Automated extraction of information from free text of Spanish oncology pathology reports.

Background: Pathology reports are stored as unstructured, ungrammatical, fragmented, and abbreviated free text with linguistic variability among pathologists. For this reason, tumor information extraction requires a significant human effort. Recording data in an efficient and high-quality format is essential in implementing and establishing a hospital-based-cancer registry. Objective: This study aimed to describe implementing a natural language processing algorithm for oncology pathology reports. Methods: An algorithm was developed to process oncology pathology reports in Spanish to extract 20 medical descriptors. The approach is based on the successive coincidence of regular expressions. Results: The validation was performed with 140 pathological reports. The topography identification was performed manually by humans and the algorithm in all reports. The human identified morphology in 138 reports and by the algorithm in 137. The average fuzzy matching score was 68.3 for Topography and 89.5 for Morphology. Conclusions: A preliminary algorithm validation against human extraction was performed over a small set of reports with satisfactory results. This shows that a regular-expression approach can accurately and precisely extract multiple specimen attributes from free-text Spanish pathology reports. Additionally, we developed a website to facilitate collaborative validation at a larger scale which may be helpful for future research on the subject.

Introducción: Los reportes de patología están almacenados como texto libre sin estructura, gramática, fragmentados o abreviados, con variabilidad lingüística entre patólogos. Por esta razón, la extracción de información de tumores requiere un esfuerzo humano significativo. Almacenar información en un formato eficiente y de alta calidad es esencial para implementar y establecer un registro hospitalario de cáncer. Objetivo: Este estudio busca describir la implementación de un algoritmo de Procesamiento de Lenguaje Natural para reportes de patología oncológica. Métodos: Desarrollamos un algoritmo para procesar reportes de patología oncológica en Español, con el objetivo de extraer 20 descriptores médicos. El abordaje se basa en la coincidencia sucesiva de expresiones regulares. Resultados: La validación se hizo con 140 reportes de patología. La identificación topográfica se realizó por humanos y por el algoritmo en todos los reportes. La morfología fue identificada por humanos en 138 reportes y por el algoritmo en 137. El valor de coincidencias parciales (fuzzy matches) promedio fue de 68.3 para Topografía y 89.5 para Morfología. Conclusiones: Se hizo una validación preliminar del algoritmo contra extracción humana sobre un pequeño grupo de reportes, con resultados satisfactorios. Esto muestra que múltiples atributos del espécimen pueden ser extraídos de manera precisa de texto libre de reportes de patología en Español, usando un abordaje de expresiones regulares. Adicionalmente, desarrollamos una página web para facilitar la validación colaborativa a gran escala, lo que puede ser beneficioso para futuras investigaciones en el tema.

Cost-Effectiveness of Pembrolizumab as an Adjuvant Treatment in Colombia for Melanoma Patients with Lymph Node Involvement After Complete Resection.

INTRODUCTION: The KEYNOTE-054 trial found that adjuvant treatment with pembrolizumab improved recurrence-free survival versus placebo in completely resected high-risk stage III melanoma patients. We assessed the cost-effectiveness of adjuvant pembrolizumab in Colombia compared with watchful waiting, a widely used strategy despite the high risk of recurrence with surgery alone. METHODS: A four-health state [recurrence-free (RF), locoregional recurrence (LR), distant metastases (DM), and death) Markov model was developed to assess the lifetime medical costs and outcomes (3% annual discount), along with cost-effectiveness ratios (ICERs). The transitions from the RF and LR states were modeled using KEYNOTE-054 data, and those from the DM state were modeled using data from the KEYNOTE-006 trial and a network meta-analysis of advanced treatments received after adjuvant pembrolizumab and watchful waiting. The health state utilities were derived from KEYNOTE-054 Euro-QoL data and literature. Costs are expressed in 2021 Colombian pesos (COP). RESULTS: Over a 46-year time horizon, patients on adjuvant pembrolizumab and watchful waiting were estimated to gain 9.69 and 7.56 quality-adjusted life-years (QALYs), 10.83 and 8.65 life-years (LYs), and incur costs of COP 663,595,726 and COP 563,237,206, respectively. The proportion of LYs spent in RF state was 84.63% for pembrolizumab and 72.13% for watchful waiting, yielding lower subsequent treatment, disease management, and terminal care costs for pembrolizumab. Adjuvant pembrolizumab improved survival by 2.18 LYs and 2.13 QALYs versus watchful waiting. The ICER per QALY was COP 47,081,917, primarily driven by recurrence rates and advanced melanoma treatments. The deterministic sensitivity analysis results were robust and consistent across various reasonable inputs and alternative scenarios. At a willingness-to-pay threshold of COP 69,150,201 per QALY, the probability of pembrolizumab being cost-effective was 65.70%. CONCLUSION: Pembrolizumab is cost-effective as an adjuvant treatment compared to watchful waiting among patients with high-risk stage III melanoma after complete resection in Colombia.

Desafíos en el manejo del sarcoma de Ewing en una paciente testigo de Jehová / Challenges in the care of Ewing's sarcoma in a Jehovah's Witness patient

El sarcoma de Ewing es una neoplasia de hueso y tejidos blandos, cuyo manejo se relaciona con toxicidad hematológica. Este aspecto representa un desafío médico y ético en los pacientes testigos de Jehová quienes, por sus creencias religiosas, rechazan la aplicación de hemoderivados, con riesgo de que se descontinúe la quimioterapia o de que se utilicen dosis subóptimas. Se presenta el caso de una mujer colombiana de 34 años, testigo de Jehová, con diagnóstico de sarcoma de Ewing con estadificación clínica IIB (T1N0M0) en las regiones maxilar y mandibular izquierdas, tratada con quimioterapia, quien presentó un valor mínimo de hemoglobina de hasta 4,5 g/dl y tuvo indicación quirúrgica como parte del tratamiento. En estos pacientes, la decisión de practicar una transfusión comprende implicaciones éticas que requieren alternativas terapéuticas y un abordaje multidisciplinario.

Ewing's sarcoma is a bone and soft tissue neoplasm, whose management is related to hematological toxicity. This aspect represents a medical and ethical challenge in Jehovah's Witnesses patients, who, due to their religious beliefs, reject the blood component transfusion, with the risk of discontinuing chemotherapy or using suboptimal doses. We present the case of a 34-year-old Colombian woman, Jehovah's Witness, diagnosed with Ewing's sarcoma with clinical stage IIB (T1N0M0) in the left maxillary and mandibular regions, treated with chemotherapy, who presented a hemoglobin nadir of up to 4.5 g/dL, and surgical indication as part of the treatment. In these patients, the transfusion decision has ethical implications that require therapeutic alternatives and a multidisciplinary approach.

Automated extraction of information from free text of Spanish oncology pathology reports

Background: Pathology reports are stored as unstructured, ungrammatical, fragmented, and abbreviated free text with linguistic variability among pathologists. For this reason, tumor information extraction requires a significant human effort. Recording data in an efficient and high-quality format is essential in implementing and establishing a hospital-based-cancer registry Objective: This study aimed to describe implementing a natural language processing algorithm for oncology pathology reports. Methods: An algorithm was developed to process oncology pathology reports in Spanish to extract 20 medical descriptors. The approach is based on the successive coincidence of regular expressions. Results: The validation was performed with 140 pathological reports. The topography identification was performed manually by humans and the algorithm in all reports. The human identified morphology in 138 reports and by the algorithm in 137. The average fuzzy matching score was 68.3 for Topography and 89.5 for Morphology. Conclusions: A preliminary algorithm validation against human extraction was performed over a small set of reports with satisfactory results. This shows that a regular-expression approach can accurately and precisely extract multiple specimen attributes from free-text Spanish pathology reports. Additionally, we developed a website to facilitate collaborative validation at a larger scale which may be helpful for future research on the subject.

Introducción: Los reportes de patología están almacenados como texto libre sin estructura, gramática, fragmentados o abreviados, con variabilidad lingüística entre patólogos. Por esta razón, la extracción de información de tumores requiere un esfuerzo humano significativo. Almacenar información en un formato eficiente y de alta calidad es esencial para implementar y establecer un registro hospitalario de cáncer. Objetivo: Este estudio busca describir la implementación de un algoritmo de Procesamiento de Lenguaje Natural para reportes de patología oncológica. Métodos: Desarrollamos un algoritmo para procesar reportes de patología oncológica en Español, con el objetivo de extraer 20 descriptores médicos. El abordaje se basa en la coincidencia sucesiva de expresiones regulares. Resultados: La validación se hizo con 140 reportes de patología. La identificación topográfica se realizó por humanos y por el algoritmo en todos los reportes. La morfología fue identificada por humanos en 138 reportes y por el algoritmo en 137. El valor de coincidencias parciales (fuzzy matches) promedio fue de 68.3 para Topografía y 89.5 para Morfología. Conclusiones: Se hizo una validación preliminar del algoritmo contra extracción humana sobre un pequeño grupo de reportes, con resultados satisfactorios. Esto muestra que múltiples atributos del espécimen pueden ser extraídos de manera precisa de texto libre de reportes de patología en Español, usando un abordaje de expresiones regulares. Adicionalmente, desarrollamos una página web para facilitar la validación colaborativa a gran escala, lo que puede ser beneficioso para futuras investigaciones en el tema.

Indigenous communities in Colombia: A cultural and holistic view of cancer management.

Cancer is one of the most burdening global health challenges. Indigenous communities are at high risk for worse healthcare outcomes because of inequalities in the incidence, prevalence, and mortality of oncological diseases, that arise from socioeconomic, racial, cultural, religious beliefs, and ethnic factors. Their perception about themselves is closely related to what affects their territory, making them possess a profound rooted feeling with their surroundings, and intense spiritual believes. Consequently, the disease process is linked to physical and emotional imbalances and alterations in their territory. Researchers from the United States, Canada, New Zealand, and Australia have worked diligently to learn about barriers to cancer management among these populations. Unfortunately, robust cancer data is lacking for most of the world's Indigenous, leading to obstacles in information systems and consequently, inequities in healthcare with the perpetuation of the problem. Therefore, a better understanding of cancer as a global health problem is required. Our study aims to propose a holistic and culturally adapted framework to improve cancer health services and outcomes among Indigenous peoples in Colombia.