Abstract
8536 Background: The current standard of care (SoC) for locally advanced stage III NSCLC is concurrent chemoradiotherapy (CCRT) but the outcomes are poor and unsatisfactory. The purpose of this study is to analyze the clinical features of patients with locally advanced lung cancer for 10 years in order to help develop future treatment strategy. Methods: This study through big data analysis retrospectively collected de-identified patient data from clinical data warehouse (CDW) using an unique algorithm with Standard Query Language (SQL). This new algorithm was developed by the close interactive collaboration between senior data scientists and medical oncologists. These algorithms include clinical natural language processing (NLP) systems that generate structured information from unstructured free text and structured data capture (SDC). We performed pre-processing work and data quality management (DQM) operation using over 700 clinical variables from 23,735 patients with NSCLC. Through data extraction, transformation, cleansing, and organization, we have developed a systematic and optimized program for lung cancer cohorts, including clinical features and molecular study and outcomes. It is also automatically updated every 24 hours in real time. Results: In the past 10 years, 23,735 patients were diagnosed with NSCLC and complete clinical data were available in 22,718 patients (95.7%). Out of total 22,718 patients 4,138 (18.2%) were diagnosed with stage III NSCLC. Among them, 2,676 patients (64.7%) received any type(s) of anti-cancer treatments or regular follow up at our institute. Of these 2,676 patients, 1,275 (47.6%) received curative surgery (+/- neo- and/or adjuvant CCRT); 685 (25.6%) patients definitive CCRT ; 220 (8.2%) patients palliative thoracic RT; 76 (2.8%) patients best supportive care. Median OS was 48.0 months for neoadjuvant CCRT followed by curative surgery, 51.8 months for curative surgery +/- adjuvant treatment, 29.4 months for unresected definitive CCRT (PFS 10.0 months (range: 9.1-10.9). Molecular profiles as well as updated clinical data will be presented. Conclusions: This unique in-house algorithm enables us to do a rapid and comprehensive analysis of the big data through CDW, which can be also automatically updated daily. This should provide clinically relevant information about real-world treatment outcomes and help implement or develop new treatment strategy in a timely manner.
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