Lawrence, Alexandra Claire

2018-05-14T10:13:58Z

2018-05-14T10:13:58Z

2008

Malignant ovarian tumours are diagnosed at an advanced stage in 75% of cases and they have the highest mortality figures of all gynaecological cancers. As the treatment of benign and malignant adnexal masses is significantly different it is important to be able to reliably distinguish between them preoperatively. Thus, women with malignancies could be referred to cancer centres, whilst those with benign conditions could be offered more conservative management.

The aims of this thesis are (1) To investigate the use of new tumour markers in the preoperative diagnosis of ovarian cancer. (2) To validate previously published models and compare their performance to subjective assessment and to the models developed in this thesis. (3) To investigate the differences between small asymptomatic masses and large masses and to investigate the accuracy of published models on the diagnosis of malignancy in small masses.

CA 125, CA 15-3 and CA 72-4 were significantly raised in the presence of ovarian cancer. CA 72-4 was higher in mucinous cancers and CA 125 and CA 15-3 were higher in serous and endometrioid cancers. Her-2/neu and CA 19-9 were not significantly different in benign or malignant disease. Logistic regression analysis showed age, CA125 and CA 15-3 to be the most valuable discriminators. A neural network was designed and trained which gave a sensitivity of 100% and a specificity of 90.9% on the test set. None of the six published models tested prospectively performed as well as in their original publication. The IOTA logistic regression model performed best and gave a sensitivity of 81.8% and a specificity of 72.3%. Subjective assessment of the mass gave a sensitivity of 72.7% with a specificity of 81.8%. Small masses were more commonly unilocular and large masses multilocular. Ascites, papillary proliferations, detectable flow and the smoothness of the internal wall discriminated well between benign and malignant small cysts. Age, menopausal status and CA125 were not discriminatory. None of the published models were as accurate as subjective assessment at diagnosing malignancy. These data suggest that statistical models may be of less value than tumour markers and subjective assessment in the diagnosis of ovarian malignancy.

This work improves our ability to predict malignancy in a pelvic mass. As a result of this work, further research might aim to combine the use of tumour markers and subjective assessment to improve the preoperative diagnosis of malignancy. It may thus be possible to provide care in a cancer centre for those women that need it and to allow conservative management or minimally invasive surgery for women with benign disease.

http://hdl.handle.net/1842/29843

The University of Edinburgh

Annexe Thesis Digitisation Project 2018 Block 18

Already catalogued

The use of novel tumour markers and statistical models in the preoperative diagnosis of ovarian cancer

Thesis or Dissertation

Doctoral

MD Doctor of Medicine