Abstract
Objectives: The purpose of the study is predicting the sites of the recurrence with PET/CT by serum CA-125 level and detecting the cut-off value of CA-125 for metastatic ovarian cancer (OC) in comparison with Fluorine-18 FDG PET/CT.
Materials & Methods: For 38 patients with histological stage III-IV OC, F-18 FDG PET/CT studies (n=59) referred for suspicion of relapsing of OC were conducted. PET/CT images were assessed as positive/negative in 4 categories based on similar location as local recurrence, peritoneal metastasis, lymph node metastases and distant metastases. Patients were divided into five groups according to the levels of CA-125. The results of PET/CT imaging were compared with the level of CA-125.
Results: Recurrence was confirmed in all FDG-PET/CT studies. In 7 of them (11.9%) CA-125 levels were normal (mean: 18.9±5.9) whereas in 52 of them (88%) were high (mean: 433.9±798.3). Moderate but highly significant positive correlation between CA-125 level and the number of metastatic foci detected by PET/CT was found. There was no statistically significant difference between CA-125 level subgroups and metastatic sites. However, the difference between CA-125 levels and location of metastasis was statistically significant only for distant metastasis and peritoneal metastasis with moderate accuracy (71% and %66, respectively).
Conclusion: Since CA-125 has moderate but highly significant positive correlation with the number of metastatic foci, it is important in clinical management of OC patients. However, it may not predict the localization of the recurrence. When suspicious findings were reported at radiodiagnostic techniques in OC patients, FDG-PET is a useful technique for detecting recurrent ovarian carcer regardless of CA-125 level.
Keywords: Serum CA-125, metastatic ovarian cancer (OC), fluorine-18 FDG PET/CT, peritoneal metastasis, lymph node metastases, distant metastases.
Graphical Abstract
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
16
3