SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials.

Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.

Methods: We collected clinical trial abstracts from key conferences and PubMed (2012-2023).

Inferring gender from first names: Comparing the accuracy of Genderize, Gender API, and the gender R package on authors of diverse nationality.

Meta-researchers commonly leverage tools that infer gender from first names, especially when studying gender disparities. However, tools vary in their accuracy, ease of use, and cost. The objective of this study was to compare the accuracy and cost of the commercial software Genderize and Gender API, and the open-source gender R package.

Collaborative Large Language Models for Automated Data Extraction in Living Systematic Reviews.

Objective: Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world two-reviewer process.

Materials And Methods: A dataset of 10 clinical trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data.

Evolving patterns in systemic treatment utilization and survival among older patients with advanced cutaneous melanoma.

Introduction: In the last decade, melanoma treatment has improved significantly. However, data on population-level treatment utilization and survival trends among older patients is limited. This study aimed to analyze trends in systemic anticancer therapy (Rx), including the uptake of immune checkpoint inhibitors (ICIs), in conjunction with trends in cause-specific survival among older patients (66+) diagnosed with advanced melanoma (2008-2019).

BUILDING A DOSE TOXO-EQUIVALENCE MODEL FROM A BAYESIAN META-ANALYSIS OF PUBLISHED CLINICAL TRIALS.

In clinical practice medications are often interchanged in treatment protocols when a patient negatively reacts to their first line of therapy. Although switching between medications is common, clinicians often lack structured guidance when choosing the initial dose and frequency of a new medication, given the former with respect to risk of adverse events. In this paper we propose to establish this dose toxo-equivalence relationship using published clinical trial results with one or both drugs of interest via a Bayesian meta-analysis model that accounts for both within- and between-study variances.

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research.

Oral antivirals for COVID-19 among patients with cancer.

Purpose: Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid® (nirmatrelvir/ritonavir) and Lagevrio® (molnupiravir). There is a paucity of data regarding the benefit from these antivirals among immunocompromised patients with cancer, and recent studies have questioned their efficacy among vaccinated patients, even those with risk factors for severe COVID-19.

Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.

Purpose: The RECIST guidelines provide a standardized approach for evaluating the response of cancer to treatment, allowing for consistent comparison of treatment efficacy across different therapies and patients. However, collecting such information from electronic health records manually can be extremely labor-intensive and time-consuming because of the complexity and volume of clinical notes. The aim of this study is to apply natural language processing (NLP) techniques to automate this process, minimizing manual data collection efforts, and improving the consistency and reliability of the results.

SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials.

Background: Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.

Methods: We collected clinical trial abstracts from key conferences and PubMed (2012-2023).

Travel-Time Disparities in Access to Proton Beam Therapy for Cancer Treatment.

Importance: Proton beam therapy is an emerging radiotherapy treatment for patients with cancer that may produce similar outcomes as traditional photon-based therapy for many cancers while delivering lower amounts of toxic radiation to surrounding tissue. Geographic proximity to a proton facility is a critical component of ensuring equitable access both for indicated diagnoses and ongoing clinical trials.

Objective: To characterize the distribution of proton facilities in the US, quantify drive-time access for the population, and investigate the likelihood of long commutes for certain population subgroups.

Brief Report: Impact of Anti-Cancer Treatments on Outcomes of COVID-19 in Patients With Thoracic Cancers: A CCC19 Registry Analysis.

Introduction:: Patients with thoracic cancers have one of the highest mortality rates among patients with cancer and COVID-19. Data evaluating the impact of recent anti-cancer therapies on COVID-19 outcomes in patients with thoracic cancers are confined to heterogenous studies with limited follow-up data. We leveraged data from the COVID-19 and Cancer Consortium (CCC19) (NCT04354701) to analyze the impact of recent anti-cancer therapies on the clinical outcomes of COVID-19 in patients with thoracic cancers.

The evolving posology and administration of immune checkpoint inhibitors: subcutaneous formulations.

Immune checkpoint inhibitors (ICIs) have transformed cancer care. Recently, atezolizumab gained its first global approval in a subcutaneous (SC) formulation by the UK medicines regulator, being notable as the first time an FDA- and/or European Medicines Agency (EMA)-approved ICI has been licensed via this administration route. Here, we discuss this approval, other SC ICIs in development, and the benefits and challenges of this administration route, including potential implications for patient care.

Oral antivirals for COVID-19 among patients with cancer.

Purpose: Immunocompromised individuals, such as those diagnosed with cancer, are at a significantly higher risk for severe illness and mortality when infected with SARS-CoV-2 (COVID-19) than the general population. Two oral antiviral treatments are approved for COVID-19: Paxlovid (nirmatrelvir/ritonavir) and Lagevrio (molnupiravir). There is a paucity of data regarding the benefit from these antivirals among immunocompromised patients with cancer, and recent studies have questioned their efficacy among vaccinated patients, even those with risk factors for severe COVID-19.

Genomic Characterization and Clinical Outcomes of Patients with Peritoneal Metastases from the AACR GENIE Biopharma Collaborative Colorectal Cancer Registry.

Unlabelled: Peritoneal metastases (PM) are common in metastatic colorectal cancer (mCRC). We aimed to characterize patients with mCRC and PM from a clinical and molecular perspective using the American Association of Cancer Research Genomics Evidence Neoplasia Information Exchange (GENIE) Biopharma Collaborative (BPC) registry. Patients' tumor samples underwent targeted next-generation sequencing.

Graphical Presentations in Systemic Anticancer Treatment Network Meta-Analyses: A Systematic Review.

Network meta-analysis (NMA) draws conclusions about indirect comparisons of randomized clinical trials and is considered high-level evidence. Most NMA publications make use of network plots to portray results. Network plots are complex graphics that can have many visual attributes to portray useful information, such as node size, color, and graph layout.

Introducing a Comprehensive Score of Systemic Anticancer Treatment Relevance.

Treatment patterns in systemic anticancer therapy (SACT) are extremely varied and complex. While professional society guidelines exist that suggest recommended treatment strategies, these guidelines are produced through an extremely laborious and sometimes opaque manual process, making it impossible for such guidelines to cover all relevant treatment scenarios. To complement these manually curated guidelines, we leveraged a database of 5818 clinical trials and 7012 supporting references from 1943-present to calculate a quantifiable "relevance score".

Frontal and anterior temporal hypometabolism post chemoradiation in head and neck cancer: A real-world PET study.

Background And Purpose: Adverse neurological effects after cancer therapy are common, but biomarkers to diagnose, monitor, or risk stratify patients are still not validated or used clinically. An accessible imaging method, such as fluorodeoxyglucose positron emission tomography (FDG PET) of the brain, could meet this gap and serve as a biomarker for functional brain changes. We utilized FDG PET to evaluate which brain regions are most susceptible to altered glucose metabolism after chemoradiation in patients with head and neck cancer (HNCa).

Cancer Informatics 2023: Data Sharing and Federating Learning Point Towards New Collaborative Opportunities.

Objective: To summarize significant research contributions on cancer informatics published in 2022.

Methods: An extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2022 that address topics in cancer informatics. The selection process comprised three steps: (i) ten candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook.

DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.

Purpose: Manual extraction of case details from patient records for cancer surveillance is a resource-intensive task. Natural Language Processing (NLP) techniques have been proposed for automating the identification of key details in clinical notes. Our goal was to develop NLP application programming interfaces (APIs) for integration into cancer registry data abstraction tools in a computer-assisted abstraction setting.

Burden of AML, 1990-2019: Estimates From the Global Burden of Disease Study.

Purpose: AML accounts for 80% of acute leukemia in adults. While progress has been made in treating younger patients in the past 2 decades, there has been limited improvement for older patients until recently. This study examines the global and European Union (EU) 15+ trends in AML between 1990 and 2019.