Abstract
Background. Absorbed radiation dose to the mandible is an important risk factor in the development of mandibular osteoradionecrosis (ORN) in head and neck cancer (HNC) patients treated with radiotherapy (RT). The prediction of mandibular ORN may not only guide the RT treatment planning optimisation process but also identify which patients would benefit from a closer follow-up post-RT for an early diagnosis and intervention of ORN. Existing mandibular ORN prediction models are based on dose-volume histogram (DVH) metrics that omit the spatial localisation and dose gradient and direction information provided by the clinical mandible radiation dose distribution maps. Methods. We propose the use of a binary classification 3D DenseNet121 to extract the relevant dosimetric information directly from the 3D mandible radiation dose distribution maps and predict the incidence of ORN. We compare the results to a Random Forest ensemble with DVH-based parameters. Results. The 3D DenseNet121 model was able to discriminate ORN vs. non-ORN cases with an average AUC of 0.71 (0.64–0.79), compared to 0.65 (0.57–0.73) for the RF model. Conclusion. Obtaining the dosimetric information directly from the clinical radiation dose distribution maps may enhance the performance and functionality of ORN normal tissue complication probability (NTCP) models.
A. P. King and T. Guerrero Urbano—Joint last authors.
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References
Frankart, A.J., Frankart, M.J., Cervenka, B., Tang, A.L., Krishnan, D.G., Takiar, V.: Osteoradionecrosis: exposing the evidence not the bone. Int. J. Radiat. Oncol. Biol. Phys. 109(5), 1206–1218 (2021)
Chen, J.A., et al.: Osteoradionecrosis of mandible bone in patients with oral cancer-associated factors and treatment outcomes. Head Neck 38, 762–768 (2016)
Patel, V., Ormondroyd, L., Lyons, A., McGurk, M.: The financial burden for the surgical management of osteoradionecrosis. Br. Dent. J. 222, 177–180 (2017)
Habib, S., Sassoon, I., Thompson, I., Patel, V.: Risk factors associated with osteoradionecrosis. Oral Surg. 14, 227–35 (2021)
Humbert-Vidan, L., et al.: PH-0387 Mandible osteoradionecrosis: a dosimetric study (poster presented at ESTRO 2021, Madrid, Spain). Radiother. Oncol. 161, S285–S286 (2021)
Van Dijk, L.V., et al.: Normal tissue complication probability (NTCP) prediction model for osteoradionecrosis of the mandible in patients with head and neck cancer after radiation therapy: large-scale observational cohort. Int. J. Radiat. Oncol. Biol. Phys. 111(2), 549–558 (2021)
Humbert-Vidan, L., Patel, V., Oksuz, I., King, A.P., Guerrero Urbano, T.: Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer. Br. J. Radiol. 94 (2021)
Beasley, W., et al.: Image-based data mining to probe dosimetric correlates of radiation-induced trismus. Int. J. Radiat. Oncol. Biol. Phys. 102(4), 1330–1338 (2018)
Jiang, W., et al.: Machine learning methods uncover radiomorphologic dose patterns in salivary glands that predict xerostomia in patients with head and neck cancer. Adv. Radiat. Oncol. 4(2), 401–412 (2019)
Gabryś, H.S., Buettner, F., Sterzing, F., Hauswald, H., Bangert, M.: Design and selection of machine learning methods using radiomics and dosiomics for normal tissue complication probability modeling of xerostomia. Front. Oncol. 8(25), 35 (2018)
Dean, J., et al.: Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy. Clin. Trans. Radiat. Oncol. 8, 27–39 (2018)
Ibragimov, B., Toesca, D., Chang, D., Yuan, Y., Koong, A., Xing, L.: Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT. Med. Phys. 45(10), 4763–4774 (2018)
Men, K., Geng, H., Zhong, H., Fan, Y., Lin, A., Xiao, Y.: A deep learning model for predicting xerostomia due to radiotherapy for head-and-neck squamous cell carcinoma in the RTOG 0522 clinical trial. Int. J. Radiat. Oncol. Biol. Phys. 105(2), 440–447 (2019)
Notani, K., et al.: Osteoradionecrosis of the mandible-factors influencing severity. Asian J. Oral Maxillofac. Surg. 14(1), 5–9 (2002)
Williams, M.V., Denekamps, J., Fowler, J.F.: A review of alpha/beta ratios for experimental tumours: implications for clinical studies of altered fractionation. Int. J. Radiat. Oncol. Biol. Phys. 11, 87–96 (1985)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988)
Robin, X., et al.: pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 12, 77 (2011)
Patel, V., et al.: Radiotherapy quadrant doses in oropharyngeal cancer treated with intensity modulated radiotherapy. Fac. Dent. J. 11, 166–72 (2015)
Aarup-Kirstensen, S., Hansen, C.R., Forner, L., Brink, C., Eriksen, J.G., Johansen, J.: Osteoradionecrosis of the mandible after radiotherapy for head and neck cancer: risk factors and dose-volme correlations. Acta. Oncol. 58(10), 1373–1377 (2019)
MDA Hnc symptom working group: dose-volume correlates of mandibular osteoradionecrosis in oropharynx cancer patients receiving intensity-modulated radiotherapy: results from a case-matched comparison. Radiother. Oncol. 124, 232–239 (2017)
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This work was supported by NVIDIA Corporation with the donation of the Titan Xp GPU and by Cancer Research UK.
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Humbert-Vidan, L., Patel, V., Andlauer, R., King, A.P., Guerrero Urbano, T. (2022). Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learning. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2022. Lecture Notes in Computer Science, vol 13540. Springer, Cham. https://doi.org/10.1007/978-3-031-17721-7_6
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