Normal Tissue Complication Probability (NTCP) Modelling of Severe Acute Mucositis using a Novel Oral Mucosal Surface Organ at Risk

Dean, Jamie; Welsh, Liam; Wong, Kee H.; Aleksić, Aleksandar; Dunne, Emma; Islam, Mohammad Rafiqul; Patel, A.; Patel, Priyanka; Petkar, Imran; Phillips, I.; Sham, J.; Schick, Ulrike; Newbold, K.; Bhide, Shreerang A.; Harrington, Kevin; Nutting, Chris; Gulliford, S.

doi:10.1016/j.clon.2016.12.001

Cited by 24 publications

(20 citation statements)

References 48 publications

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“…Moreover, there has been growing interest in the adoption of machine learning classifiers in NTCP modeling ( 13 – 15 ). Buettner et al used Bayesian logistic regression together with dose-shape features to predict xerostomia in head-and-neck cancer patients ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

et al. 2018

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PurposeThe purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands.Material and methodsA cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0–6 months (early), 6–15 months (late), 15–24 months (long-term), and at any time (a longitudinal model) after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis.ResultsNTCP models based on the parotid mean dose failed to predict xerostomia (AUCs < 0.60). The most informative predictors were found for late and long-term xerostomia. Late xerostomia correlated with the contralateral dose gradient in the anterior–posterior (AUC = 0.72) and the right–left (AUC = 0.68) direction, whereas long-term xerostomia was associated with parotid volumes (AUCs > 0.85), dose gradients in the right–left (AUCs > 0.78), and the anterior–posterior (AUCs > 0.72) direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose–volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing methods.ConclusionWe demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments. Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia. The facilitated machine learning pipeline is described in detail and can serve as a valuable reference for future work in radiomic and dosiomic NTCP modeling.

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Section: Introductionmentioning

confidence: 99%

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

et al. 2018

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“…Incorporation of 3D dose information within dose-response models can be performed with 3D moment invariants. This has been used for the parotid glands (xerostomia) 28 , oral mucosa (mucositis) 29,30 and pharyngeal mucosa (dysphagia) 31 . This technique parameterises the centre of mass, skewness and spread of the dose distribution within an organ, which enables toxicities in both parallel and serial structures to be investigated.…”

Section: Discussionmentioning

confidence: 99%

Image-based Data Mining to Probe Dosimetric Correlates of Radiation-induced Trismus

Beasley

Thor

McWilliam

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

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IBDM bypasses the common assumption that dose patterns within structures are unimportant. Our novel IBDM approach for continuous outcome variables successfully identified a cluster of voxels that are highly associated with trismus, overlapping partially with the ipsilateral masseter. Tests on an external validation cohort showed an even stronger correlation with trismus. These results support use of the region in HNRT treatment planning to potentially reduce trismus.

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“…Although regarded as a severe and potentially dose limiting acute side effect of radiotherapy for oropharyngeal cancer, there is uncertainty in defining the delineation of the oral cavity as an OAR to enable oral mucositis reduction [ 20 , 21 , 22 ]. The reasons for this uncertainty can be elucidated from a consideration of the history of head and neck radiotherapy over the last 30 years.…”

Section: Oral Cavity Oar Definitionmentioning

confidence: 99%

“…This consisted of a 3 mm contour of all the mucosal surfaces in the oral cavity. The same group then successfully published NTCP models for grade 3 mucositis based on the original patients in the Bhide et al study with an additional 207 patients from other prospective studies [ 22 ]. However, they found that neither the use of the MSC nor the use of machine learning or atlas-based methods improved the performance of the NTCP model [ 35 , 36 ].…”

Section: Oral Cavity Oar Definitionmentioning

confidence: 99%

Assessing Novel Drugs and Radiation Technology in the Chemoradiation of Oropharyngeal Cancer

et al. 2018

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Integrating immunotherapy, proton therapy and biological dose escalation into the definitive chemoradiation of oropharyngeal cancer poses several challenges. Reliable and reproducible data must be obtained in a timely fashion. However, despite recent international radiotherapy contouring guidelines, controversy persists as to the applicability of such guidelines to all cases. Similarly, a lack of consensus exists concerning both the definition of the organ at risk for oral mucositis and the most appropriate endpoint to measure for this critical toxicity. Finally, the correlation between early markers of efficacy such as complete response on PET CT following treatment and subsequent survival needs elucidation for biological subsets of oropharyngeal cancer.

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Normal Tissue Complication Probability (NTCP) Modelling of Severe Acute Mucositis using a Novel Oral Mucosal Surface Organ at Risk

Abstract: The simpler oral cavity OAR should be preferred over the mucosal surface OAR for NTCP modelling of severe mucositis. We recommend minimising the volume of mucosa receiving intermediate and high doses, where possible.

Cited by 24 publications

References 48 publications

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

Image-based Data Mining to Probe Dosimetric Correlates of Radiation-induced Trismus

Assessing Novel Drugs and Radiation Technology in the Chemoradiation of Oropharyngeal Cancer

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