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Bullen J, Nickel B, McCaffery K, Wilt TJ, Smith J, Boroumand F, Parker L, Millar J, Brodersen JB, Dahm P, Delahunt B, Varma M, Glasziou P, Warden A, Diller L, Billington L, van Rensburg C, Bell K. Impact of the diagnostic label for a low-risk prostate lesion: protocol for two online factorial randomised experiments. BMJ Open 2024; 14:e085947. [PMID: 39122400 PMCID: PMC11331948 DOI: 10.1136/bmjopen-2024-085947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024] Open
Abstract
INTRODUCTION Many types of prostate cancer present minimal risk to a man's lifespan or well-being, but existing terminology makes it difficult for men to distinguish these from high-risk prostate cancers. This study aims to explore whether using an alternative label for low-risk prostate cancer influences management choice and anxiety levels among Australian men and their partners. METHODS AND ANALYSIS We will run two separate studies for Australian men and Australian women with a male partner. Both studies are between-subjects factorial (3×2) randomised online hypothetical experiments. Following consent, eligible participants will be randomised 1:1:1 to three labels: 'low-risk prostate cancer, Gleason Group 1', 'low-risk prostate neoplasm' or 'low-risk prostate lesion'. Participants will then undergo a second randomisation step with 1:1 allocation to the provision of detailed information on the benefits and harms of different management choices versus the provision of less detailed information about management choices. The required sample sizes are 1290 men and 1410 women. The primary outcome is the participant choice of their preferred management strategy: no immediate treatment (prostate-specific antigen (PSA)-based monitoring or active surveillance using PSA, MRI, biopsy with delayed treatment for disease progression) versus immediate treatment (prostatectomy or radiation therapy). Secondary outcomes include preferred management choice (from the four options listed above), diagnosis anxiety, management choice anxiety and management choice at a later time point (for participants who initially choose a monitoring strategy). ETHICS AND DISSEMINATION Ethics approval has been received from The University of Sydney Human Research Ethics Committee (2023/572). The results of the study will be published in a peer-reviewed medical journal and a plain language summary of the findings will be shared on the Wiser Healthcare publications page http://www.wiserhealthcare.org.au/category/publications/ TRIAL REGISTRATION NUMBERS: Australian New Zealand Clinical Trials Registry (ID 386701 and 386889).
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Affiliation(s)
- James Bullen
- School of Public Health, University of Sydney, Sydney, New South Wales, Australia
| | - Brooke Nickel
- Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, New South Wales, Australia
- Wiser Healthcare Research Collaboration, Sydney, New South Wales, Australia
| | - Kirsten McCaffery
- Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, New South Wales, Australia
- Wiser Healthcare Research Collaboration, Sydney, New South Wales, Australia
| | - Timothy J Wilt
- Center for Chronic Disease Outcomes Research and Minneapolis VA High Value Care Initiative, Minneapolis VA Health Care System, Minneapolis, Minnesota, USA
- Department of Medicine, Section of General Internal Medicine, University of Minnesota Twin Cities, Minneapolis, Minnesota, USA
| | - Jenna Smith
- Sydney Health Literacy Lab, School of Public Health, University of Sydney, Sydney, New South Wales, Australia
| | - Farzaneh Boroumand
- School of Public Health, University of Sydney, Sydney, New South Wales, Australia
| | - Lisa Parker
- School of Pharmacy, Faculty of Medicine and Health, Charles Perkins Centre, University of Sydney, Sydney, New South Wales, Australia
- Department of Radiation Oncology, Royal North Shore Hospital, NSW Health, Sydney, New South Wales, Australia
| | - Jeremy Millar
- Radiation Oncology, Alfred Health, Melbourne, Victoria, Australia
- School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
| | - John Brandt Brodersen
- Centre of General Practice, Department of Public Health & Research Unit for General Practice, Region Zealand, University of Copenhagen, Copenhagen, Denmark
- Research Unit for General Practice, Department of Community Medicine, UiT The Arctic University of Norway Faculty of Health Sciences, Tromso, Norway
| | - Philipp Dahm
- Department of Urology, University of Minnesota, Minneapolis, Minnesota, USA
- Urology Section, Minneapolis Veterans Administration Health System, Minneapolis, Minnesota, USA
| | - Brett Delahunt
- Wellington School of Medicine and Health Sciences, University of Otago Wellington, Wellington, New Zealand
| | - Murali Varma
- Department of Cellular Pathology, University Hospital of Wales, Cardiff, UK
| | - Paul Glasziou
- Institute for Evidence-Based Healthcare, Bond University, Gold Coast, Queensland, Australia
| | - Andrew Warden
- Wiser Healthcare Research Collaboration, Sydney, New South Wales, Australia
| | - Lawrence Diller
- Wiser Healthcare Research Collaboration, Sydney, New South Wales, Australia
| | - Larry Billington
- Health Consumers New South Wales, Sydney, New South Wales, Australia
| | | | - Katy Bell
- School of Public Health, University of Sydney, Sydney, New South Wales, Australia
- Wiser Healthcare Research Collaboration, Sydney, New South Wales, Australia
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Nguyen AM, Carter GC, Wilson LAM, Canfield S. Real-world utilization, patient characteristics, and treatment patterns among men with localized prostate cancer tested with the 17-gene genomic prostate score® (GPS TM) assay. Prostate 2024; 84:922-931. [PMID: 38666513 DOI: 10.1002/pros.24709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 03/22/2024] [Accepted: 04/03/2024] [Indexed: 06/04/2024]
Abstract
OBJECTIVES Descriptive study focusing on real-world utilization and characteristics of men with prostate cancer tested with the 17-gene Genomic Prostate Score® (GPS™) assay by linking administrative claims and electronic health record (EHR) data with GPS results. METHODS This retrospective, observational cohort study (January 1, 2013 to December 31, 2020) included men aged 40-80 years with localized prostate cancer claims, continuous enrollment in Optum's Integrated Claims data set, ≥1 day of EHR clinical activity, and a GPS result. Men were classified as undergoing definitive therapy (DT) (prostatectomy, radiation, or focal therapy) or active surveillance (AS). AS and DT distribution were analyzed across GPS results, National Comprehensive Cancer Network® (NCCN®) risk, and race. Costs were assessed 6 months after the first GPS result (index); clinical outcomes and AS persistence were assessed during the variable follow-up. All variables were analyzed descriptively. RESULTS Of 834 men, 650 (77.9%) underwent AS and 184 (22.1%) DT. Most men had Quan-Charlson comorbidity scores of 1-2 and a tumor stage of T1c (index). The most common Gleason patterns were 3 + 3 (79.6%) (AS cohort) and 3 + 4 (55.9%) (DT cohort). The mean (standard deviation) GPS results at index were 23.2 (11.3) (AS) and 30.9 (12.9) (DT). AS decreased with increasing GPS result and NCCN risk. Differences between races were minimal. Total costs were substantially higher in the DT cohort. CONCLUSIONS Most men with GPS-tested localized prostate cancer underwent AS, indicating the GPS result can inform clinical management. Decreasing AS with increasing GPS result and NCCN risk suggests the GPS complements NCCN risk stratification.
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Hong JH, Kuo MC, Cheng YT, Lu YC, Huang CY, Liu SP, Chow PM, Huang KH, Chueh SCJ, Chen CH, Pu YS. Active Surveillance for Taiwanese Men with Localized Prostate Cancer: Intermediate-Term Outcomes and Predictive Factors. World J Mens Health 2024; 42:587-599. [PMID: 37853534 PMCID: PMC11216962 DOI: 10.5534/wjmh.230107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 10/20/2023] Open
Abstract
PURPOSE Active surveillance (AS) is one of the management options for patients with low-risk and select intermediate-risk prostate cancer (PC). However, factors predicting disease reclassification and conversion to active treatment from a large population of pure Asian cohorts regarding AS are less evaluated. This study investigated the intermediate-term outcomes of patients with localized PC undergoing AS. MATERIALS AND METHODS This cohort study enrolled consecutive men with localized non-high-risk PC diagnosed in Taiwan between June 2012 and Jan 2023. The study endpoints were disease reclassification (either pathological or radiographic progression) and conversion to active treatment. The factors predicting endpoints were evaluated using the Cox proportional hazards model. RESULTS A total of 405 patients (median age: 67.2 years) were consecutively enrolled and followed up with a median of 64.6 months. Based on the National Comprehensive Cancer Network (NCCN) risk grouping, 70 (17.3%), 164 (40.5%), 140 (34.6%), and 31 (7.7%) patients were classified as very low-risk, low-risk, favorable-intermediate risk, and unfavorable intermediate-risk PC, respectively. The 5-year reclassification rates were 24.8%, 27.0%, 18.6%, and 25.3%, respectively. The 5-year conversion rates were 20.4%, 28.8%, 43.6%, and 37.8%, respectively. A prostate-specific antigen density (PSAD) of ≥0.15 ng/mL² predicted reclassification (hazard ratio [HR] 1.84, 95% confidence interval [CI] 1.17-2.88) and conversion (HR 1.56, 95% CI 1.05-2.31). A maximal percentage of cancer in positive cores (MPCPC) of ≥15% predicted conversion (15% to <50%: HR 1.41, 95% CI 0.91-2.18; ≥50%: HR 1.97, 95% CI 1.1453-3.40) compared with that of <15%. A Gleason grade group (GGG) of 3 tumor also predicted conversion (HR 2.69, 95% CI 1.06-6.79; GGG 3 vs 1). One patient developed metastasis, but none died of PC during the study period (2,141 person-years). CONCLUSIONS AS is a viable option for Taiwanese men with non-high-risk PC, in terms of reclassification and conversion. High PSAD predicted reclassification, whereas high PSAD, MPCPC, and GGG predicted conversion.
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Affiliation(s)
- Jian-Hua Hong
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ming-Chieh Kuo
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Urology, National Taiwan University Hospital Yunlin Branch, Yunlin, Taiwan
| | - Yung-Ting Cheng
- Department of Urology, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Yu-Chuan Lu
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
- Department of Surgical Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Chao-Yuan Huang
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
| | - Shih-Ping Liu
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
| | - Po-Ming Chow
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
| | - Kuo-How Huang
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Chung-Hsin Chen
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan.
| | - Yeong-Shiau Pu
- Department of Urology, National Taiwan University Hospital, Taipei, Taiwan
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Bazoge A, Morin E, Daille B, Gourraud PA. Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review. JMIR Med Inform 2023; 11:e42477. [PMID: 38100200 PMCID: PMC10757232 DOI: 10.2196/42477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 01/16/2023] [Accepted: 09/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. OBJECTIVE The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. METHODS This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. RESULTS We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). CONCLUSIONS CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.
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Affiliation(s)
- Adrien Bazoge
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
| | - Emmanuel Morin
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Béatrice Daille
- Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France
| | - Pierre-Antoine Gourraud
- Nantes Université, CHU de Nantes, Pôle Hospitalo-Universitaire 11: Santé Publique, Clinique des données, INSERM, CIC 1413, F-44000 Nantes, France
- Nantes Université, INSERM, CHU de Nantes, École Centrale Nantes, Centre de Recherche Translationnelle en Transplantation et Immunologie, CR2TI, F-44000 Nantes, France
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Amaro F, Carvalho M, Bastos MDL, Guedes de Pinho P, Pinto J. Metabolic signature biomarkers for predicting the recurrence of urological cancers. Clin Chim Acta 2023; 549:117553. [PMID: 37690663 DOI: 10.1016/j.cca.2023.117553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/07/2023] [Accepted: 09/07/2023] [Indexed: 09/12/2023]
Abstract
A significant number of patients diagnosed with localized urological cancers experience relapse and disease progression after surgery. Hence, molecular markers for patient risk stratification are needed to improve the current management guidelines. This article critically reviews for the first time, to our knowledge, the promise of metabolomics-based approaches to identify metabolic signatures as candidate prognostic biomarkers to predict recurrences at the time of surgery in prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC). Dysregulations in the levels of several tumoral, circulating, and excreted metabolites have been reported in PCa patients experiencing recurrence within 1.5 to 8 years of follow-up. The combination of these metabolic biomarkers with clinical parameters (e.g., pathological T stage, Gleason score) has shown great potential to improve the predictive ability of PCa recurrence. In contrast, predictive biomarkers of recurrence in BCa and RCC have been poorly explored. Overall, this review highlights the great potential of metabolomics in discovering prognostic biomarkers for a more accurate patient risk stratification in urological cancers.
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Affiliation(s)
- Filipa Amaro
- Associate Laboratory i4HB, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; UCIBIO-REQUIMTE, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal.
| | - Márcia Carvalho
- Associate Laboratory i4HB, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; UCIBIO-REQUIMTE, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; FP-I3ID, FP-BHS, University Fernando Pessoa, 4200-150 Porto, Portugal; Faculty of Health Sciences, University Fernando Pessoa, 4200-150 Porto, Portugal
| | - Maria de Lourdes Bastos
- Associate Laboratory i4HB, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; UCIBIO-REQUIMTE, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Paula Guedes de Pinho
- Associate Laboratory i4HB, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; UCIBIO-REQUIMTE, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal
| | - Joana Pinto
- Associate Laboratory i4HB, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal; UCIBIO-REQUIMTE, Department of Biological Sciences, Laboratory of Toxicology, Faculty of Pharmacy, University of Porto, 4050-313 Porto, Portugal.
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Health Care Costs Attributable to Prostate Cancer in British Columbia, Canada: A Population-Based Cohort Study. Curr Oncol 2023; 30:3176-3188. [PMID: 36975453 PMCID: PMC10047657 DOI: 10.3390/curroncol30030240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/10/2023] Open
Abstract
We aimed to estimate the total health care costs attributable to prostate cancer (PCa) during care phases by age, cancer stage, tumor grade, and primary treatment in the first year in British Columbia (BC), Canada. Using linked administrative health data, we followed a cohort of men aged ≥ 50 years at diagnosis with PCa between 2010 and 2017 (Cohort 1) from the diagnosis date until the date of death, the last date of observation, or 31 December 2019. Patients who died from PCa after 1 January 2010, were selected for Cohort 2. PCa attributable costs were estimated by comparing costs in patients to matched controls. Cohort 1 (n = 22,672) had a mean age of 69.9 years (SD = 8.9) and a median follow-up time of 5.2 years. Cohort 2 included 6942 patients. Mean PCa attributable costs were the highest during the first year after diagnosis ($14,307.9 [95% CI: $13,970.0, $14,645.8]) and the year before death ($9959.7 [$8738.8, $11,181.0]). Primary treatment with radiation therapy had significantly higher costs each year after diagnosis than a radical prostatectomy or other surgeries in advanced-stage PCa. Androgen deprivation therapy (and/or chemotherapy) had the highest cost for high-grade and early-stage cancer during the three years after diagnosis. No treatment group had the lowest cost. Updated cost estimates could inform economic evaluations and decision-making.
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Kato T, Yokomizo A, Matsumoto R, Tohi Y, Miyakawa J, Mitsuzuka K, Sasaki H, Inokuchi J, Matsumura M, Sakamoto S, Kinoshita H, Fukuhara H, Kamiya N, Kimura R, Nitta M, Okuno H, Akakura K, Kakehi Y, Sugimoto M. Comparison of the medical costs between active surveillance and other treatments for early prostate cancer in Japan using data from the PRIAS-JAPAN study. Int J Urol 2022; 29:1271-1278. [PMID: 35855586 DOI: 10.1111/iju.14977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/19/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVES To compare the medical costs of active surveillance with those of robot-assisted laparoscopic prostatectomy, brachytherapy, intensity-modulated radiation therapy, and hormone therapy for low-risk prostate cancer. METHODS The costs of protocol biopsies performed in the first year of surveillance (between January 2010 and June 2020) and those of brachytherapy and radiation therapy performed between May 2019 and June 2020 at the Kagawa University Hospital were analyzed. Hormone therapy costs were assumed to be the costs of luteinizing hormone-releasing hormone analogs for over 5 years. Active surveillance-eligible patients were defined based on the following: age <74 years, ≤T2, Gleason score ≤6, prostate-specific antigen level ≤10 ng/ml, and 1-2 positive cores. We estimated the total number of active surveillance-eligible patients in Japan based on the Japan Study Group of Prostate Cancer (J-CAP) study and the 2017 cancer statistical data. We then calculated the 5-year treatment costs of active surveillance-eligible patients using the J-CAP and PRIAS-JAPAN study data. RESULTS In 2017, number of active surveillance-eligible patients in Japan was estimated to be 2808. The 5-year total costs of surveillance, prostatectomy, brachytherapy, radiation therapy, and hormone therapy were 1.65, 14.0, 4.61, 4.04, and 5.87 million United States dollar (USD), respectively. If 50% and 100% of the patients in each treatment group had opted for active surveillance as the initial treatment, the total treatment cost would have been reduced by USD 6.89 million (JPY 889 million) and USD 13.8 million (JPY 1.78 billion), respectively. CONCLUSION Expanding active surveillance to eligible patients with prostate cancer helps save medical costs.
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Affiliation(s)
- Takuma Kato
- Department of Urology, Faculty of Medicine, Kagawa University, Kita-gun, Japan
| | - Akira Yokomizo
- Department of Urology, Harasanshin Hospital, Fukuoka, Japan
| | - Ryuji Matsumoto
- Department of Renal and Genito-Urinary Surgery, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yoichiro Tohi
- Department of Urology, Faculty of Medicine, Kagawa University, Kita-gun, Japan
| | - Jimpei Miyakawa
- Department of Urology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Mitsuzuka
- Department of Urology, Tohoku University School of Medicine, Sendai, Japan
| | - Hiroshi Sasaki
- Department of Urology, The Jikei University School of Medicine, Tokyo, Japan
| | - Junichi Inokuchi
- Department of Urology, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Masafumi Matsumura
- Department of Urology, National Hospital Organization Shikoku Cancer Center, Matsuyama, Japan
| | - Shinichi Sakamoto
- Department of Urology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Hidefumi Kinoshita
- Department of Urology and Andrology, Graduate School of Medicine, Kansai Medical University, Hirakata, Japan
| | - Hiroshi Fukuhara
- Department of Urology, Kyorin University School of Medicine, Tokyo, Japan
| | - Naoto Kamiya
- Department of Urology, Toho University Sakura Medical Center, Sakura, Japan
| | - Ryu Kimura
- Department of Urology, University of the Ryukyus, Graduate School of Medicine, Nishihara, Japan
| | - Masahiro Nitta
- Department of Urology, Tokai University School of Medicine, Hiratsuka, Japan
| | - Hiroshi Okuno
- Department of Urology, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Koichiro Akakura
- Department of Urology, Japan Community Health Care Organization, Tokyo Shinjuku Medical Center, Tokyo, Japan
| | - Yoshiyuki Kakehi
- Department of Urology, Faculty of Medicine, Kagawa University, Kita-gun, Japan
| | - Mikio Sugimoto
- Department of Urology, Faculty of Medicine, Kagawa University, Kita-gun, Japan
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Bozkurt S, Magnani CJ, Seneviratne MG, Brooks JD, Hernandez-Boussard T. Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage. Front Digit Health 2022; 4:793316. [PMID: 35721793 PMCID: PMC9201076 DOI: 10.3389/fdgth.2022.793316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 05/12/2022] [Indexed: 11/30/2022] Open
Abstract
Background Explicit documentation of stage is an endorsed quality metric by the National Quality Forum. Clinical and pathological cancer staging is inconsistently recorded within clinical narratives but can be derived from text in the Electronic Health Record (EHR). To address this need, we developed a Natural Language Processing (NLP) solution for extraction of clinical and pathological TNM stages from the clinical notes in prostate cancer patients. Methods Data for patients diagnosed with prostate cancer between 2010 and 2018 were collected from a tertiary care academic healthcare system's EHR records in the United States. This system is linked to the California Cancer Registry, and contains data on diagnosis, histology, cancer stage, treatment and outcomes. A randomly selected sample of patients were manually annotated for stage to establish the ground truth for training and validating the NLP methods. For each patient, a vector representation of clinical text (written in English) was used to train a machine learning model alongside a rule-based model and compared with the ground truth. Results A total of 5,461 prostate cancer patients were identified in the clinical data warehouse and over 30% were missing stage information. Thirty-three to thirty-six percent of patients were missing a clinical stage and the models accurately imputed the stage in 21-32% of cases. Twenty-one percent had a missing pathological stage and using NLP 71% of missing T stages and 56% of missing N stages were imputed. For both clinical and pathological T and N stages, the rule-based NLP approach out-performed the ML approach with a minimum F1 score of 0.71 and 0.40, respectively. For clinical M stage the ML approach out-performed the rule-based model with a minimum F1 score of 0.79 and 0.88, respectively. Conclusions We developed an NLP pipeline to successfully extract clinical and pathological staging information from clinical narratives. Our results can serve as a proof of concept for using NLP to augment clinical and pathological stage reporting in cancer registries and EHRs to enhance the secondary use of these data.
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Affiliation(s)
- Selen Bozkurt
- Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, United States
| | | | - Martin G. Seneviratne
- Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, United States
| | - James D. Brooks
- School of Medicine, Stanford University, Stanford, CA, United States
| | - Tina Hernandez-Boussard
- Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, United States
- Department of Biomedical Data Sciences, Stanford University, Stanford, CA, United States
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