Publications by authors named "J Boekhoff"

Purpose: This study investigated the concordance of five different publicly available Large Language Models (LLM) with the recommendations of a multidisciplinary tumor board regarding treatment recommendations for complex breast cancer patient profiles.

Methods: Five LLM, including three versions of ChatGPT (version 4 and 3.5, with data access until September 3021 and January 2022), Llama2, and Bard were prompted to produce treatment recommendations for 20 complex breast cancer patient profiles.

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Precision oncology treatments are being applied more commonly in breast and gynecological oncology through the implementation of Molecular Tumor Boards (MTBs), but real-world clinical outcome data remain limited. A retrospective analysis was conducted in patients with breast cancer (BC) and gynecological malignancies referred to our center's MTB from 2018 to 2023. The analysis covered patient characteristics, next-generation sequencing (NGS) results, MTB recommendations, therapy received, and clinical outcomes.

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Article Synopsis
  • - Homologous recombination deficiency (HRD) assays are crucial for personalized treatment in ovarian cancer but often fail due to unclear tissue requirements, leading to poor diagnostics for many patients.
  • - In a study involving 2,702 tumor samples, 90.3% were successfully tested using a specific HRD assay, revealing that 41.1% were HRD positive and identifying key factors affecting testing success, such as tumor cell content and area.
  • - The study recommends selecting high-grade serous ovarian cancer samples with at least 30% tumor cell content and a tumor area of 0.5 cm or greater to improve testing success rates, which could potentially reach up to 98%.
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With the recent diffusion of access to publicly available large language models (LLMs), common interest in generative artificial-intelligence-based applications for medical purposes has skyrocketed. The increased use of these models by tech-savvy patients for personal health issues calls for a scientific evaluation of whether LLMs provide a satisfactory level of accuracy for treatment decisions. This observational study compares the concordance of treatment recommendations from the popular LLM ChatGPT 3.

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In breast cancer, the current guideline for pathological workup includes recommendations for advanced molecular analysis of certain predictive molecular markers in addition to basic immunohistochemical diagnostics. These markers are determined depending on tumor stage, including sequencing techniques and immunohistochemical methods. This comprises the systematic investigation of molecular alterations such as PIK3CA or BRCA1,2 mutations, NTRK fusions, or microsatellite instability as a basis for targeted therapy.

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