Functional estrogen receptor signal transduction pathway activity and antihormonal therapy response in low-grade ovarian carcinoma.

BACKGROUND: Advanced low-grade ovarian carcinoma (LGOC) is difficult to treat. In several studies, high estrogen receptor (ER) protein expression was observed in patients with LGOC, which suggests that antihormonal therapy (AHT) is a treatment option. However, only a subgroup of patients respond to AHT, and this response cannot be adequately predicted by currently used immunohistochemistry (IHC). A possible explanation is that IHC only takes the ligand, but not the activity, of the whole signal transduction pathway (STP) into account. Therefore, in this study, the authors assessed whether functional STP activity can be an alternative tool to predict response to AHT in LGOC. METHODS: Tumor tissue samples were obtained from patients with primary or recurrent LGOC who subsequently received AHT. Histoscores of ER and progesterone receptor (PR) were determined. In addition, STP activity of the ER STP and of six other STPs known to play a role in ovarian cancer was assessed and compared with the STP activity of healthy postmenopausal fallopian tube epithelium. RESULTS: Patients who had normal ER STP activity had a progression-free survival (PFS) of 16.1 months. This was significantly shorter in patients who had low and very high ER STP activity, with a median PFS of 6.0 and 2.1 months, respectively (p < .001). Unlike ER histoscores, PR histoscores were strongly correlated to the ER STP activity and thus to PFS. CONCLUSIONS: Aberrant low and very high functional ER STP activity and low PR histoscores in patients with LGOC indicate decreased response to AHT. ER IHC is not representative of functional ER STP activity and is not related to PFS.

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review.

OBJECTIVES: Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors. METHODS: We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards. RESULTS: In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set. CONCLUSION: Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.