ABSTRACT
BACKGROUND: Data are needed to improve the current understanding of clinical management and characteristics of patients with advanced prostate cancer (PC) treated with androgen receptor pathway inhibition (ARPI) therapy. METHODS: This retrospective cohort study using real-world, population-level data from Alberta, Canada included all individuals diagnosed in 2017-2020 with de novo metastatic castration-sensitive PC (mCSPC) or nonmetastatic castration-resistant PC (nmCRPC) who initiated androgen deprivation therapy (ADT). For mCSPC, patients were classified as ARPI-exposed if they received an ARPI within 180 days of initiating ADT, while patients with nmCRPC were classified as ARPI-exposed if they received an ARPI within 2 years of diagnosis. RESULTS: This study included 976 patients with mCSPC and 233 with nmCRPC of which 33.5% and 25.3% received an ARPI, respectively. The proportion of patients with mCSPC treated with an ARPI increased considerably for patients diagnosed in 2020 compared to 2017 (56.2% vs. 6.0%). In contrast, the use of ARPI to treat nmCRPC only increased marginally from 2017 to 2019/2020 (19.7% vs. 28.9%). Patients with mHSPC who were ARPI-exposed had longer median survival than patients who were ARPI-naive (38.47 (95% CI = 32.84-NA) vs. 34.19 (95% CI = 33.33-38.83; P = .03)), with a higher proportion of patients surviving to 2-years. For nmCRPC, survival was similar between ARPI-exposed and ARPI-naive. In multivariable analyses, receiving ARPI for mCSPC was associated with younger patient age, more recent diagnoses, fewer comorbidities, a higher number of metastatic sites, referral to a medical oncologist as well as receiving surgery and radiation before ADT. Receiving ARPI for nmCRPC was associated with referral to a medical oncologist, younger age, and more recent diagnoses. CONCLUSIONS: Outcome analyses in this population suggest a continued unmet clinical need and complex clinical management pathways. Given that treatment pathways have evolved considerably, continued follow-up to understand the impact of these advancements on patient outcomes are warranted.
ABSTRACT
BACKGROUND: Laboratory data can provide great value to support research aimed at reducing the incidence, prolonging survival and enhancing outcomes of cancer. Data is characterized by the information it carries and the format it holds. Data captured in Alberta's biomarker laboratory repository is free text, cluttered and rouge. Such data format limits its utility and prohibits broader adoption and research development. Text analysis for information extraction of unstructured data can change this and lead to more complete analyses. Previous work on extracting relevant information from free text, unstructured data employed Natural Language Processing (NLP), Machine Learning (ML), rule-based Information Extraction (IE) methods, or a hybrid combination between them. METHODS: In our study, text analysis was performed on Alberta Precision Laboratories data which consisted of 95,854 entries from the Southern Alberta Dataset (SAD) and 6944 entries from the Northern Alberta Dataset (NAD). The data covers all of Alberta and is completely population-based. Our proposed framework is built around rule-based IE methods. It incorporates topics such as Syntax and Lexical analyses to achieve deterministic extraction of data from biomarker laboratory data (i.e., Epidermal Growth Factor Receptor (EGFR) test results). Lexical analysis compromises of data cleaning and pre-processing, Rich Text Format text conversion into readable plain text format, and normalization and tokenization of text. The framework then passes the text into the Syntax analysis stage which includes the rule-based method of extracting relevant data. Rule-based patterns of the test result are identified, and a Context Free Grammar then generates the rules of information extraction. Finally, the results are linked with the Alberta Cancer Registry to support real-world cancer research studies. RESULTS: Of the original 5512 entries in the SAD dataset and 5017 entries in the NAD dataset which were filtered for EGFR, the framework yielded 5129 and 3388 extracted EGFR test results from the SAD and NAD datasets, respectively. An accuracy of 97.5% was achieved on a random sample of 362 tests. CONCLUSIONS: We presented a text analysis framework to extract specific information from unstructured clinical data. Our proposed framework has shown that it can successfully extract relevant information from EGFR test results.