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Background: The majority of ovarian cancer biomarker discovery efforts focus on the identification of proteins that can improve the predictive power of presently available diagnostic tests. We here show that metabolomics, the study of metabolic changes in biological systems, can also provide characteristic small molecule fingerprints related to this disease.

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.

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Arthroscopic reduction and fixation of a bony Bankart lesion can achieve good results in selected cases. The size of the reconstructed glenoid is crucial to the success of the surgery.

Background: Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic, especially at the early stages of progression and/or recurrence. We report here the integration of a new mass spectrometric technology with a novel support vector machine computational method for use in cancer diagnostics, and describe the application of the method to ovarian cancer.Methods: We coupled a high-throughput ambient ionization technique for mass spectrometry (direct analysis in real-time mass spectrometry) to profile relative metabolite levels in sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions. The profiles were input to a customized functional support vector machine-based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations.Results: The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy (100% sensitivity and 100% specificity by the 64-30 split validation test; 100% sensitivity and 98% specificity by leave-one-out cross-validations).Conclusion: The method has significant clinical potential as a cancer diagnostic tool. Because of the extremely low prevalence of ovarian cancer in the general population (∼0.04%), extensive prospective testing will be required to evaluate the test's potential utility in general screening applications. However, more immediate applications might be as a diagnostic tool in higher-risk groups or to monitor cancer recurrence after therapeutic treatment.Impact: The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome. Cancer Epidemiol Biomarkers Prev; 19(9); 2262-71. ©2010 AACR.

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