A lung cancer diagnosis and treatment dataset with geno- and phenotypical characteristics of the patient.

This dataset comprises information about 1242 lung cancer patients collected by the Medical Oncology Department of the Puerta de Hierro University Hospital of Majadahonda in Madrid, Spain. It includes information about cancer diagnosis and treatment, as well as personal and medical data recorded during anamneses. The dataset could assist in data analysis with the aim of discovering relationships between the applied treatment(s), the evolution of the disease and the associated adverse effects.

Family history of cancer and lung cancer: Utility of big data and artificial intelligence for exploring the role of genetic risk.

Objectives: Lung Cancer (LC) is a multifactorial disease for which the role of genetic susceptibility has become increasingly relevant. Our aim was to use artificial intelligence (AI) to analyze differences between patients with LC based on family history of cancer (FHC).

Materials And Methods: From August 2016 to June 2020 clinical information was obtained from Thoracic Tumors Registry (TTR), a nationwide database sponsored by the Spanish Lung Cancer Group.

Boosting predictive models and augmenting patient data with relevant genomic and pathway information.

The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and diverse patient responses to treatments. Personalized care and patient outcomes heavily rely on early relapse identification, yet current predictive models, despite their potential, lack comprehensive genetic data. This inadequacy fuels our research focus-integrating specific genetic information, such as pathway scores, into clinical data.

Examining explainable clinical decision support systems with think aloud protocols.

Machine learning tools are increasingly used to improve the quality of care and the soundness of a treatment plan. Explainable AI (XAI) helps users in understanding the inner mechanisms of opaque machine learning models and is a driver of trust and adoption. Explanation methods for black-box models exist, but there is a lack of user studies on the interpretability of the provided explanations.

Transformers for extracting breast cancer information from Spanish clinical narratives.

The wide adoption of electronic health records (EHRs) offers immense potential as a source of support for clinical research. However, previous studies focused on extracting only a limited set of medical concepts to support information extraction in the cancer domain for the Spanish language. Building on the success of deep learning for processing natural language texts, this paper proposes a transformer-based approach to extract named entities from breast cancer clinical notes written in Spanish and compares several language models.

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer.

Objective: Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency.

Integration of Clinical Information and Imputed Aneuploidy Scores to Enhance Relapse Prediction in Early Stage Lung Cancer Patients.

Early-stage lung cancer is crucial clinically due to its insidious nature and rapid progression. Most of the prediction models designed to predict tumour recurrence in the early stage of lung cancer rely on the clinical or medical history of the patient. However, their performance could likely be improved if the input patient data contained genomic information.

Clinical factors influencing long-term survival in a real-life cohort of early stage non-small-cell lung cancer patients in Spain.

Background: Current prognosis in oncology is reduced to the tumour stage and performance status, leaving out many other factors that may impact the patient´s management. Prognostic stratification of early stage non-small-cell lung cancer (NSCLC) patients with poor prognosis after surgery is of considerable clinical relevance. The objective of this study was to identify clinical factors associated with long-term overall survival in a real-life cohort of patients with stage I-II NSCLC and develop a prognostic model that identifies features associated with poor prognosis and stratifies patients by risk.

Cost-effectiveness of diagnostic tests during follow-up in lung cancer patients: an evidence-based study.

Background: Many patients with non-metastatic non-small cell lung cancer (NSCLC) are cured by surgery but part of them develop recurrence. Strategies are needed to identify these relapses. Currently, there is no consensus on the follow-up schedule after curative resection for patients with NSCLC.

Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach.

Detecting negation and uncertainty is crucial for medical text mining applications; otherwise, extracted information can be incorrectly identified as real or factual events. Although several approaches have been proposed to detect negation and uncertainty in clinical texts, most efforts have focused on the English language. Most proposals developed for Spanish have focused mainly on negation detection and do not deal with uncertainty.

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.

Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients.

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock.

Purpose Of Review: Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, inflammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms' disruption may be linked to cancer. The integration of circadian biology into cancer research may offer new options for increasing cancer treatment effectiveness and would encompass the prevention, diagnosis, and treatment of this disease.

To burn-out or not to burn-out: a cross-sectional study in healthcare professionals in Spain during COVID-19 pandemic.

Objective: To assess the prevalence of burn-out syndrome in healthcare workers working on the front line (FL) in Spain during COVID-19.

Design: Cross-sectional, online survey-based study.

Settings: Sampling was performed between 21st April and 3rd May 2020.

Reconstructing the patient's natural history from electronic health records.

The automatic extraction of a patient's natural history from Electronic Health Records (EHRs) is a critical step towards building intelligent systems that can reason about clinical variables and support decision making. Although EHRs contain a large amount of valuable information about the patient's medical care, this information can only be fully understood when analyzed in a temporal context. Any intelligent system should then be able to extract medical concepts, date expressions, temporal relations and the temporal ordering of medical events from the free texts of EHRs; yet, this task is hard to tackle, due to the domain specific nature of EHRs, writing quality and lack of structure of these texts, and more generally the presence of redundant information.

Transformed follicular lymphoma in the rituximab era: A report from the Spanish Lymphoma Oncology Group.

Follicular lymphoma (FL) is the second most common non-Hodgkin lymphoma (NHL) subtype. The histological transformation (HT) of FL is an event considered frequent in the natural history of this tumor. We studied the transformation rates, predictive factors, and treatment characteristics that may impact in the survival of patients with FL and HT.

Peritoneal washing is an adequate source for somatic BRCA1/2 mutation testing in ovarian malignancies.

Genetic screening for BRCA mutations should be offered to all women diagnosed with epithelial ovarian, fallopian tube, and/or peritoneal cancers given the implications for treatment options and cancer risk assessments. Yet, while germline breast cancer susceptibility gene 1 (BRCA1) and breast cancer susceptibility gene 2 (BRCA2) testing is commonly performed, BRCA1/2 somatic mutations testing is rather challenging since the poor quality of DNA extracted from formalin fixed paraffin embedded (FFPE) samples can significantly impair this process. Peritoneal lavage is routinely performed in surgeries of suspected ovarian malignancies.

Long-term follow up of Hodgkin lymphoma.

Background: Hodgkin lymphoma (HL) is the paradigm of curable disease. This study analyzed the overall survival (OS) of patients with HL and compared their survival between decades and with the expected survival of a general population.

Results: The median follow-up was 22 years.