Accurate pattern-based extraction of complex Gleason score expressions from pathology reports

Miettinen, Joonas; Tanskanen, Tomas; Degerlund, Henna; Nevala, Aapeli; Malila, Nea; Pitkäniemi, Janne

doi:10.1016/j.jbi.2021.103850

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…While the concept of a kidney biopsy registry is not new, with many such registries described in the literature, 13 , 15 , 26 - 28 we adopted a novel method for data extraction using a natural language processing algorithm, demonstrating the feasibility of the creation of such a registry without an exhaustive chart review of potentially thousands of patients. Although natural language processing software has been used for data extraction from pathology reports in oncology, 29 - 31 to our knowledge this is the first application of such software for data extraction from kidney biopsy pathology reports. Our methods could serve as a model to other centers looking to create a similar registry with high-quality data, without an in-depth manual review of potentially thousands of biopsy reports.…”

Section: Discussionmentioning

confidence: 99%

The Development of a Comprehensive Clinicopathologic Registry for Glomerular Diseases Using Natural Language Processing

Barr

Harasemiw

Gibson

et al. 2023

Can J Kidney Health Dis

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Background: Glomerulonephritis (GN) represents a common cause of chronic kidney disease, and treatment to slow or prevent progression of GN is associated with significant morbidity. Large patient registries have improved the understanding of risk stratification, treatment selection, and definitions of treatment response in GN, but can be resource-intensive, with incomplete patient capture. Objective: To describe the creation of a comprehensive clinicopathologic registry for all patients undergoing kidney biopsy in Manitoba, using natural language processing software for data extraction from pathology reports, as well as to describe cohort characteristics and outcomes. Design: Retrospective population-based cohort study. Setting: Tertiary care center in the province of Manitoba. Patients: All patients undergoing a kidney biopsy in the province of Manitoba from 2002 to 2019. Measurements: Descriptive statistics are presented for the most common glomerular diseases, along with outcomes of kidney failure and mortality for the individual diseases. Methods: Data from native kidney biopsy reports from January 2002 to December 2019 were extracted into a structured database using a natural language processing algorithm employing regular expressions. The pathology database was then linked with population-level clinical, laboratory, and medication data, creating a comprehensive clinicopathologic registry. Kaplan-Meier curves and Cox models were constructed to assess the relationship between type of GN and outcomes of kidney failure and mortality. Results: Of 2421 available biopsies, 2103 individuals were linked to administrative data, of which 1292 had a common glomerular disease. The incidence of yearly biopsies increased almost 3-fold over the study period. Among common glomerular diseases, immunoglobulin A (IgA) nephropathy was the most common (28.6%), whereas infection-related GN had the highest proportions of kidney failure (70.3%) and all-cause mortality (42.3%). Predictors of kidney failure included urine albumin-to-creatinine ratio at the time of biopsy (adjusted hazard ratio [HR] = 1.43, 95% confidence interval [CI] = 1.24-1.65), whereas predictors of mortality included age at the time of biopsy (adjusted HR = 1.05, 95% CI = 1.04-1.06) and infection-related GN (adjusted HR = 1.85, 95% CI = 1.14-2.99, compared with the reference category of IgA nephropathy). Limitations: Retrospective, single-center study with a relatively small number of biopsies. Conclusions: Creation of a comprehensive glomerular diseases registry is feasible and can be facilitated through the use of novel data extraction methods. This registry will facilitate further epidemiological research in GN.

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Section: Discussionmentioning

confidence: 99%

The Development of a Comprehensive Clinicopathologic Registry for Glomerular Diseases Using Natural Language Processing

Barr

Harasemiw

Gibson

et al. 2023

Can J Kidney Health Dis

View full text Add to dashboard Cite

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“…Numerous recent studies have utilized information derived from pathology reports, both in general 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 and using subsets of the TCGA pathology report dataset. 14 , 15 , 16 , 17 , 18 , 19 , 20 Previous studies incorporating TCGA pathology report data have largely relied on manual curation and limited term-set extraction across smaller subsets of the dataset.…”

Section: Introductionmentioning

confidence: 99%

TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models

Kefeli,

Tatonetti

2024

Patterns

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Benchmark Pathology Report Text Corpus with Cancer Type Classification

Kefeli¹,

Tatonetti

2023

Preprint

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In cancer research, pathology report text is a largely un-tapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly-available datasets for benchmarking report-based models. Two recent advances suggest the urgent need for a benchmark dataset. First, improved optical character recognition (OCR) techniques will make it possible to access older pathology reports in an automated way, increasing data available for analysis. Second, recent improvements in natural language processing (NLP) techniques using AI allow more accurate prediction of clinical targets from text. We apply state-of-the-art OCR and customized post-processing to publicly available report PDFs from The Cancer Genome Atlas, generating a machine-readable corpus of 9,523 reports. We perform a proof-of-principle cancer-type classification across 32 tissues, achieving 0.992 average AU-ROC. This dataset will be useful to researchers across specialties, including research clinicians, clinical trial investigators, and clinical NLP researchers.

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Accurate pattern-based extraction of complex Gleason score expressions from pathology reports

Cited by 3 publications

References 5 publications

The Development of a Comprehensive Clinicopathologic Registry for Glomerular Diseases Using Natural Language Processing

The Development of a Comprehensive Clinicopathologic Registry for Glomerular Diseases Using Natural Language Processing

TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models

Benchmark Pathology Report Text Corpus with Cancer Type Classification

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