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Deep learning–based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net

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Abstract

Purpose

To evaluate deep learning–based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net.

Methods

This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson’s correlation and Bland–Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment.

Results

All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91–0.93).

Conclusion

The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI.

Clinical relevance statement

Deep learning–based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning.

Key Points

• The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI.

• MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model.

• Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.

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Abbreviations

2D:

Two-dimensional

3D:

Three-dimensional

DL:

Deep learning

DSC:

Dice similarity coefficient

HPV:

Human papillomavirus

OPSCC:

Oropharyngeal squamous cell carcinoma

T1CE:

Fat-suppressed contrast-enhanced T1-weighted images

T2WI:

T2-weighted images

References

  1. Weatherspoon DJ, Chattopadhyay A, Boroumand S, Garcia I (2015) Oral cavity and oropharyngeal cancer incidence trends and disparities in the United States: 2000–2010. Cancer Epidemiol 39:497–504

    Article  PubMed  PubMed Central  Google Scholar 

  2. Gormley M, Creaney G, Schache A, Ingarfield K, Conway DI (2022) Reviewing the epidemiology of head and neck cancer: definitions, trends and risk factors. Br Dent J 233:780–786

    Article  PubMed  PubMed Central  Google Scholar 

  3. de Almeida JR, Li R, Magnuson JS et al (2015) Oncologic outcomes after transoral robotic surgery: a multi-institutional study. JAMA Otolaryngol Head Neck Surg 141:1043–1051

    Article  PubMed  PubMed Central  Google Scholar 

  4. Forastiere AA, Zhang Q, Weber RS et al (2013) Long-term results of RTOG 91–11: a comparison of three nonsurgical treatment strategies to preserve the larynx in patients with locally advanced larynx cancer. J Clin Oncol 31:845

    Article  CAS  PubMed  Google Scholar 

  5. Eisbruch A, Harris J, Garden AS et al (2010) Multi-institutional trial of accelerated hypofractionated intensity-modulated radiation therapy for early-stage oropharyngeal cancer (RTOG 00–22). Int J Radiat Oncol Biol Phys 76:1333–1338

    Article  PubMed  Google Scholar 

  6. Urban D, Corry J, Rischin D (2014) What is the best treatment for patients with human papillomavirus–positive and –negative oropharyngeal cancer? Cancer 120:1462–1470

    Article  PubMed  Google Scholar 

  7. Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63:101693

    Article  PubMed  Google Scholar 

  8. Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18:203–211

    Article  CAS  PubMed  Google Scholar 

  9. Huo L, Hu X, Xiao Q, Gu Y, Chu X, Jiang L (2021) Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images. Magn Reson Imaging 82:31–41

    Article  CAS  PubMed  Google Scholar 

  10. Lin D, Wang Z, Li H et al (2023) Automated measurement of pancreatic fat deposition on Dixon MRI using nnU-Net. J Magn Reson Imaging 57:296–307

    Article  PubMed  Google Scholar 

  11. Theis M, Tonguc T, Savchenko O et al (2023) Deep learning enables automated MRI-based estimation of uterine volume also in patients with uterine fibroids undergoing high-intensity focused ultrasound therapy. Insights Imaging 14:1

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kang H, Witanto JN, Pratama K et al (2023) Fully automated MRI segmentation and volumetric measurement of intracranial meningioma using deep learning. J Magn Reson Imaging 57:871–881. https://doi.org/10.1002/jmri.28332

    Article  PubMed  Google Scholar 

  13. Wennmann M, Neher P, Stanczyk N et al (2023) Deep learning for automatic bone marrow apparent diffusion coefficient measurements from whole-body magnetic resonance imaging in patients with multiple myeloma: a retrospective multicenter study. Investig Radiol 58:273–282. https://doi.org/10.1097/RLI.0000000000000932

  14. Heidenreich JF, Gassenmaier T, Ankenbrand MJ, Bley TA, Wech T (2021) Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction. Eur J Radiol 141:109817

    Article  PubMed  Google Scholar 

  15. Kok YE, Pszczolkowski S, Law ZK et al (2022) Semantic segmentation of spontaneous intracerebral hemorrhage, intraventricular hemorrhage, and associated edema on CT images using deep learning. Radiol Artif Intell 4:e220096

    Article  PubMed  PubMed Central  Google Scholar 

  16. Dot G, Schouman T, Dubois G, Rouch P, Gajny L (2022) Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework. Eur Radiol 32:3639–3648

    Article  PubMed  Google Scholar 

  17. Cardenas CE, McCarroll RE, Court LE et al (2018) Deep learning algorithm for auto-delineation of high-risk oropharyngeal clinical target volumes with built-in dice similarity coefficient parameter optimization function. Int J Radiat Oncol Biol Phys 101:468–478

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kihara S, Koike Y, Takegawa H et al (2022) Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment. Med Dosim 48:20–24. https://doi.org/10.1016/j.meddos.2022.09.004

    Article  PubMed  Google Scholar 

  19. Wahid KA, Ahmed S, He R et al (2022) Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: results from a prospective imaging registry. Clin Transl Radiat Oncol 32:6–14

    CAS  PubMed  Google Scholar 

  20. Rodríguez Outeiral R, Bos P, Al-Mamgani A, Jasperse B, Simões R, van der Heide UA (2021) Oropharyngeal primary tumor segmentation for radiotherapy planning on magnetic resonance imaging using deep learning. Phys Imaging Radiat Oncol 19:39–44

    Article  PubMed  PubMed Central  Google Scholar 

  21. Li X, Morgan PS, Ashburner J, Smith J, Rorden C (2016) The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods 264:47–56

    Article  PubMed  Google Scholar 

  22. Avants BB, Tustison N, Song G (2009) Advanced normalization tools (ANTS). Insight J 2:1–35

    Google Scholar 

  23. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  24. Aydin OU, Taha AA, Hilbert A et al (2021) On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking. Eur Radiol Exp 5:1–7

    Article  Google Scholar 

  25. Savjani R (2021) nnU-Net: further automating biomedical image autosegmentation. Radiol Imaging Cancer 3:e209039

    Article  PubMed  PubMed Central  Google Scholar 

  26. El-Hariri H, SoutoMaior Neto LA, Cimflova P et al (2022) Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with acute ischemic stroke. Comput Biol Med 141:105033

    Article  PubMed  Google Scholar 

  27. Cimflova P, Ospel JM, Marko M, Menon BK, Qiu W (2022) Variability assessment of manual segmentations of ischemic lesion volume on 24-h non-contrast CT. Neuroradiology 64:1165–1173

    Article  PubMed  Google Scholar 

  28. Chung KJ, Kuang H, Federico A et al (2021) Semi-automatic measurement of intracranial hemorrhage growth on non-contrast CT. Int J Stroke 16:192–199

    Article  PubMed  Google Scholar 

  29. Hodneland E, Dybvik JA, Wagner-Larsen KS et al (2021) Automated segmentation of endometrial cancer on MR images using deep learning. Sci Rep 11:1–8

    Article  Google Scholar 

  30. Blinde S, Mohamed ASR, Al-Mamgani A et al (2017) Large interobserver variation in the International MR-LINAC Oropharyngeal Carcinoma Delineation Study. Int J Radiat Oncol Biol Phys 99:E639–E640

    Article  Google Scholar 

  31. Moe YM, Groendahl AR, Tomic O, Dale E, Malinen E, Futsaether CM (2021) Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients. Eur J Nucl Med Mol Imaging 48:2782–2792

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bielak L, Wiedenmann N, Berlin A et al (2020) Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis. Radiat Oncol 15:1–9

    Article  Google Scholar 

  33. Ren J, Eriksen JG, Nijkamp J, Korreman SS (2021) Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation. Acta Oncol 60:1399–1406

    Article  CAS  PubMed  Google Scholar 

  34. Shiga K, Ogawa T, Katagiri K et al (2012) Differences between oral cancer and cancers of the pharynx and larynx on a molecular level. Oncol Lett 3:238–243

    Article  CAS  PubMed  Google Scholar 

  35. Argiris A, Karamouzis MV, Raben D, Ferris RL (2008) Head and neck cancer. Lancet 371:1695–1709

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Arshad M, Hara J, Rosenberg AJ et al (2022) Assessment of tumor burden and response by RECIST vs. volume change in HPV+ oropharyngeal cancer – an exploratory analysis of prospective trials. Int J Radiat Oncol Biol Phys 114:S113–S114

    Article  Google Scholar 

  37. Choi Y, Nam Y, Jang J et al (2020) Prediction of human papillomavirus status and overall survival in patients with untreated oropharyngeal squamous cell carcinoma: development and validation of CT-based radiomics. Am J Neuroradiol 41:1897–1904

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Min Park Y, Yol Lim J, Woo Koh Y, Kim S-H, Chang Choi E (2021) Prediction of treatment outcome using MRI radiomics and machine learning in oropharyngeal cancer patients after surgical treatment. Oral Oncol 122:105559

    Article  PubMed  Google Scholar 

  39. Wang P, Wang X, Zhang M, Li G, Zhao N, Qiao Q (2022) Combining the radiomics signature and HPV status for the risk stratification of patients with OPC. Oral Dis (Early View). https://doi.org/10.1111/odi.14386

  40. Song B, Yang K, Garneau J et al (2021) Radiomic features associated with HPV status on pretreatment computed tomography in oropharyngeal squamous cell carcinoma inform clinical prognosis. Front Oncol 11:744250

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A1A01040285).

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Authors and Affiliations

  1. Department of Radiology, Seoul St. Mary’s Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea

    Yangsean Choi, Minkook Seo & Jinhee Jang

  2. Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Centre, 43 Olympic-Ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea

    Yangsean Choi

  3. Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea

    Jooin Bang & Sang-Yeon Kim

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  1. Yangsean Choi
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Correspondence to Yangsean Choi.

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The scientific guarantor of this publication is Yangsean Choi.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was not required for this study because of the retrospective study design.

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Institutional Review Board approval was obtained.

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• retrospective

• cross-sectional study

• performed at one institution

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Choi, Y., Bang, J., Kim, SY. et al. Deep learning–based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10585-y

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