed  Google Scholar 

  37. Vinod SK, Min M, Jameson MG, Holloway LC (2016) A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology. J Med Imaging Radiat Oncol 60:393–406. https://doi.org/10.1111/1754-9485.12462

    Article  PubMed  Google Scholar 

  38. Casati M, Piffer S, Calusi S, Marrazzo L, Simontacchi G, Di Cataldo V et al (2022) Clinical validation of an automatic atlas-based segmentation tool for male pelvis CT images. J Applied Clin Med Phys. https://doi.org/10.1002/acm2.13507

    Article  Google Scholar 

  39. Wong J, Fong A, McVicar N, Smith S, Giambattista J, Wells D et al (2020) Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol 144:152–158. https://doi.org/10.1016/j.radonc.2019.10.019

    Article  PubMed  Google Scholar 

  40. Watkins WT, Qing K, Han C, Hui S, Liu A (2022) Auto-segmentation for total marrow irradiation. Front Oncol 12:970425. https://doi.org/10.3389/fonc.2022.970425

    Article  PubMed  PubMed Central  Google Scholar 

  41. Vandewinckele L, Claessens M, Dinkla A, Brouwer C, Crijns W, Verellen D et al (2020) Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance. Radiother Oncol 153:55–66. https://doi.org/10.1016/j.radonc.2020.09.008

    Article  PubMed  Google Scholar 

  42. Brouwer CL, Steenbakkers RJHM, Bourhis J, Budach W, Grau C, Grégoire V et al (2015) CT-based delineation of organs at risk in the head and neck region: DAHANCA, EORTC, GORTEC, HKNPCSG, NCIC CTG, NCRI, NRG Oncology and TROG consensus guidelines. Radiother Oncol 117:83–90. https://doi.org/10.1016/j.radonc.2015.07.041

    Article  PubMed  Google Scholar 

Download references

Funding

This work was funded by the Italian Ministry of Health, Grant AuToMI (GR-2019-12370739).

Author information

Authors and Affiliations

  1. Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy

    Damiano Dei, Nicola Lambri, Luisa Bellu, Carmelo Carlo-Stella, Roberto Rusconi, Giacomo Reggiori & Marta Scorsetti

  2. Department of Radiotherapy and Radiosurgery, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy

    Damiano Dei, Nicola Lambri, Elena Clerici, Luisa Bellu, Pierina Navarria, Roberto Rusconi, Giacomo Reggiori, Stefano Tomatis, Marta Scorsetti & Pietro Mancosu

  3. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

    Leonardo Crespi, Ricardo Coimbra Brioso & Daniele Loiacono

  4. Health Data Science Centre, Human Technopole, Milan, Italy

    Leonardo Crespi

  5. Department of Oncology and Hematology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, Milan, Italy

    Chiara De Philippis, Stefania Bramanti & Carmelo Carlo-Stella

Authors
  1. Damiano Dei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicola Lambri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leonardo Crespi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ricardo Coimbra Brioso
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniele Loiacono
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Elena Clerici
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Luisa Bellu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chiara De Philippis
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Pierina Navarria
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Stefania Bramanti
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Carmelo Carlo-Stella
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Roberto Rusconi
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Giacomo Reggiori
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Stefano Tomatis
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Marta Scorsetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Pietro Mancosu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by DD, NL, LC, RCB, DL, and PM The first draft of the manuscript was written by DD and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nicola Lambri.

Ethics declarations

Competing interests

The authors have no conflict of interests to disclose.

Ethical approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of IRCCS Humanitas Research Hospital (ID 2928, 26 January 2021). ClinicalTrials.gov identifier: NCT04976205.

Consent to participate

Informed consent was obtained from all individual participants included in the 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 (DOCX 732 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

Dei, D., Lambri, N., Crespi, L. et al. Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation. Radiol med 129, 515–523 (2024). https://doi.org/10.1007/s11547-024-01760-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11547-024-01760-8

Keywords

Search

Navigation