Developing and Validating an Automatic Support System for Tumor Coding in Pathology Reports in Spanish

Simple item page
dc.article.numbere2400124
dc.catalogadorvzp
dc.contributor.authorVillena, Fabián
dc.contributor.authorBáez, Pablo
dc.contributor.authorPeñafiel, Sergio
dc.contributor.authorRojas, Matías
dc.contributor.authorParedes, Inti
dc.contributor.authorDunstan Escudero, Jocelyn Mariel
dc.date.accessioned2025-03-17T19:29:00Z
dc.date.available2025-03-17T19:29:00Z
dc.date.issued2025
dc.description.abstractPathology reports provide valuable information for cancer registries to understand, plan, and implement strategies to mitigate the impact of cancer. However, coding essential information from unstructured reports is performed by experts in a time-consuming manual process. We developed and validated a novel two-step automatic coding system that first recognizes tumor morphology and topography mentions from free text and then suggests codes from the International Classification of Diseases for Oncology (ICD-O) in Spanish.MATERIALS AND METHODSWe created an annotated corpus of tumor morphology and topography mentions consisting of 1,101 documents. We combined it with the CANTEMIST corpus (Cancer Text Mining Shared Task). Specifically, we implemented a named entity recognition (NER) model using the bidirectional long short-term memory network-conditional random field architecture enhanced with a stacked embedding layer. We applied transfer learning from state-of-the-art pretrained language models to obtain high-quality contextual representations, thus improving the detection of entities. The mentions found using this model were subsequently coded using a search engine tailored to the ICD-O codes.RESULTSOur NER models achieved an F1 score of 0.86 and 0.90 for tumor morphology and topography, respectively. The overall performance of our automatic coding system achieved an accuracy at five suggestions of 0.72 and 0.65 for tumor morphology and topography, respectively.CONCLUSIONThese results demonstrate the feasibility of implementing natural language processing tools in the routine of a cancer center to extract and code valuable information from pathology reports. Our recommender system allows reliable and transparent coding at the moment of consultation. This publication shares the annotated corpus in Spanish, annotation guidelines, and source code to reproduce our experiments.
dc.description.funderANID/FONDECYT; Folios de beca: FB210005, 1241825 ICN17_002, AFB240002, 3210395 y 21220200
dc.fechaingreso.objetodigital2025-03-17
dc.format.extent9 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1200/CCI.24.00124
dc.identifier.issn2473-4276
dc.identifier.urihttps://doi.org/10.1200/CCI.24.00124
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102691
dc.information.autorucEscuela de Ingeniería; Dunstan Escudero, Jocelyn Mariel; S/I; 1285723
dc.language.isoen
dc.nota.accesocontenido completo
dc.revistaJCO CLINICAL CANCER INFORMATICS
dc.rightsacceso abierto
dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.subject.ods03 Good health and well-being
dc.subject.odspa03 Salud y bienestar
dc.titleDeveloping and Validating an Automatic Support System for Tumor Coding in Pathology Reports in Spanish
dc.typeartículo
dc.volumen9
sipa.codpersvinculados1285723
sipa.trazabilidadORCID;2025-03-03
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
villena-et-al-2025-developing-and-validating-an-automatic-support-system-for-tumor-coding-in-pathology-reports-in.pdf
Size:
511.7 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas