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Introduction/Background: Cancer registries in the US collect timely and systematic data on new cancer cases, extent of disease, staging, biomarker status, treatment, survival, and mortality of cancer cases. Existing methodologies for accessing local cancer registry data for research are time-consuming and often rely on the manual merging of data by staff registrars. In addition, existing registries do not provide direct access to these data nor do they routinely provide linkage to discrete electronic health record (EHR) data, reports, or imaging data. Automation of such linkage can provide an impressive data resource and make valuable data available for translational cancer research. Methods: The UPMC Network Cancer Registry collects highly structured, longitudinal data on all reportable cancer patients, from the point of the diagnosis throughout treatment and follow-up/outcomes. Using commercial registry software, we collect data in compliance with standards governed by the North American Association of Central Cancer Registries. This standardization ensures that the data are highly structured with standard coding and collection methods, which support data exchange among central cancer registries and the Centers for Disease Control and Prevention. Results: At the UPMC Hillman Cancer Center and University of Pittsburgh, we explored the feasibility of linking this well-curated, structured cancer registry data with unstructured text (i.e., pathology and radiology reports), using the Text Information Extraction System (TIES). We used the TIES platform to integrate breast cancer cases from the UPMC Network Cancer Registry system and then combine these data with other EHR data as a pilot use case that can be replicated for other cancers. Conclusions: As a result of this integration, we now have a single searchable repository of information for breast cancer patients from the UPMC registry, combined with their pathology and radiology reports. The system that we developed is easily scalable to other health systems and cancer centers.

Gynecologic cancers pose significant morbidity and mortality burden for US women. Endometrial cancer is the most common gynecologic malignancy in the US, while ovarian cancer is one of the deadliest. Since research funding for some of the gynecologic malignancies has been limited, novel research on the existing datasets needs to be harnessed to improve the health of the women affected by these conditions. We aim to evaluate if Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions, has capabilities for conducting research in the area of gynecologic cancers. TIES system provides effective tools for searching deidentified structured and unstructured data for gynecologic cancers with capabilities to explore pathology and radiology records. We used TIES system to identify records of patients with ovarian, endometrial, and cervical cancers. We used temporal search features to identify patients who developed ovarian tumors after being diagnosed with endometrial tumors. We examined records for completeness of information on diagnosis, age, and procedure year. We found that TIES system has 13,762 records for endometrial cancer, 7,692 records for ovarian cancer, and 10,826 records for cervical cancer for Pittsburgh site. 306 records have been identified for patients who had both ovarian and endometrial malignancies. Nearly 100% of the records had information on age of the patient, year of the procedure, and exact diagnosis. TIES is a promising tool for conducting research in the area of gynecologic malignancies, especially for rare diseases such as concurrent endometrial and ovarian cancers. Existing data repositories like TIES need to be further explored for novel research questions in the area of gynecologic cancer. Citation Format: Faina Y. Linkov, Melissa Schwenk, Michael Davis, Robert Edwards, Michael Becich. New directions in gynecologic cancer research utilizing Text Information Extraction System (TIES) Cancer Research Network. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr B71.