A novel algorithm for better distinction of primary mucinous ovarian carcinomas and mucinous carcinomas metastatic to the ovary.

Primary mucinous ovarian carcinomas (MOC) are notoriously difficult to distinguish from mucinous carcinomas metastatic to the ovary (mMC). Studies performed on small cohorts reported algorithms based on tumor size and laterality to aid in distinguishing MOC from mMC. We evaluated and improved these by performing a large-scale, nationwide search in the Dutch Pathology Registry. All registered pathology reports fulfilling our search criteria concerning MOC in the Netherlands from 2000 to 2011 were collected. Age, histology, laterality, and size were extracted. An existing database covering the same timeline containing tumors metastatic to the ovary was used, extracting all mMC, age, size, laterality, and primary tumor location. Existing algorithms were applied to our cohort. Subsequently, an algorithm based on tumor histology, laterality, and a nomogram based on age and size was created for differentiating MOC and mMC. We identified 735 MOC and 1018 mMC. Patients with MOC were significantly younger and MOC were significantly larger and more often unilateral than mMC. Signet ring cell carcinomas were rarely primary. Our algorithm used signet ring cell histology, bilaterality, and a nomogram integrating patient age and tumor size to diagnose mMC. Sensitivity and specificity for mMC was 90.1% and 59.0%, respectively. Applying existing algorithms on our cohort yielded a far lower sensitivity. The algorithm described here using tumor histology, laterality, size, and patient age has higher sensitivity but lower specificity compared to earlier algorithms and aids in indicating tumor origin, but for conclusive diagnosis, careful integration of morphology, immunohistochemistry, and clinical and imaging data is recommended.