Abstract
Surgical management of ovarian tumours largely depends on their histo-pathological diagnosis. Currently, screening for ovarian malignancy with tumour markers in conjunction with radiological investigations has a low specificity for discriminating benign from malignant tumours. Also, pre-operative biopsy of ovarian masses increases the risk of intra-peritoneal dissemination of malignancy. Intra-operative frozen section, although sufficiently accurate in differentiating tumours according to their histological type, increases operation times. This results in increased surgery-related risks to the patient and additional burden to resource allocation. We set out to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, combined with chemometric analysis can be applied to discriminate between normal, borderline and malignant ovarian tumours and classify ovarian carcinoma subtypes according to the unique spectral signatures of their molecular composition. Formalin-fixed, paraffin-embedded ovarian tissue blocks were de-waxed, mounted on Low-E slides and desiccated before being analysed using ATR-FTIR spectroscopy. Chemometric analysis in the form of principal component analysis (PCA), successive projection algorithm (SPA) and genetic algorithm (GA), followed by linear discriminant analysis (LDA) of the obtained spectra revealed clear segregation between benign versus borderline versus malignant tumours as well as segregation between different histological tumour subtypes, when these approaches are used in combination. ATR-FTIR spectroscopy coupled with chemometric analysis has the potential to provide a novel diagnostic approach in the accurate diagnosis of ovarian tumours assisting surgical decision making to avoid under-treatment or over-treatment, with minimal impact to the patient.
Highlights
Ovarian cancer is the 5th most common gynaecological cancer and the 4th most common cause of cancer death in women in the UK.[1]
On comparing the spectra using principal component analysis (PCA)-linear discriminant analysis (LDA), seven PCs where used as this number provided significant classification (P < 0.001) without the introduction of arbitrary separation
The chemometric technique that classified the three classes most successfully [66.4%] was genetic algorithm (GA)-LDA using 29 variables determined from the minimum cost function G (Fig. 3g and h)
Summary
Ovarian cancer is the 5th most common gynaecological cancer (incidence of 18 per 100 000) and the 4th most common cause of cancer death (mortality of 8.8 per 10 000) in women in the UK.[1]. Ovarian cancer encompasses a heterogeneous group of tumours, as indicated by differences in epidemiological and genetic risk factors, precursor lesions, patterns of spread, molecular events during oncogenesis, response to chemotherapy and prognosis.[3] Ninety percent of ovarian cancers are malignant epithelial tumours termed carcinomas, the remainder being germ cell and sex cord-stromal tumours.[4] The commonest types of ovarian carcinomas are high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), mucinous carcinoma (MC), endometrioid carcinoma (EC), clear cell carcinoma (CCC), carcinosarcoma (CS) and mixed tumours (MT). Carcinomas are graded according to their cellular differentiation from normal tissue. This does not apply to HGSC and LGSC as they are considered different entities.[5] Borderline epithelial tumours comprise approximately 15% of epithelial ovarian tumours and have a good prognosis. With the implementation of the “international classification of diseases for oncology (ICD-O-3)”, these tumours are no longer considered malignant.[6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
