ArticleJournal of pathology informatics2022
Automatic Classification of Cancer Pathology Reports: A Systematic Review.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- A Secure, Scalable Large Language Model-Based System (CIDER) for High-Throughput Clinical Data Extraction From Medical Reports: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- SCRIPT: Stratified clinical risk prediction from pathology reports using large language models.Journal of pathology informatics · 2026Article
- Current status and future direction of cancer research using artificial intelligence for clinical application.Cancer science · 2025Review
- A survey of NLP methods for oncology in the past decade with a focus on cancer registry applications.Artificial intelligence review · 2025Article
- Classifying Tumor Reportability Status From Unstructured Electronic Pathology Reports Using Language Models in a Population-Based Cancer Registry Setting.JCO clinical cancer informatics · 2024Article
- Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial.JMIR research protocols · 2024Article
- A Multi-Institutional Natural Language Processing Pipeline to Extract Performance Status From Electronic Health Records.Cancer control : journal of the Moffitt Cancer CenterArticle
- Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LABDigital healthArticle
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathology reports primarily consist of unstructured free text and thus the clinical information contained in the reports is not trivial to access or query. Multiple natural language processing (NLP) techniques have been proposed to automate the coding of pathology reports via text classification. In this systematic review, we follow the guidelines proposed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; Page et al., 2020:
Identifiers
What OpenQuestion holds
Registered trials
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.