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Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma

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A Correction to this article was published on 11 June 2022

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Abstract

Objectives

This study aimed to create a predictive model for cervical lymph node metastasis (CLNM) in patients with tongue squamous cell carcinoma (SCC) based on radiomics features detected by [18F]-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography (PET).

Methods

A total of 40 patients with tongue SCC who underwent 18F-FDG PET imaging during their first medical examination were enrolled. During the follow-up period (mean 28 months), 20 patients had CLNM, including six with late CLNM, whereas the remaining 20 patients did not have CLNM. Radiomics features were extracted from 18F-FDG PET images of all patients irrespective of metal artifact, and clinicopathological factors were obtained from the medical records. Late CLNM was defined as the CLNM that occurred after major treatment. The least absolute shrinkage and selection operator (LASSO) model was used for radiomics feature selection and sequential data fitting. The receiver operating characteristic curve analysis was used to assess the predictive performance of the 18F-FDG PET-based model and clinicopathological factors model (CFM) for CLNM.

Results

Six radiomics features were selected from LASSO analysis. The average values of the area under the curve (AUC), accuracy, sensitivity, and specificity of radiomics analysis for predicting CLNM from 18F-FDG PET images were 0.79, 0.68, 0.65, and 0.70, respectively. In contrast, those of the CFM were 0.54, 0.60, 0.60, and 0.60, respectively. The 18F-FDG PET-based model showed significantly higher AUC than that of the CFM.

Conclusions

The 18F-FDG PET-based model has better potential for diagnosing CLNM and predicting late CLNM in patients with tongue SCC than the CFM.

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Data availability

The data that support the findings of this study are available from the corresponding author, TK, upon reasonable request.

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References

  1. Castelijns JA, van den Brekel MW. Detection of lymph node metastases in the neck: radiologic criteria. AJNR Am J Neuroradiol. 2001;22:3–4. https://doi.org/10.1148/radiology.192.3.8058923.

    Article  Google Scholar 

  2. Eida S, Sumi M, Yonetsu K, Kimura Y, Nakamura T. Combination of helical CT and Doppler sonography in the follow-up of patients with clinical N0 stage neck disease and oral cancer. AJNR Am J Neuroradiol. 2003;24:312–8.

    Google Scholar 

  3. Schöder H, Carlson DL, Kraus DH, Stambuk HE, Gönen M, Erdi YE, et al. 18F-FDG PET/CT for detecting nodal metastases in patients with oral cancer staged N0 by clinical examination and CT/MRI. J Nucl Med. 2006;47:755–62.

    Google Scholar 

  4. Pandeshwar P, Jayanthi K, Raghuram P. Pre-operative contrast enhanced computer tomographic evaluation of cervical nodal metastatic disease in oral squamous cell carcinoma. Indian J Cancer. 2013;50:310–5. https://doi.org/10.4103/0019-509X.123605.

    Article  Google Scholar 

  5. Pfister DG, Ang K, Brizel DM, Burtness BA, Cmelak AJ, Colevas AD, et al. Head and Neck Cancers, version 3.2021, NCCN clinical practice guidelines in oncology. Accessed 20 Sep 2021. http://www.nccn.org/guidelines/guidelines-detail?category=1&id=1437;9:596–650;9:596–650. https://doi.org/10.6004/jnccn.2011.0053

  6. D’Cruz AK, Vaish R, Kapre N, Dandekar M, Gupta S, Hawaldar R, et al. Elective versus therapeutic neck dissection in node-negative oral cancer. N Engl J Med. 2015;373:521–9. https://doi.org/10.1056/NEJMoa1506007.

    Article  Google Scholar 

  7. Yuen AP, Wei WI, Wong YM, Tang KC. Elective neck dissection versus observation in the treatment of early oral tongue carcinoma. Head Neck. 1997;19:583–8. https://doi.org/10.1002/(SICI)1097-0347(199710)19:7%3c583::AID-HED4%3e3.0.CO;2-3.

    Article  Google Scholar 

  8. Lim YC, Lee JS, Koo BS, Kim SH, Kim YH, Choi EC. Treatment of contralateral N0 neck in early squamous cell carcinoma of the oral tongue: elective neck dissection versus observation. Laryngoscope. 2006;116:461–5. https://doi.org/10.1097/01.mlg.0000195366.91395.9b.

    Article  Google Scholar 

  9. Kelner N, Vartanian JG, Pinto CA, Coutinho-Camillo CM, Kowalski LP. Does elective neck dissection in T1/T2 carcinoma of the oral tongue and floor of the mouth influence recurrence and survival rates? Br J Oral Maxillofac Surg. 2014;52:590–7. https://doi.org/10.1016/j.bjoms.2014.03.020.

    Article  Google Scholar 

  10. Zhong Y, Yuan M, Zhang T, Zhang YD, Li H, Yu TF. Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma. AJR Am J Roentgenol. 2018;211:109–13. https://doi.org/10.2214/AJR.17.19074.

    Article  Google Scholar 

  11. Cui X, Wang N, Zhao Y, Chen S, Li S, Xu M, et al. Preoperative prediction of axillary lymph node metastasis in breast cancer using radiomics features of DCE-MRI. Sci Rep. 2019. https://doi.org/10.1038/s41598-019-38502-0.

    Article  Google Scholar 

  12. Japanese Society for Head and Neck cancer guidelines for the treatment of oral cancer. Accessed 30 Jun 2021. http://www.jsco-cpg.jp/headandneck-cancer/algo/#III-B-1

  13. Japanese Society of Oral Oncology guidelines for the treatment of oral cancer. Accessed 30 Jun 2021. https://www.jstage.jst.go.jp/article/jsot1989/19/3/19_3_139/_pdf/-char/ja

  14. Yamamoto E, Miyakawa A, Kohama G. Mode of invasion and lymph node metastasis in squamous cell carcinoma of the oral cavity. Head Neck Surg. 1984;6:938–47. https://doi.org/10.1002/hed.2890060508.

    Article  Google Scholar 

  15. Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60:5471–96. https://doi.org/10.1088/0031-9155/60/14/5471.

    Article  Google Scholar 

  16. Haga A, Takahashi W, Aoki S, Nawa K, Yamashita H, Abe O, et al. Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis. Radiol Phys Technol. 2018;11:27–35. https://doi.org/10.1007/s12194-017-0433-2.

    Article  Google Scholar 

  17. Di Martino E, Nowak B, Hassan HA, Hausmann R, Adam G, Buell U, et al. Diagnosis and staging of head and neck cancer: a comparison of modern imaging modalities (positron emission tomography, computed tomography, color-coded duplex sonography) with panendoscopic and histopathologic findings. Arch Otolaryngol Head Neck Surg. 2000;126:1457–61. https://doi.org/10.1001/archotol.126.12.1457.

    Article  Google Scholar 

  18. Ahn PH, Garg MK. Positron emission tomography/computed tomography for target delineation in head and neck cancers. Semin Nucl Med. 2008;38:141–8. https://doi.org/10.1053/j.semnuclmed.2007.11.002.

    Article  Google Scholar 

  19. Houweling AC, Wolf AL, Vogel WV, Hamming-Vrieze O, van Vliet-Vroegindeweij CV, van de Kamer JB, et al. FDG-PET and diffusion-weighted MRI in head-and-neck cancer patients: implications for dose painting. Radiother Oncol. 2013;106:250–4. https://doi.org/10.1016/j.radonc.2013.01.003.

    Article  Google Scholar 

  20. Yan O, Wang H, Han Y, Fu S, Chen Y, Liu F. Prognostic relevance of 18F-FDG-PET/CT-guided target volume delineation in loco-regionally advanced nasopharyngeal carcinomas: a comparative study. Front Oncol. 2021;11: 709622. https://doi.org/10.3389/fonc.2021.709622.

    Article  Google Scholar 

  21. Lee SJ, Choi JY, Lee HJ, Baek CH, Son YI, Hyun SH, et al. Prognostic value of volume-based 18F-fluorodeoxyglucose PET/CT parameters in patients with clinically node-negative oral tongue squamous cell carcinoma. Korean J Radiol. 2012;13:752–9. https://doi.org/10.3348/kjr.2012.13.6.752.

    Article  Google Scholar 

  22. Thomas TO, Refaat T, Choi M, Bacchus I, Sachdev S, Rademaker AW, et al. Brachial plexus dose tolerance in head and neck cancer patients treated with sequential intensity modulated radiation therapy. Radiat Oncol. 2015;10:94. https://doi.org/10.1186/s13014-015-0409-5.

    Article  Google Scholar 

  23. Merlotti A, Alterio D, Vigna-Taglianti RV, Muraglia A, Lastrucci L, Manzo R, et al. Technical guidelines for head and neck cancer IMRT on behalf of the Italian association of radiation oncology - head and neck working group. Radiat Oncol. 2014;9:264. https://doi.org/10.1186/s13014-014-0264-9.

    Article  Google Scholar 

  24. Zhou Z, Chen L, Sher D, Zhang Q, Shah J, Pham NL, et al. Predicting lymph node metastasis in head and neck cancer by combining many-objective radiomics and 3-dimensional convolutional neural network through evidential reasoning. Annu Int Conf IEEE Eng Med Biol Soc. 2018. https://doi.org/10.1109/EMBC.2018.8513070.

    Article  Google Scholar 

  25. Haider SP, Zeevi T, Baumeister P, Reichel C, Sharaf K, Forghani R, et al. Potential added value of PET/CT radiomics for survival prognostication beyond AJCC 8th edition staging in oropharyngeal squamous cell carcinoma. Cancers (Basel). 2020;12:1778. https://doi.org/10.3390/cancers12071778.

    Article  Google Scholar 

  26. Martens RM, Koopman T, Noij DP, Pfaehler E, Übelhör C, Sharma S, et al. Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma. EJNMMI Res. 2020;10:102. https://doi.org/10.1186/s13550-020-00686-2.

    Article  Google Scholar 

  27. Chen L, Zhou Z, Sher D, Zhang Q, Shah J, Pham NL, et al. Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer. Phys Med Biol. 2019;64: 075011. https://doi.org/10.1088/1361-6560/ab083a.

    Article  Google Scholar 

  28. Zhai TT, Langendijk JA, van Dijk LV, Halmos GB, Witjes MJH, Oosting SF, et al. The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation. Oral Oncol. 2019;95:178–86. https://doi.org/10.1016/j.oraloncology.2019.06.020.

    Article  Google Scholar 

  29. Diamant A, Chatterjee A, Vallières M, Shenouda G, Seuntjens J. Deep learning in head & neck cancer outcome prediction. Sci Rep. 2019;9:2764. https://doi.org/10.1038/s41598-019-39206-1.

    Article  Google Scholar 

  30. Romeo V, Cuocolo R, Ricciardi C, Ugga L, Cocozza S, Verde F, et al. Prediction of tumor grade and nodal status in oropharyngeal and oral cavity squamous-cell carcinoma using a radiomic approach. Anticancer Res. 2020;40:271–80.

    Article  Google Scholar 

  31. Miki K, Mori S, Hasegawa A, Naganawa K, Koto M. Single-energy metal artefact reduction with CT for carbon-ion radiation therapy treatment planning. Br J Radiol. 2016;89:20150988. https://doi.org/10.1259/bjr.20150988.

    Article  Google Scholar 

  32. Arena L, Morehouse HT, Safir J. MR imaging artifacts that simulate disease: how to recognize and eliminate them. Radiographics. 1995;15:1373–94. https://doi.org/10.1148/radiographics.15.6.8577963.

    Article  Google Scholar 

  33. Kaneda T, Minami M, Curtin HD, Utsunomiya T, Shirouzu I, Yamashiro M, et al. Dental bur fragments causing metal artifacts on MR images. AJNR Am J Neuroradiol. 1998;19:317–9.

    Google Scholar 

  34. Huang SH, Hwang D, Lockwood G, Goldstein DP, O’Sullivan B. Predictive value of tumor thickness for cervical lymph-node involvement in squamous cell carcinoma of the oral cavity: a meta-analysis of reported studies. Cancer. 2009;115:1489–97. https://doi.org/10.1002/cncr.24161.

    Article  Google Scholar 

  35. Bur AM, Holcomb A, Goodwin S, Woodroof J, Karadaghy O, Shnayder Y, et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral Oncol. 2019;92:20–5. https://doi.org/10.1016/j.oraloncology.2019.03.011.

    Article  Google Scholar 

  36. Shaha AR, Spiro RH, Shah JP, Strong EW. Squamous carcinoma of the floor of the mouth. Am J Surg. 1984;148:455–9. https://doi.org/10.1016/0002-9610(84)90369-6.

    Article  Google Scholar 

  37. Spiro RH, Huvos AG, Wong GY, Spiro JD, Gnecco CA, Strong EW. Predictive value of tumor thickness in squamous carcinoma confined to the tongue and floor of the mouth. Am J Surg. 1986;152:345–50. https://doi.org/10.1016/0002-9610(86)90302-8.

    Article  Google Scholar 

  38. Rodolico V, Barresi E, Di Lorenzo R, Leonardi V, Napoli P, Rappa F, et al. Lymph node metastasis in lower lip squamous cell carcinoma in relation to tumour size, histologic variables and p27Kip1 protein expression. Oral Oncol. 2004;40:92–8. https://doi.org/10.1016/S1368-8375(03)00141-6.

    Article  Google Scholar 

  39. Umeda M, Yokoo S, Take Y, Omori A, Nakanishi K, Shimada K. Lymph node metastasis in squamous cell carcinoma of the oral cavity: correlation between histologic features and the prevalence of metastasis. Head Neck. 1992;14:263–72. https://doi.org/10.1002/hed.2880140402.

    Article  Google Scholar 

  40. Franceschi D, Gupta R, Spiro RH, Shah JP. Improved survival in the treatment of squamous carcinoma of the oral tongue. Am J Surg. 1993;166:360–5. https://doi.org/10.1016/S0002-9610(05)80333-2.

    Article  Google Scholar 

  41. Shin JH, Yoon HJ, Kim SM, Lee JH, Myoung H. Analyzing the factors that influence occult metastasis in oral tongue cancer. J Korean Assoc Oral Maxillofac Surg. 2020;46:99–107. https://doi.org/10.5125/jkaoms.2020.46.2.99.

    Article  Google Scholar 

  42. Frierson HF Jr, Cooper PH. Prognostic factors in squamous cell carcinoma of the lower lip. Hum Pathol. 1986;17:346–54. https://doi.org/10.1016/S0046-8177(86)80457-9.

    Article  Google Scholar 

  43. Sparano A, Weinstein G, Chalian A, Yodul M, Weber R. Multivariate predictors of occult neck metastasis in early oral tongue cancer. Otolaryngol Head Neck Surg. 2004;131:472–6. https://doi.org/10.1016/j.otohns.2004.04.008.

    Article  Google Scholar 

  44. Kurokawa H, Yamashita Y, Takeda S, Zhang M, Fukuyama H, Takahashi T. Risk factors for late cervical lymph node metastases in patients with stage I or II carcinoma of the tongue. Head Neck. 2002;24:731–6. https://doi.org/10.1002/hed.10130.

    Article  Google Scholar 

  45. Jakobsson PA, Eneroth CM, Killander D, Moberger G, Mårtensson B. Histologic classification and grading of malignancy in carcinoma of the larynx. Acta Radiol Ther Phys Biol. 1973;12:1–8. https://doi.org/10.3109/02841867309131085.

    Article  Google Scholar 

  46. Willén R, Nathanson A. Squamous cell carcinoma of the gingiva Histological classification and grading of malignancy. Acta Oto-laryngol. 1973;75:299–300. https://doi.org/10.3109/00016487309139722.

    Article  Google Scholar 

  47. Yamane M, Ishii J, Izumo T, Nagasawa T, Amagasa T. Noninvasive quantitative assessment of oral tongue cancer by intraoral ultrasonography. Head Neck. 2007;29:307–14. https://doi.org/10.1002/hed.20523.

    Article  Google Scholar 

  48. Kaneoya A, Hasegawa S, Tanaka Y, Omura K. Quantitative analysis of invasive front in tongue cancer using ultrasonography. J Oral Maxillofac Surg. 2009;67:40–6. https://doi.org/10.1016/j.joms.2007.08.006.

    Article  Google Scholar 

  49. Shinozaki Y, Jinbu Y, Ito H, Noguchi T, Kusama M, Matsumoto N, et al. Relationship between appearance of tongue carcinoma on intraoral ultrasonography and histopathologic findings. Oral Surg Oral Med Oral Pathol Oral Radiol. 2014;117:634–9. https://doi.org/10.1016/j.oooo.2014.02.001.

    Article  Google Scholar 

  50. Chien CY, Su CY, Hwang CF, Chuang HC, Chen CM, Huang CC. High expressions of CD105 and VEGF in early oral cancer predict potential cervical metastasis. J Surg Oncol. 2006;94:413–7. https://doi.org/10.1002/jso.20546.

    Article  Google Scholar 

  51. Lim SC, Zhang S, Ishii G, Endoh Y, Kodama K, Miyamoto S, et al. Predictive markers for late cervical metastasis in stage I and II invasive squamous cell carcinoma of the oral tongue. Clin Cancer Res. 2004;10:166–72. https://doi.org/10.1158/1078-0432.CCR-0533-3.

    Article  Google Scholar 

  52. Gontarz M, Wyszyńska-Pawelec G, Zapała J, Czopek J, Lazar A, Tomaszewska R. Immunohistochemical predictors in squamous cell carcinoma of the tongue and floor of the mouth. Head Neck. 2016;38(Suppl 1):E747–53. https://doi.org/10.1002/hed.24087.

    Article  Google Scholar 

  53. Mermod M, Jourdan EF, Gupta R, Bongiovanni M, Tolstonog G, Simon C, et al. Development and validation of a multivariable prediction model for the identification of occult lymph node metastasis in oral squamous cell carcinoma. Head Neck. 2020;42:1811–20. https://doi.org/10.1002/hed.26105.

    Article  Google Scholar 

  54. Shan J, Jiang R, Chen X, Zhong Y, Zhang W, Xie L, et al. Machine learning predicts lymph node metastasis in early-stage oral tongue squamous cell carcinoma. J Oral Maxillofac Surg. 2020;78:2208–18. https://doi.org/10.1016/j.joms.2020.06.015.

    Article  Google Scholar 

  55. Yip SS, Aerts HJ. Applications and limitations of radiomics. Phys Med Biol. 2016;61:R150–66. https://doi.org/10.1088/0031-9155/61/13/R150.

    Article  Google Scholar 

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Acknowledgements

We thank Dr. Noriaki Takeda and Dr. Makoto Fukui for their scientific advice.

Funding

This work was supported by Grants-in-Aid for Scientific-Research (Grant number 19K10268).

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by TK, AH, KK, AT, MS, YK, HI, and YM. The first draft of the manuscript was written by TK, and all authors commented on the previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Takaharu Kudoh.

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The authors declare no conflicts of interest.

Ethical approval

The study was approved by the ethics committee of the Tokushima University (approval number 3212, date of approval July 23, 2018), and the study protocol was performed in accordance to the Declaration of Helsinki.

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The requirement for informed consent was waived by the institutional review board owing to the retrospective nature of the study.

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Kudoh, T., Haga, A., Kudoh, K. et al. Radiomics analysis of [18F]-fluoro-2-deoxyglucose positron emission tomography for the prediction of cervical lymph node metastasis in tongue squamous cell carcinoma. Oral Radiol 39, 41–50 (2023). https://doi.org/10.1007/s11282-022-00600-7

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