Biomarkers for predicting complete debulking in ovarian cancer: lessons to be learned
Research output: Contribution to journal › Journal article › Research › peer-review
AIM: We aimed to construct and validate a model based on biomarkers to predict complete primary debulking surgery for ovarian cancer patients.
PATIENTS AND METHODS: The study consisted of three parts: Part I: Biomarker data obtained from mass spectrometry, baseline data and, surgical outcome were used to construct predictive indices for complete tumour resection; Part II: sera from randomly selected patients from part I were analyzed using enzyme-linked immunosorbent assay (ELISA) to investigate the correlation to mass spectrometry; Part III: the indices from part I were validated in a new cohort of patients.
RESULTS: Part I: The area under the receiver operating characteristic curve (AUC) was 0.82 for both indices. Part II: Linear regression analysis gave an R(2) value of 0.52 and 0.63 for transferrin and β2-microglobulin, respectively. Part III: The AUC of the two indices decreased to 0.64.
CONCLUSION: Our validated model based on biomarkers was unable to predict surgical outcome for patients with ovarian cancer.
| Original language | English |
|---|---|
| Journal | Anticancer Research |
| Volume | 34 |
| Issue number | 2 |
| Pages (from-to) | 679-682 |
| Number of pages | 4 |
| ISSN | 0250-7005 |
| Publication status | Published - Feb 2014 |
- Cohort Studies, Enzyme-Linked Immunosorbent Assay, Female, Humans, Linear Models, Logistic Models, Mass Spectrometry, Models, Statistical, Ovarian Neoplasms, Predictive Value of Tests, Tumor Markers, Biological
Research areas
ID: 137670701