Browsing by Author "Paredes, Inti"

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    Automatic detection of distant metastasis mentions in radiology reports in spanish
    (American Society of Clinical Oncology, 2024) Ahumada, Ricardo; Dunstan Escudero, Jocelyn Mariel; Rojas, Matías; Peñafiel, Sergio; Paredes, Inti; Báez, Pablo
    A critical task in oncology is extracting information related to cancer metastasis from electronic health records. Metastasis-related information is crucial for planning treatment, evaluating patient prognoses, and cancer research. However, the unstructured way in which findings of distant metastasis are often written in radiology reports makes it difficult to extract information automatically. The main aim of this study was to extract distant metastasis findings from free-text imaging and nuclear medicine reports to classify the patient status according to the presence or absence of distant metastasis. MATERIALS AND METHODS: We created a distant metastasis annotated corpus using positron emission tomography-computed tomography and computed tomography reports of patients with prostate, colorectal, and breast cancers. Entities were labeled M1 or M0 according to affirmative or negative metastasis descriptions. We used a named entity recognition model on the basis of a bidirectional long short-term memory model and conditional random fields to identify entities. Mentions were subsequently used to classify whole reports into M1 or M0. RESULTS: The model detected distant metastasis mentions with a weighted average F1 score performance of 0.84. Whole reports were classified with an F1 score of 0.92 for M0 documents and 0.90 for M1 documents. CONCLUSION: These results show the usefulness of the model in detecting distant metastasis findings in three different types of cancer and the consequent classification of reports. The relevance of this study is to generate structured distant metastasis information from free-text imaging reports in Spanish. In addition, the manually annotated corpus, annotation guidelines, and code are freely released to the research community.
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    Developing and Validating an Automatic Support System for Tumor Coding in Pathology Reports in Spanish
    (2025) Villena, Fabián; Báez, Pablo; Peñafiel, Sergio; Rojas, Matías; Paredes, Inti; Dunstan Escudero, Jocelyn Mariel
    Pathology 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.
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    Response to Kempf et al on Methodological and Practical Aspects of a Distant Metastasis Detection Model
    (American Society of Clinical Oncology, 2024) Ahumada, Ricardo; Dunstan Escudero, Jocelyn Mariel; Paredes, Inti; Baez, Pablo