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

Beasley, William; Thor, Maria; McWilliam, A.; Green, Andrew; Mackay, Ranald I; Slevin, N.; Olsson, Caroline; Pettersson, Niclas; Finizia, Caterina; Estilo, Cherry L.; Riaz, Nadeem; Lee, Nancy Y.; Deasy, Joseph O.; Herk, Marcel van

doi:10.1016/j.ijrobp.2018.05.054

Cited by 31 publications

(36 citation statements)

References 37 publications

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“…Clinical assessment of patients is challenging due to a restricted ability to assess disease status. There are several patient, tumour and treatment related factors for trismus of which radiotherapy is a known contributor with an incidence in advanced oropharyngeal cancers of 35-55% [16][17][18][19][20]. Mouth opening is a complex action controlled by the synergistic actions of the paired muscles of mastication.…”

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confidence: 99%

Use of a novel atlas for muscles of mastication to reduce inter observer variability in head and neck radiotherapy contouring

Hague

Beasley

Dixon

et al. 2019

Radiotherapy and Oncology

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Trismus is caused by injury to the masticatory muscles resulting from cancer or its treatment. Contouring these muscles to reduce dose and radiation related trismus can be problematic due to interobserver variability. This study aimed to evaluate the reduction in interobserver variability achievable with a new contouring atlas. Materials/methods: The atlas included: medial and lateral pterygoids (MP, LP), masseter (M) and temporalis (T) muscles, and the temporo-mandibular joint (TMJ). Seven clinicians delineated five paired structures on CT scans from 5 patients without the atlas. After 5 weeks, contouring was repeated using the atlas. Using contours generated by the clinicians on the same 5 CT scans as reference, dice similarity coefficient (DSC), mean distance-to-agreement (DTA) and centre of mass (COM) difference were compared with and without the atlas. Comparison was also performed split by training grade. Mean and standard deviation (SD) values were measured. Results: The atlas reduced interobserver variability for all structures. Mean DTA significantly improved for MP (p = 0.01), M (p < 0.01), T (p < 0.01) and TMJ (p < 0.01). Mean DTA improved using the atlas for the trainees across all muscles, with the largest reduction in variability observed for the T (4.3 ± 7.1 v 1.2 ± 0.4 mm, p = 0.06) and TMJ (2.1 ± 0.7 v 0.8 ± 0.3 mm, p < 0.01). Distance between the COM and interobserver variability reduced in all directions for MP and T. Conclusion: A new atlas for contouring masticatory muscles during radiotherapy planning for head and neck cancer reduces interobserver variability and could be used as an educational tool.

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mentioning

confidence: 99%

Use of a novel atlas for muscles of mastication to reduce inter observer variability in head and neck radiotherapy contouring

Hague

Beasley

Dixon

et al. 2019

Radiotherapy and Oncology

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“…The five reports demonstrated a significant dose-effect relationship between ipsilateral masseter or pterygoid muscle and trismus in the multivariate analysis (14)(15)(16)(17)(18). The three reports demonstrated that the clinical factors; tumor location, gender, baseline MIO, baseline age, dentition, free soft tissue transfer after surgery, reirradiation, chemotherapy, natural logarithm of time post-RT, and overall treatment time of RT correlated with trismus in multivariate analysis (43,46,47).…”

Section: Discussionmentioning

confidence: 99%

“…Trismus is considered to result from slowly progressive fibrosis of the mandible joints and muscles involved in mandibular movements (9)(10)(11). Some data exist on the relationship between dosimetric factors of organs at risk (OARs) and trismus in univariate analysis and multivariate analysis (12)(13)(14)(15)(16)(17)(18)(19). However, limited data exist on the relation among dosimetric factors of OARs, clinical factors, and trismus in multivariate analysis (20).…”

Section: Abstract Background/aim: the Aim Of This Study Was To Develmentioning

confidence: 99%

Development of Normal Tissue Complication Probability Model for Trismus in Head and Neck Cancer Patients Treated With Radiotherapy: The Role of Dosimetric and Clinical Factors

et al. 2019

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following eligibility criteria: 1) HNC originating in the oral cavity, nasopharynx, oropharynx, hypopharynx, or larynx; 2) treated with definitive RT either alone or in combination with chemotherapy or cetuximab; 3) no previous surgery, RT and/or chemotherapy; 4) no previous malignancies; 5) no distant metastases; 6) planningcomputed tomography (CT) and three-dimensional dose distributions available prior to and 6 months after completion of (CH)RT; 7) no efforts of reducing dose in potential organs at risks (OARs) for trismus; 8) alive with no recurrences 6 months after RT, and: 9) no rehabilitation or medication for trismus. The study population used for this analysis was composed of 132 patients who fulfilled all these eligibility criteria. The majority of the patients were male (77%) and the mean age of the study population was 62 years, ranging from 33 to 89 years. The number of the patients treated with concurrent systemic therapy was 40 (30%). The demographic and tumor characteristics of this study population are listed in Table I.

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“…The aim of the study by Beasley et al (20) was to identify specific CT image regions with a dose-toxicity association to identify radiation-induced trismus in H&N patients treated with RT. To achieve this objective, an image-based data mining (IBDM) framework was applied to a cohort of 86 patients.…”

Section: Head and Neckmentioning

confidence: 99%

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

et al. 2020

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In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.

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

Cited by 31 publications

References 37 publications

Use of a novel atlas for muscles of mastication to reduce inter observer variability in head and neck radiotherapy contouring

Use of a novel atlas for muscles of mastication to reduce inter observer variability in head and neck radiotherapy contouring

Development of Normal Tissue Complication Probability Model for Trismus in Head and Neck Cancer Patients Treated With Radiotherapy: The Role of Dosimetric and Clinical Factors

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

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