Evidence map›Paper›PMID 29094145›Full record

ArticleJAMA dermatology2018

Population-Based Analysis of Histologically Confirmed Melanocytic Proliferations Using Natural Language Processing.

Jason P Lott, Denise M Boudreau, Ray L Barnhill, Martin A Weinstock, Eleanor Knopp, Michael W Piepkorn, David E Elder, Steven R Knezevich, Andrew Baer, Anna N A Tosteson and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in JAMA dermatology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
6.0field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 71 citations in OpenAlex.

  1. Natural language processing in dermatology: A systematic literature review and state of the art.Journal of the European Academy of Dermatology and Venereology : JEADV · 2024
    Pooled it
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  6. Review
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  13. Automated analysis of whole slide digital skin biopsy images.Frontiers in artificial intelligence · 2022
    Article
  14. Article
  15. Scale-Aware Transformers for Diagnosing Melanocytic Lesions.IEEE access : practical innovations, open solutions · 2021
    Article
  16. Article
  17. Article
  18. Application of natural language processing algorithms for extracting information from news articles in event-based surveillance.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2020
    Article
  19. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 9 institutions in 2 countries.

Jason P LottCornell Scott-Hill Health Center, New Haven, Connecticut.
Denise M BoudreauKaiser Permanente Washington Health Research Institute, Seattle.
Ray L BarnhillDepartment of Pathology, Institut Curie, and Faculty of Medicine, University of Paris Descartes, Paris, France.
Martin A WeinstockDepartments of Dermatology and Epidemiology, Brown University, Providence, Rhode Island.
Eleanor KnoppDivision of Dermatology, Department of Medicine, University of Washington, Seattle.
Michael W PiepkornDivision of Dermatology, Department of Medicine, University of Washington, Seattle.
David E ElderDepartment of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia.
Steven R KnezevichPathology Associates, Clovis, California.
Andrew BaerKaiser Permanente Washington Health Research Institute, Seattle.
Anna N A TostesonThe Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Joann G ElmoreDepartment of Medicine, University of Washington School of Medicine, Seattle.
University of Washington · USClovis Oncology (United States) · USCornell University · USDartmouth College · USGroup Health Cooperative · USHospital of the University of Pennsylvania · USInstitut Curie · FRKaiser Permanente Washington Health Research Institute · USRhode Island Hospital · US

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Steven D Leach · 1985 to 2026
$91.3M
NCI NIH HHS P30 CA023108
6 · The paper itself

Abstract

Importance: Population-based information on the distribution of histologic diagnoses associated with skin biopsies is unknown. Electronic medical records (EMRs) enable automated extraction of pathology report data to improve our epidemiologic understanding of skin biopsy outcomes, specifically those of melanocytic origin. Objective: To determine population-based frequencies and distribution of histologically confirmed melanocytic lesions. Design, Setting, and Participants: A natural language processing (NLP)-based analysis of EMR pathology reports of adult patients who underwent skin biopsies at a large integrated health care delivery system in the US Pacific Northwest from January 1, 2007, through December 31, 2012. Exposures: Skin biopsy procedure. Main Outcomes and Measures: The primary outcome was histopathologic diagnosis, obtained using an NLP-based system to process EMR pathology reports. We determined the percentage of diagnoses classified as melanocytic vs nonmelanocytic lesions. Diagnoses classified as melanocytic were further subclassified using the Melanocytic Pathology Assessment Tool and Hierarchy for Diagnosis (MPATH-Dx) reporting schema into the following categories: class I (nevi and other benign proliferations such as mildly dysplastic lesions typically requiring no further treatment), class II (moderately dysplastic and other low-risk lesions that may merit narrow reexcision with <5-mm margins), class III (eg, melanoma in situ and other higher-risk lesions warranting reexcision with 5-mm to 1-cm margins), and class IV/V (invasive melanoma requiring wide reexcision with ≥1-cm margins and potential adjunctive therapy). Health system cancer registry data were used to define the percentage of invasive melanoma cases within MPATH-Dx class IV (stage T1a) vs V (≥stage T1b). Results: A total of 80 368 skin biopsies, performed on 47 529 patients, were examined. Nearly 1 in 4 skin biopsies were of melanocytic lesions (23%; n = 18 715), which were distributed according to MPATH-Dx categories as follows: class I, 83.1% (n = 15 558); class II, 8.3% (n = 1548); class III, 4.5% (n = 842); class IV, 2.2% (n = 405); and class V, 1.9% (n = 362). Conclusions and Relevance: Approximately one-quarter of skin biopsies resulted in diagnoses of melanocytic proliferations. These data provide the first population-based estimates across the spectrum of melanocytic lesions ranging from benign through dysplastic to malignant. These results may serve as a foundation for future research seeking to understand the epidemiology of melanocytic proliferations and optimization of skin biopsy utilization.

Indexed as

Natural Language ProcessingAdultAgedBiopsy, NeedleCell ProliferationCross-Sectional StudiesCutaneous Malignant MelanomaElectronic Health RecordsFemaleHumansImmunohistochemistryMaleMelanocytesMelanomaMiddle AgedSEER Program

Identifiers

PMID29094145
PMCPMC5833584
OpenAlexW2765773998

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.