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Title: Morphometric analysis of data inherent in examination by magnetic resonance imaging : importance to natural history, prognosis and disease staging of squamous carcinoma of the oral cavity
Author: Boland, Paul William
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2010
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Abstract:
Magnetic resonance imaging plays an important yet underutilized role in determining the natural history and prognosis of oral carcinoma. Depth of tumour invasion is an emergent factor in the oral cancer literature. However, problems exist with the definition of cut-points suitable for inclusion in TNM staging criteria. Statistical methodology represents a possible explanation but is underexplored. In this work, a review of the depth of invasion literature is conducted with emphasis on statistical technique. As well, statistical simulation is used to explore the implications of the of the minimum p-value method. The results demonstrate that the use of continuous variable categorization and multiple testing is widespread, and contributes to cut-point variability and false-positive tests. Depth, as a predictor of OCLNM and survival, must be questioned. The volume of tumour invasion is a promising prognostic factor that has not been fully investigated in the oral carcinoma literature. In this work, the volume of tumour invasion is measured on MRI and compared to thickness and maximum diameter in its capacity to predict 2-year all-cause, disease-related and disease-free survival, as well as occult cervical lymph node metastasis prediction. As part of a comprehensive approach, morphometric factors are incorporated into multifactor predictive models using regression, artificial neural networks and recursive partitioning. It is evident that MRI-based volume is superior all other linear measurements for both occult cervical lymph node metastasis and survival prediction. Artificial neural networks wee superior to all other techniques for survival prediction. There is a case for a unified artificial neural networks model for survival prediction that uses volume, midline invasion and N-stage to determine prognosis. This model can be used to determine individualized probabilities of 2-year survival. The lateral extrinsic muscles of the tongue lie just beneath the surface of the lateral tongue, yet their invasion is a criterion for T4 classification using the TNM staging system. In this work, the Visible Human Female is used to conduct an anatomic study of the extrinsic muscles of the tongue. Linear measurement is used to quantify the distance from the surface mucosa to the most superficial muscle fibres of the styloglossus and genioglossus. Further, the lateral extrinsic muscles are poorly demonstrated on MRI. An anatomic atlas of the tongue is fused with MRI images of oral carcinoma to demonstrate lateral muscle invasion. The results demonstrate that the styloglossus and hyoglossus lie very close to the surface of the lateral tongue, in some cases passing within 1 mm of the surface mucosa. These extrinsic muscles are readily invaded by even small tumours of the lateral tongue. Strict application of the TNM T4a criteria leads to unnecessary upstaging as these carcinomas do not warrant the prognosis and aggressive treatment of Stage IV disease. Extrinsic muscle invasion should be removed as a T4a criterion for the oral cavity. A separate category, T4a (oral tongue) specifying invasion of the genioglossus is also recommended. This work presented in this thesis is an original contribution to the field of oral cavity cancer research and has determined that there is capacity for improvement in current efforts to determine the natural history and prognosis of oral cavity squamous cell carcinoma. This thesis is the first to examine the role of statistical methodology in oral carcinoma depth of invasion cut-point variability. Further, this work presents an original approach to the prediction of regional metastasis and survival using advanced multivariate modeling techniques. No other work explored MRI-measured volume using the substantial sample size gathered in this thesis. Finally, this work is the first to demonstrate that lateral extrinsic muscle invasion is an unnecessary component of the T4a (oral cavity) classification criteria and should be reconsidered.
Supervisor: Golding, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.526563  DOI: Not available
Keywords: Otolaryngology ; Radiology ; Tumours ; Oncology ; oral cancer ; magnetic resonance imaging ; staging ; prognosis ; surgery
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