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Diagnostic problems in medicine are sometimes polytomous, meaning that the outcome has more than two distinct categories. For example, ovarian tumors can be benign, borderline, primary invasive, or metastatic. Extending the main measure of binary discrimination, the c-statistic or area under the ROC curve, to nominal polytomous settings is not straightforward. This paper reviews existing measures and presents the polytomous discrimination index (PDI) as an alternative. The PDI assesses all sets of k cases consisting of one case from each outcome category. For each category i (i = 1, … ,k), it is assessed whether the risk of category i is highest for the case from category i. A score of 1∕k is given per category for which this holds, yielding a set score between 0 and 1 to indicate the level of discrimination. The PDI is the average set score and is interpreted as the probability to correctly identify a case from a randomly selected category within a set of k cases. This probability can be split up by outcome category, yielding k category-specific values that result in the PDI when averaged. We demonstrate the measures on two diagnostic problems (residual mass histology after chemotherapy for testicular cancer; diagnosis of ovarian tumors). We compare the behavior of the measures on theoretical data, showing that PDI is more strongly influenced by simultaneous discrimination between all categories than by partial discrimination between pairs of categories. In conclusion, the PDI is attractive because it better matches the requirements of a measure to summarize polytomous discrimination.

BackgroundElevated levels of matrix metalloproteinases have been found to associate with poor prognosis in various carcinomas. This study aimed at evaluating plasma levels of MMP1, MMP8 and MMP13 as diagnostic and prognostic markers of breast cancer.MethodsA total of 208 breast cancer patients, of which 21 with inflammatory breast cancer, and 42 healthy controls were included. Plasma MMP1, MMP8 and MMP13 levels were measured using ELISA and correlated with clinicopathological characteristics.ResultsMedian plasma MMP1 levels were higher in controls than in breast cancer patients (3.45 vs. 2.01 ng/ml), while no difference was found for MMP8 (10.74 vs. 10.49 ng/ml). ROC analysis for MMP1 revealed an AUC of 0.67, sensitivity of 80% and specificity of 24% at a cut-off value of 4.24 ng/ml. Plasma MMP13 expression could not be detected. No correlation was found between MMP1 and MMP8 levels. We found a trend of lower MMP1 levels with increasing tumour size (p = 0.07); and higher MMP8 levels with premenopausal status (p = 0.06) and NPI (p = 0.04). The median plasma MMP1 (p = 0.02) and MMP8 (p = 0.007) levels in the non-inflammatory breast cancer patients were almost twice as high as those found in the inflammatory breast cancer patients. Intriguingly, plasma MMP8 levels were positively associated with lymph node involvement but showed a negative correlation with the risk of distant metastasis. Both controls and lymph node negative patients (pN0) had lower MMP8 levels than patients with moderate lymph node involvement (pN1, pN2) (p = 0.001); and showed a trend for higher MMP8 levels compared to patients with extensive lymph node involvement (pN3) and a strong predisposition to distant metastasis (p = 0.11). Based on the hypothesis that blood and tissue protein levels are in reverse association, these results suggest that MMP8 in the tumour may have a protective effect against lymph node metastasis.ConclusionIn summary, we observed differences in MMP1 and MMP8 plasma levels between healthy controls and breast cancer patients as well as between breast cancer patients. Interestingly, our results suggest that MMP8 may affect the metastatic behaviour of breast cancer cells through protection against lymph node metastasis, underlining the importance of anti-target identification in drug development.