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. 2022 Jul 25;19(15):9057.
doi: 10.3390/ijerph19159057.

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center

Andrea D'Aviero  1 , Alessia Re  1 , Francesco Catucci  1 , Danila Piccari  1   2 , Claudio Votta  1   2 , Domenico Piro  1   2 , Antonio Piras  3 , Carmela Di Dio  1 , Martina Iezzi  1 , Francesco Preziosi  1 , Sebastiano Menna  4 , Flaviovincenzo Quaranta  4 , Althea Boschetti  1 , Marco Marras  1 , Francesco Miccichè  2 , Roberto Gallus  5 , Luca Indovina  2 , Francesco Bussu  6   7 , Vincenzo Valentini  2   8 , Davide Cusumano  4 , Gian Carlo Mattiucci  1   8
Affiliations

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center

Andrea D'Aviero et al. Int J Environ Res Public Health. .

Abstract

Background: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by three expert radiation oncologists from a single center.

Methods: Planning computed tomography (CT) scans of patients undergoing RT treatments for H&N cancers were considered. CT scans were processed by Limbus Contour auto-segmentation software, a commercial deep-learning auto-segmentation based software to generate AC. H&N protocol was used to perform AC, with the structure set consisting of bilateral brachial plexus, brain, brainstem, bilateral cochlea, pharyngeal constrictors, eye globes, bilateral lens, mandible, optic chiasm, bilateral optic nerves, oral cavity, bilateral parotids, spinal cord, bilateral submandibular glands, lips and thyroid. Manual revision of OARs was performed according to international consensus guidelines. The AC and MC were compared using the Dice similarity coefficient (DSC) and 95% Hausdorff distance transform (DT).

Results: A total of 274 contours obtained by processing CT scans were included in the analysis. The highest values of DSC were obtained for the brain (DSC 1.00), left and right eye globes and the mandible (DSC 0.98). The structures with greater MC editing were optic chiasm, optic nerves and cochleae.

Conclusions: In this preliminary analysis, deep-learning auto-segmentation seems to provide acceptable H&N OAR delineations. For less accurate organs, AC could be considered a starting point for review and manual adjustment. Our results suggest that AC could become a useful time-saving tool to optimize workload and resources in RT departments.

Keywords: auto-contouring; deep-learning; head and neck; radiotherapy artificial intelligence.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Boxplot analysis of DICE.
Figure 2
Figure 2
Boxplot analysis of 95% Hausdorff distance.
See this image and copyright information in PMC

References

    1. AIOM I Numeri del Cancro in Italia. [(accessed on 24 March 2022)]. Available online: https://www.aiom.it/i-numeri-del-cancro-in-italia/
    1. Patterson J.M., Lu L., Watson L.-J., Harding S., Ness A.R., Thomas S., Waylen A., Pring M., Waterboer T., Sharp L. Associations between markers of social functioning and depression and quality of life in survivors of head and neck cancer: Findings from the Head and Neck Cancer 5000 study. Psycho-Oncol. 2022;31:478–485. doi: 10.1002/pon.5830. - DOI - PubMed
    1. Pfister D.G., Spencer S., Adelstein D., Adkins D., Anzai Y., Brizel D.M., Bruce J.Y., Busse P.M., Caudell J.J., Cmelak A.J., et al. Head and Neck Cancers, Version 2.2020, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Cancer Netw. 2020;18:873–898. doi: 10.6004/jnccn.2020.0031. - DOI - PubMed
    1. Lo Nigro C., Denaro N., Merlotti A., Merlano M. Head and neck cancer: Improving outcomes with a multidisciplinary approach. Cancer Manag. Res. 2017;9:363–371. doi: 10.2147/CMAR.S115761. - DOI - PMC - PubMed
    1. Machiels J.-P., Leemans C.R., Golusinski W., Grau C., Licitra L., Gregoire V. Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS–ESMO–ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann. Oncol. 2020;31:1462–1475. doi: 10.1016/j.annonc.2020.07.011. - DOI - PubMed

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